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Merge branch 'develop'

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This commit is contained in:
Tiago Ribeiro
2024-12-23 16:39:03 +00:00
parent 111108556b 22593b737b
commit 7d04b144c4
211 changed files with 56922 additions and 45062 deletions
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2
.dockerignore
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@@ -5,4 +5,6 @@ README.md
*.pyd
__pycache__
.pytest_cache
postman
/scripts
/.venv

5
.gitignore vendored
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@@ -2,5 +2,6 @@ __pycache__
.idea
.env
.DS_Store
/firebase-configs/test_firebase.json
/scripts
.venv
_scripts
*.env

17
.idea/ielts-be.iml generated
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@@ -1,17 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/.venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.11 (ielts-be)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PackageRequirementsSettings">
<option name="versionSpecifier" value="Don't specify version" />
</component>
<component name="PyDocumentationSettings">
<option name="format" value="GOOGLE" />
<option name="myDocStringFormat" value="Google" />
</component>
</module>

6
.idea/inspectionProfiles/profiles_settings.xml generated
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@@ -1,6 +0,0 @@
<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

10
.idea/misc.xml generated
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@@ -1,10 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="Python 3.11 (ielts-be)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (ielts-be)" project-jdk-type="Python SDK" />
<component name="PyCharmProfessionalAdvertiser">
<option name="shown" value="true" />
</component>
</project>

8
.idea/modules.xml generated
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@@ -1,8 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/ielts-be.iml" filepath="$PROJECT_DIR$/.idea/ielts-be.iml" />
</modules>
</component>
</project>

6
.idea/vcs.xml generated
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@@ -1,6 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
</component>
</project>

19
Dockerfile
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@@ -1,6 +1,10 @@
FROM python:3.11-slim as requirements-stage
WORKDIR /tmp
RUN pip install poetry
COPY pyproject.toml ./poetry.lock* /tmp/
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes
# Use the official lightweight Python image.
# https://hub.docker.com/_/python
FROM python:3.11-slim
# Allow statements and log messages to immediately appear in the logs
@@ -9,8 +13,11 @@ ENV PYTHONUNBUFFERED True
# Copy local code to the container image.
ENV APP_HOME /app
WORKDIR $APP_HOME
COPY . ./
COPY --from=requirements-stage /tmp/requirements.txt /app/requirements.txt
RUN apt update && apt install -y \
ffmpeg \
poppler-utils \
@@ -23,20 +30,18 @@ RUN apt update && apt install -y \
curl \
&& rm -rf /var/lib/apt/lists/*
RUN curl -sL https://deb.nodesource.com/setup_20.x | bash - \
&& apt-get install -y nodejs
RUN npm install -g firebase-tools
# Install production dependencies.
RUN pip install --no-cache-dir -r requirements.txt
RUN pip install --no-cache-dir -r /app/requirements.txt
EXPOSE 5000
EXPOSE 8000
# Run the web service on container startup. Here we use the gunicorn
# webserver, with one worker process and 8 threads.
# For environments with multiple CPU cores, increase the number of workers
# to be equal to the cores available.
# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
CMD exec gunicorn --bind 0.0.0.0:5000 --workers 1 --threads 8 --timeout 0 app:app
ENTRYPOINT ["gunicorn", "--bind", "0.0.0.0:8000", "--workers", "1", "--threads", "8", "--timeout", "0", "-k", "uvicorn.workers.UvicornWorker", "ielts_be:app"]

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README.md Normal file
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@@ -0,0 +1,26 @@
# Run the app
1. pip install poetry
2. poetry install
3. python app.py
# Modules
- api -> endpoints
- configs -> app configs and constants
- controllers -> meant for handling exceptions, transforming data or orchestrate complex use cases with several services, for now mostly just calls services directly
- dtos -> pydantic models used for receiving data and for validation
- exceptions -> if custom exceptions are needed to throw in services so they can be handled in the controllers to construct some specific http response
- helpers -> a bunch of lightweight functions grouped by some kind of logic
- mappers -> to map complex data
- middlewares -> classes that are run before executing the endpoint code
- repositories -> interfaces with data stores
- services -> all the business logic goes here
- utils -> loose functions used on one-off occasions
# Dependency injection
If you want to add new controllers/services/repositories you will have to change
app/configs/dependency_injection.py
Also make sure you have @inject on your endpoint when calling these.

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app.py
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1
audio-samples/placeholder.txt
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@@ -1 +0,0 @@
THIS FILE ONLY EXISTS TO KEEP THIS FOLDER IN THE REPO

20
docker-compose.yml
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@@ -1,10 +1,10 @@
version: "3"
services:
ielts-be:
container_name: ielts-be
build: .
image: ecrop/ielts-be:latest
ports:
- 8080:5000
restart: unless-stopped
version: "3"
services:
ielts-be:
container_name: ielts-be
build: .
image: ecrop/ielts-be:latest
ports:
- 8080:8000
restart: unless-stopped

1
download-audio/placeholder.txt
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@@ -1 +0,0 @@
THIS FILE ONLY EXISTS TO KEEP THIS FOLDER IN THE REPO

1
download-video/placeholder.txt
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@@ -1 +0,0 @@
THIS FILE ONLY EXISTS TO KEEP THIS FOLDER IN THE REPO

62
elai/AvatarEnum.py Normal file
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@@ -0,0 +1,62 @@
from enum import Enum
class AvatarEnum(Enum):
# Works
GIA_BUSINESS = {
"avatar_code": "gia.business",
"avatar_gender": "female",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/gia/business/gia_business.png",
"avatar_canvas": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/gia/business/gia_business.png",
"voice_id": "EXAVITQu4vr4xnSDxMaL",
"voice_provider": "elevenlabs"
}
# Works
VADIM_BUSINESS = {
"avatar_code": "vadim.business",
"avatar_gender": "male",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/vadim/business/vadim_business.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/vadim/business/vadim_business.png",
"voice_id": "flq6f7yk4E4fJM5XTYuZ",
"voice_provider": "elevenlabs"
}
ORHAN_BUSINESS = {
"avatar_code": "orhan.business",
"avatar_gender": "male",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/orhan/business/orhan.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/orhan/business/orhan.png",
"voice_id": "en-US-AndrewMultilingualNeural",
"voice_provider": "azure"
}
FLORA_BUSINESS = {
"avatar_code": "flora.business",
"avatar_gender": "female",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/flora/business/flora_business.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/flora/business/flora_business.png",
"voice_id": "en-US-JaneNeural",
"voice_provider": "azure"
}
SCARLETT_BUSINESS = {
"avatar_code": "scarlett.business",
"avatar_gender": "female",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/scarlett/business/scarlett_business.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/scarlett/business/scarlett_business.png",
"voice_id": "en-US-NancyNeural",
"voice_provider": "azure"
}
PARKER_CASUAL = {
"avatar_code": "parker.casual",
"avatar_gender": "male",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/parker/casual/parker_casual.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/parker/casual/parker_casual.png",
"voice_id": "en-US-TonyNeural",
"voice_provider": "azure"
}
ETHAN_BUSINESS = {
"avatar_code": "ethan.business",
"avatar_gender": "male",
"avatar_url": "https://elai-avatars.s3.us-east-2.amazonaws.com/common/ethan/business/ethan_business_low.png",
"avatar_canvas": "https://d3u63mhbhkevz8.cloudfront.net/common/ethan/business/ethan_business_low.png",
"voice_id": "en-US-JasonNeural",
"voice_provider": "azure"
}

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elai/avatars.json Normal file
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elai/english_voices.json Normal file
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0
heygen/filter_json.py → elai/filter_json.py
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elai/voices.json Normal file
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24
firebase-configs/encoach-staging.json
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@@ -1,13 +1,13 @@
{
"type": "service_account",
"project_id": "encoach-staging",
"private_key_id": "5718a649419776df9637589f8696a258a6a70f6c",
"private_key": "-----BEGIN PRIVATE KEY-----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\n9kwpOmZpjNmC/ERD69imWhY=\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-8rs9e@encoach-staging.iam.gserviceaccount.com",
"client_id": "108221424237414412378",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-8rs9e%40encoach-staging.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
{
"type": "service_account",
"project_id": "encoach-staging",
"private_key_id": "5718a649419776df9637589f8696a258a6a70f6c",
"private_key": "-----BEGIN PRIVATE KEY-----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\n9kwpOmZpjNmC/ERD69imWhY=\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-8rs9e@encoach-staging.iam.gserviceaccount.com",
"client_id": "108221424237414412378",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-8rs9e%40encoach-staging.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
}

26
firebase-configs/mti-ielts-626a2dcf6091.json
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@@ -1,13 +1,13 @@
{
"type": "service_account",
"project_id": "mti-ielts",
"private_key_id": "626a2dcf60916a1b5011f388495b8f9c4fc065ef",
"private_key": "-----BEGIN PRIVATE KEY-----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\nTQqp88pfTbOnkdJBVi0AWs5B\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-dyg6p@mti-ielts.iam.gserviceaccount.com",
"client_id": "104980563453519094431",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-dyg6p%40mti-ielts.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
}
{
"type": "service_account",
"project_id": "mti-ielts",
"private_key_id": "626a2dcf60916a1b5011f388495b8f9c4fc065ef",
"private_key": "-----BEGIN PRIVATE KEY-----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\nTQqp88pfTbOnkdJBVi0AWs5B\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-dyg6p@mti-ielts.iam.gserviceaccount.com",
"client_id": "104980563453519094431",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-dyg6p%40mti-ielts.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
}

24
firebase-configs/storied-phalanx-349916.json
View File

@@ -1,13 +1,13 @@
{
"type": "service_account",
"project_id": "storied-phalanx-349916",
"private_key_id": "c9e05f6fe413b1031a71f981160075ff4b044444",
"private_key": "-----BEGIN PRIVATE KEY-----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\n8sDu//ohb7nCzceEI0DnWs13\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-3ml0u@storied-phalanx-349916.iam.gserviceaccount.com",
"client_id": "114163760341944984396",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-3ml0u%40storied-phalanx-349916.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
{
"type": "service_account",
"project_id": "storied-phalanx-349916",
"private_key_id": "c9e05f6fe413b1031a71f981160075ff4b044444",
"private_key": "-----BEGIN PRIVATE KEY-----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\n8sDu//ohb7nCzceEI0DnWs13\n-----END PRIVATE KEY-----\n",
"client_email": "firebase-adminsdk-3ml0u@storied-phalanx-349916.iam.gserviceaccount.com",
"client_id": "114163760341944984396",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-3ml0u%40storied-phalanx-349916.iam.gserviceaccount.com",
"universe_domain": "googleapis.com"
}

441
helper/api_messages.py
View File

@@ -1,441 +0,0 @@
from enum import Enum
from typing import List
class QuestionType(Enum):
LISTENING_SECTION_1 = "Listening Section 1"
LISTENING_SECTION_2 = "Listening Section 2"
LISTENING_SECTION_3 = "Listening Section 3"
LISTENING_SECTION_4 = "Listening Section 4"
WRITING_TASK_1 = "Writing Task 1"
WRITING_TASK_2 = "Writing Task 2"
SPEAKING_1 = "Speaking Task Part 1"
SPEAKING_2 = "Speaking Task Part 2"
READING_PASSAGE_1 = "Reading Passage 1"
READING_PASSAGE_2 = "Reading Passage 2"
READING_PASSAGE_3 = "Reading Passage 3"
def get_grading_messages(question_type: QuestionType, question: str, answer: str, context: str = None):
if QuestionType.WRITING_TASK_1 == question_type:
messages = [
{
"role": "user",
"content": "You are a IELTS examiner.",
},
{
"role": "user",
"content": f"The question you have to grade is of type Writing Task 1 and is the following: {question}",
}
]
if not (context is None or context == ""):
messages.append({
"role": "user",
"content": f"To grade the previous question, bear in mind the following context: {context}",
})
messages.extend([
{
"role": "user",
"content": "It is mandatory for you to provide your response with the overall grade and breakdown grades, "
"with just the following json format: {'comment': 'comment about answer quality', 'overall': 7.0, "
"'task_response': {'Task Achievement': 8.0, 'Coherence and Cohesion': 6.5, 'Lexical Resource': 7.5, "
"'Grammatical Range and Accuracy': 6.0}}",
},
{
"role": "user",
"content": "Example output: { 'comment': 'Overall, the response is good but there are some areas that need "
"improvement.\n\nIn terms of Task Achievement, the writer has addressed all parts of the question "
"and has provided a clear opinion on the topic. However, some of the points made are not fully "
"developed or supported with examples.\n\nIn terms of Coherence and Cohesion, there is a clear "
"structure to the response with an introduction, body paragraphs and conclusion. However, there "
"are some issues with cohesion as some sentences do not flow smoothly from one to another.\n\nIn "
"terms of Lexical Resource, there is a good range of vocabulary used throughout the response and "
"some less common words have been used effectively.\n\nIn terms of Grammatical Range and Accuracy, "
"there are some errors in grammar and sentence structure which affect clarity in places.\n\nOverall, "
"this response would score a band 6.5.', 'overall': 6.5, 'task_response': "
"{ 'Coherence and Cohesion': 6.5, 'Grammatical Range and Accuracy': 6.0, 'Lexical Resource': 7.0, "
"'Task Achievement': 7.0}}",
},
{
"role": "user",
"content": f"Evaluate this answer according to ielts grading system: {answer}",
},
])
return messages
elif QuestionType.WRITING_TASK_2 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS examiner.",
},
{
"role": "user",
"content": f"The question you have to grade is of type Writing Task 2 and is the following: {question}",
},
{
"role": "user",
"content": "It is mandatory for you to provide your response with the overall grade and breakdown grades, "
"with just the following json format: {'comment': 'comment about answer quality', 'overall': 7.0, "
"'task_response': {'Task Achievement': 8.0, 'Coherence and Cohesion': 6.5, 'Lexical Resource': 7.5, "
"'Grammatical Range and Accuracy': 6.0}}",
},
{
"role": "user",
"content": "Example output: { 'comment': 'Overall, the response is good but there are some areas that need "
"improvement.\n\nIn terms of Task Achievement, the writer has addressed all parts of the question "
"and has provided a clear opinion on the topic. However, some of the points made are not fully "
"developed or supported with examples.\n\nIn terms of Coherence and Cohesion, there is a clear "
"structure to the response with an introduction, body paragraphs and conclusion. However, there "
"are some issues with cohesion as some sentences do not flow smoothly from one to another.\n\nIn "
"terms of Lexical Resource, there is a good range of vocabulary used throughout the response and "
"some less common words have been used effectively.\n\nIn terms of Grammatical Range and Accuracy, "
"there are some errors in grammar and sentence structure which affect clarity in places.\n\nOverall, "
"this response would score a band 6.5.', 'overall': 6.5, 'task_response': "
"{ 'Coherence and Cohesion': 6.5, 'Grammatical Range and Accuracy': 6.0, 'Lexical Resource': 7.0, "
"'Task Achievement': 7.0}}",
},
{
"role": "user",
"content": f"Evaluate this answer according to ielts grading system: {answer}",
},
]
elif QuestionType.SPEAKING_1 == question_type:
return [
{
"role": "user",
"content": "You are an IELTS examiner."
},
{
"role": "user",
"content": f"The question you need to grade is a Speaking Task Part 1 question, and it is as follows: {question}"
},
{
"role": "user",
"content": "Please provide your assessment using the following JSON format: {'comment': 'Comment about answer "
"quality will go here', 'overall': 7.0, 'task_response': {'Fluency and "
"Coherence': 8.0, 'Lexical Resource': 6.5, 'Grammatical Range and Accuracy': 7.5, 'Pronunciation': 6.0}}"
},
{
"role": "user",
"content": "Example output: {'comment': 'Comment about answer quality will go here', 'overall': 6.5, "
"'task_response': {'Fluency and Coherence': 7.0, "
"'Lexical Resource': 6.5, 'Grammatical Range and Accuracy': 7.0, 'Pronunciation': 6.0}}"
},
{
"role": "user",
"content": "Please assign a grade of 0 if the answer provided does not address the question."
},
{
"role": "user",
"content": f"Assess this answer according to the IELTS grading system: {answer}"
},
{
"role": "user",
"content": "Remember to consider Fluency and Coherence, Lexical Resource, Grammatical Range and Accuracy, "
"and Pronunciation when grading the response."
}
]
elif QuestionType.SPEAKING_2 == question_type:
return [
{
"role": "user",
"content": "You are an IELTS examiner."
},
{
"role": "user",
"content": f"The question you need to grade is a Speaking Task Part 2 question, and it is as follows: {question}"
},
{
"role": "user",
"content": "Please provide your assessment using the following JSON format: {\"comment\": \"Comment about "
"answer quality\", \"overall\": 7.0, \"task_response\": {\"Fluency and Coherence\": 8.0, \"Lexical "
"Resource\": 6.5, \"Grammatical Range and Accuracy\": 7.5, \"Pronunciation\": 6.0}}"
},
{
"role": "user",
"content": "Example output: {\"comment\": \"The candidate has provided a clear response to the question "
"and has given examples of how they spend their weekends. However, there are some issues with "
"grammar and pronunciation that affect the overall score. In terms of fluency and coherence, "
"the candidate speaks clearly and smoothly with only minor hesitations. They have also provided "
"a well-organized response that is easy to follow. Regarding lexical resource, the candidate "
"has used a range of vocabulary related to weekend activities but there are some errors in "
"word choice that affect the meaning of their sentences. In terms of grammatical range and "
"accuracy, the candidate has used a mix of simple and complex sentence structures but there "
"are some errors in subject-verb agreement and preposition use. Finally, regarding pronunciation, "
"the candidate's speech is generally clear but there are some issues with stress and intonation "
"that make it difficult to understand at times.\", \"overall\": 6.5, \"task_response\": {\"Fluency "
"and Coherence\": 7.0, \"Lexical Resource\": 6.5, \"Grammatical Range and Accuracy\": 7.0, "
"\"Pronunciation\": 6.0}}"
},
{
"role": "user",
"content": "Please assign a grade of 0 if the answer provided does not address the question."
},
{
"role": "user",
"content": f"Assess this answer according to the IELTS grading system: {answer}"
},
{
"role": "user",
"content": "Remember to consider Fluency and Coherence, Lexical Resource, Grammatical Range and Accuracy, "
"and Pronunciation when grading the response."
}
]
else:
raise Exception("Question type not implemented: " + question_type.value)
def get_speaking_grading_messages(answers: List):
messages = [
{
"role": "user",
"content": "You are an IELTS examiner."
},
{
"role": "user",
"content": "The exercise you need to grade is a Speaking Task, and it is has the following questions and answers:"
}
]
for item in answers:
question = item["question"]
answer = item["answer_text"]
messages.append({
"role": "user",
"content": f"Question: {question}; Answer: {answer}"
})
messages.extend([
{
"role": "user",
"content": f"Assess this answer according to the IELTS grading system."
},
{
"role": "user",
"content": "Please provide your assessment using the following JSON format: {'comment': 'Comment about answer "
"quality will go here', 'overall': 7.0, 'task_response': {'Fluency and "
"Coherence': 8.0, 'Lexical Resource': 6.5, 'Grammatical Range and Accuracy': 7.5, 'Pronunciation': 6.0}}"
},
{
"role": "user",
"content": "Example output: {'comment': 'Comment about answer quality will go here', 'overall': 6.5, "
"'task_response': {'Fluency and Coherence': 7.0, "
"'Lexical Resource': 6.5, 'Grammatical Range and Accuracy': 7.0, 'Pronunciation': 6.0}}"
},
{
"role": "user",
"content": "Please assign a grade of 0 if the answer provided does not address the question."
},
{
"role": "user",
"content": "Remember to consider Fluency and Coherence, Lexical Resource, Grammatical Range and Accuracy, "
"and Pronunciation when grading the response."
}
])
return messages
def get_question_gen_messages(question_type: QuestionType):
if QuestionType.LISTENING_SECTION_1 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "Provide me with a transcript similar to the ones in ielts exam Listening Section 1. "
"Create an engaging transcript simulating a conversation related to a unique type of service "
"that requires getting the customer's details. Make sure to include specific details "
"and descriptions to bring"
"the scenario to life. After the transcript, please "
"generate a 'form like' fill in the blanks exercise with 6 form fields (ex: name, date of birth)"
" to fill related to the customer's details. Finally, "
"provide the answers for the exercise. The response must be a json following this format: "
"{ 'type': '<type of registration (ex: hotel, gym, english course, etc)>', "
"'transcript': '<transcript of just the conversation about a registration of some sort, "
"identify the person talking in each speech line>', "
"'exercise': { 'form field': { '1': '<form field 1>', '2': '<form field 2>', "
"'3': '<form field 3>', '4': '<form field 4>', "
"'5': '<form field 5>', '6': '<form field 5>' }, "
"'answers': {'1': '<answer to fill blank space in form field 1>', '2': '<answer to fill blank "
"space in form field 2>', '3': '<answer to fill blank space in form field 3>', "
"'4': '<answer to fill blank space in form field 4>', '5': '<answer to fill blank space in form field 5>',"
" '6': '<answer to fill blank space in form field 6>'}}}",
}
]
elif QuestionType.LISTENING_SECTION_2 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "Provide me with a transcript similar to the ones in ielts exam Listening section 2. After the transcript, please "
"generate a fill in the blanks exercise with 6 statements related to the text content. Finally, "
"provide the answers for the exercise. The response must be a json following this format: "
"{ 'transcript': 'transcript about some subject', 'exercise': { 'statements': { '1': 'statement 1 "
"with a blank space to fill', '2': 'statement 2 with a blank space to fill', '3': 'statement 3 with a "
"blank space to fill', '4': 'statement 4 with a blank space to fill', '5': 'statement 5 with a blank "
"space to fill', '6': 'statement 6 with a blank space to fill' }, "
"'answers': {'1': 'answer to fill blank space in statement 1', '2': 'answer to fill blank "
"space in statement 2', '3': 'answer to fill blank space in statement 3', "
"'4': 'answer to fill blank space in statement 4', '5': 'answer to fill blank space in statement 5',"
" '6': 'answer to fill blank space in statement 6'}}}",
}
]
elif QuestionType.LISTENING_SECTION_3 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "Provide me with a transcript similar to the ones in ielts exam Listening section 3. After the transcript, please "
"generate 4 multiple choice questions related to the text content. Finally, "
"provide the answers for the exercise. The response must be a json following this format: "
"{ 'transcript': 'generated transcript similar to the ones in ielts exam Listening section 3', "
"'exercise': { 'questions': [ { 'question': "
"'question 1', 'options': ['option 1', 'option 2', 'option 3', 'option 4'], 'answer': 1}, "
"{'question': 'question 2', 'options': ['option 1', 'option 2', 'option 3', 'option 4'], "
"'answer': 3}, {'question': 'question 3', 'options': ['option 1', 'option 2', 'option 3', "
"'option 4'], 'answer': 0}, {'question': 'question 4', 'options': ['option 1', 'option 2', "
"'option 3', 'option 4'], 'answer': 2}]}}",
}
]
elif QuestionType.LISTENING_SECTION_4 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "Provide me with a transcript similar to the ones in ielts exam Listening section 4. After the transcript, please "
"generate 4 completion-type questions related to the text content to complete with 1 word. Finally, "
"provide the answers for the exercise. The response must be a json following this format: "
"{ 'transcript': 'generated transcript similar to the ones in ielts exam Listening section 4', "
"'exercise': [ { 'question': 'question 1', 'answer': 'answer 1'}, "
"{'question': 'question 2', 'answer': 'answer 2'}, {'question': 'question 3', 'answer': 'answer 3'}, "
"{'question': 'question 4', 'answer': 'answer 4'}]}",
}
]
elif QuestionType.WRITING_TASK_2 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "The question you have to generate is of type Writing Task 2.",
},
{
"role": "user",
"content": "It is mandatory for you to provide your response with the question "
"just with the following json format: {'question': 'question'}",
},
{
"role": "user",
"content": "Example output: { 'question': 'We are becoming increasingly dependent on computers. "
"They are used in businesses, hospitals, crime detection and even to fly planes. What things will "
"they be used for in the future? Is this dependence on computers a good thing or should we he more "
"auspicious of their benefits?'}",
},
{
"role": "user",
"content": "Generate a question for IELTS exam Writing Task 2.",
},
]
elif QuestionType.SPEAKING_1 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "The question you have to generate is of type Speaking Task Part 1.",
},
{
"role": "user",
"content": "It is mandatory for you to provide your response with the question "
"just with the following json format: {'question': 'question'}",
},
{
"role": "user",
"content": "Example output: { 'question': 'Let’s talk about your home town or village. "
"What kind of place is it? What’s the most interesting part of your town/village? "
"What kind of jobs do the people in your town/village do? "
"Would you say it’s a good place to live? (Why?)'}",
},
{
"role": "user",
"content": "Generate a question for IELTS exam Speaking Task.",
},
]
elif QuestionType.SPEAKING_2 == question_type:
return [
{
"role": "user",
"content": "You are a IELTS program that generates questions for the exams.",
},
{
"role": "user",
"content": "The question you have to generate is of type Speaking Task Part 2.",
},
{
"role": "user",
"content": "It is mandatory for you to provide your response with the question "
"just with the following json format: {'question': 'question'}",
},
{
"role": "user",
"content": "Example output: { 'question': 'Describe something you own which is very important to you. "
"You should say: where you got it from how long you have had it what you use it for and "
"explain why it is important to you.'}",
},
{
"role": "user",
"content": "Generate a question for IELTS exam Speaking Task.",
},
]
else:
raise Exception("Question type not implemented: " + question_type.value)
def get_question_tips(question: str, answer: str, correct_answer: str, context: str = None):
messages = [
{
"role": "user",
"content": "You are a IELTS exam program that analyzes incorrect answers to questions and gives tips to "
"help students understand why it was a wrong answer and gives helpful insight for the future. "
"The tip should refer to the context and question.",
}
]
if not (context is None or context == ""):
messages.append({
"role": "user",
"content": f"This is the context for the question: {context}",
})
messages.extend([
{
"role": "user",
"content": f"This is the question: {question}",
},
{
"role": "user",
"content": f"This is the answer: {answer}",
},
{
"role": "user",
"content": f"This is the correct answer: {correct_answer}",
}
])
return messages

661
helper/constants.py
View File

@@ -1,661 +0,0 @@
AUDIO_FILES_PATH = 'download-audio/'
FIREBASE_LISTENING_AUDIO_FILES_PATH = 'listening_recordings/'
VIDEO_FILES_PATH = 'download-video/'
FIREBASE_SPEAKING_VIDEO_FILES_PATH = 'speaking_videos/'
GRADING_TEMPERATURE = 0.1
TIPS_TEMPERATURE = 0.2
GEN_QUESTION_TEMPERATURE = 0.7
GPT_3_5_TURBO = "gpt-3.5-turbo"
GPT_4_TURBO = "gpt-4-turbo"
GPT_4_O = "gpt-4o"
GPT_3_5_TURBO_16K = "gpt-3.5-turbo-16k"
GPT_3_5_TURBO_INSTRUCT = "gpt-3.5-turbo-instruct"
GPT_4_PREVIEW = "gpt-4-turbo-preview"
GRADING_FIELDS = ['comment', 'overall', 'task_response']
GEN_FIELDS = ['topic']
GEN_TEXT_FIELDS = ['title']
LISTENING_GEN_FIELDS = ['transcript', 'exercise']
READING_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch']
READING_3_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch', 'ideaMatch']
LISTENING_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm']
LISTENING_1_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksFill',
'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm']
LISTENING_2_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions']
LISTENING_3_EXERCISE_TYPES = ['multipleChoice3Options', 'writeBlanksQuestions']
LISTENING_4_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm']
TOTAL_READING_PASSAGE_1_EXERCISES = 13
TOTAL_READING_PASSAGE_2_EXERCISES = 13
TOTAL_READING_PASSAGE_3_EXERCISES = 14
TOTAL_LISTENING_SECTION_1_EXERCISES = 10
TOTAL_LISTENING_SECTION_2_EXERCISES = 10
TOTAL_LISTENING_SECTION_3_EXERCISES = 10
TOTAL_LISTENING_SECTION_4_EXERCISES = 10
LISTENING_MIN_TIMER_DEFAULT = 30
WRITING_MIN_TIMER_DEFAULT = 60
SPEAKING_MIN_TIMER_DEFAULT = 14
BLACKLISTED_WORDS = ["jesus", "sex", "gay", "lesbian", "homosexual", "god", "angel", "pornography", "beer", "wine",
"cocaine", "alcohol", "nudity", "lgbt", "casino", "gambling", "catholicism",
"discrimination", "politic", "christianity", "islam", "christian", "christians",
"jews", "jew", "discrimination", "discriminatory"]
EN_US_VOICES = [
{'Gender': 'Female', 'Id': 'Salli', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Salli',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Matthew', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Matthew',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Kimberly', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kimberly',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Kendra', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kendra',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Justin', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Justin',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Joey', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Joey',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Joanna', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Joanna',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Ivy', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Ivy',
'SupportedEngines': ['neural', 'standard']}]
EN_GB_VOICES = [
{'Gender': 'Female', 'Id': 'Emma', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Emma',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Brian', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Brian',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Amy', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Amy',
'SupportedEngines': ['neural', 'standard']}]
EN_GB_WLS_VOICES = [
{'Gender': 'Male', 'Id': 'Geraint', 'LanguageCode': 'en-GB-WLS', 'LanguageName': 'Welsh English', 'Name': 'Geraint',
'SupportedEngines': ['standard']}]
EN_AU_VOICES = [{'Gender': 'Male', 'Id': 'Russell', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Russell', 'SupportedEngines': ['standard']},
{'Gender': 'Female', 'Id': 'Nicole', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Nicole', 'SupportedEngines': ['standard']}]
ALL_VOICES = EN_US_VOICES + EN_GB_VOICES + EN_GB_WLS_VOICES + EN_AU_VOICES
NEURAL_EN_US_VOICES = [
{'Gender': 'Female', 'Id': 'Danielle', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Danielle',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Gregory', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Gregory',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Kevin', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kevin',
'SupportedEngines': ['neural']},
{'Gender': 'Female', 'Id': 'Ruth', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Ruth',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Stephen', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Stephen',
'SupportedEngines': ['neural']}]
NEURAL_EN_GB_VOICES = [
{'Gender': 'Male', 'Id': 'Arthur', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Arthur',
'SupportedEngines': ['neural']}]
NEURAL_EN_AU_VOICES = [
{'Gender': 'Female', 'Id': 'Olivia', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Olivia', 'SupportedEngines': ['neural']}]
NEURAL_EN_ZA_VOICES = [
{'Gender': 'Female', 'Id': 'Ayanda', 'LanguageCode': 'en-ZA', 'LanguageName': 'South African English',
'Name': 'Ayanda', 'SupportedEngines': ['neural']}]
NEURAL_EN_NZ_VOICES = [
{'Gender': 'Female', 'Id': 'Aria', 'LanguageCode': 'en-NZ', 'LanguageName': 'New Zealand English', 'Name': 'Aria',
'SupportedEngines': ['neural']}]
NEURAL_EN_IN_VOICES = [
{'Gender': 'Female', 'Id': 'Kajal', 'LanguageCode': 'en-IN', 'LanguageName': 'Indian English', 'Name': 'Kajal',
'SupportedEngines': ['neural']}]
NEURAL_EN_IE_VOICES = [
{'Gender': 'Female', 'Id': 'Niamh', 'LanguageCode': 'en-IE', 'LanguageName': 'Irish English', 'Name': 'Niamh',
'SupportedEngines': ['neural']}]
ALL_NEURAL_VOICES = NEURAL_EN_US_VOICES + NEURAL_EN_GB_VOICES + NEURAL_EN_AU_VOICES + NEURAL_EN_ZA_VOICES + NEURAL_EN_NZ_VOICES + NEURAL_EN_IE_VOICES
MALE_VOICES = [item for item in ALL_VOICES if item.get('Gender') == 'Male']
FEMALE_VOICES = [item for item in ALL_VOICES if item.get('Gender') == 'Female']
MALE_NEURAL_VOICES = [item for item in ALL_NEURAL_VOICES if item.get('Gender') == 'Male']
FEMALE_NEURAL_VOICES = [item for item in ALL_NEURAL_VOICES if item.get('Gender') == 'Female']
difficulties = ["easy", "medium", "hard"]
mti_topics = [
"Education",
"Technology",
"Environment",
"Health and Fitness",
"Engineering",
"Work and Careers",
"Travel and Tourism",
"Culture and Traditions",
"Social Issues",
"Arts and Entertainment",
"Climate Change",
"Social Media",
"Sustainable Development",
"Health Care",
"Immigration",
"Artificial Intelligence",
"Consumerism",
"Online Shopping",
"Energy",
"Oil and Gas",
"Poverty and Inequality",
"Cultural Diversity",
"Democracy and Governance",
"Mental Health",
"Ethics and Morality",
"Population Growth",
"Science and Innovation",
"Poverty Alleviation",
"Cybersecurity and Privacy",
"Human Rights",
"Food and Agriculture",
"Cyberbullying and Online Safety",
"Linguistic Diversity",
"Urbanization",
"Artificial Intelligence in Education",
"Youth Empowerment",
"Disaster Management",
"Mental Health Stigma",
"Internet Censorship",
"Sustainable Fashion",
"Indigenous Rights",
"Water Scarcity",
"Social Entrepreneurship",
"Privacy in the Digital Age",
"Sustainable Transportation",
"Gender Equality",
"Automation and Job Displacement",
"Digital Divide",
"Education Inequality"
]
topics = [
"Art and Creativity",
"History of Ancient Civilizations",
"Environmental Conservation",
"Space Exploration",
"Artificial Intelligence",
"Climate Change",
"The Human Brain",
"Renewable Energy",
"Cultural Diversity",
"Modern Technology Trends",
"Sustainable Agriculture",
"Natural Disasters",
"Cybersecurity",
"Philosophy of Ethics",
"Robotics",
"Health and Wellness",
"Literature and Classics",
"World Geography",
"Social Media Impact",
"Food Sustainability",
"Economics and Markets",
"Human Evolution",
"Political Systems",
"Mental Health Awareness",
"Quantum Physics",
"Biodiversity",
"Education Reform",
"Animal Rights",
"The Industrial Revolution",
"Future of Work",
"Film and Cinema",
"Genetic Engineering",
"Climate Policy",
"Space Travel",
"Renewable Energy Sources",
"Cultural Heritage Preservation",
"Modern Art Movements",
"Sustainable Transportation",
"The History of Medicine",
"Artificial Neural Networks",
"Climate Adaptation",
"Philosophy of Existence",
"Augmented Reality",
"Yoga and Meditation",
"Literary Genres",
"World Oceans",
"Social Networking",
"Sustainable Fashion",
"Prehistoric Era",
"Democracy and Governance",
"Postcolonial Literature",
"Geopolitics",
"Psychology and Behavior",
"Nanotechnology",
"Endangered Species",
"Education Technology",
"Renaissance Art",
"Renewable Energy Policy",
"Modern Architecture",
"Climate Resilience",
"Artificial Life",
"Fitness and Nutrition",
"Classic Literature Adaptations",
"Ethical Dilemmas",
"Internet of Things (IoT)",
"Meditation Practices",
"Literary Symbolism",
"Marine Conservation",
"Sustainable Tourism",
"Ancient Philosophy",
"Cold War Era",
"Behavioral Economics",
"Space Colonization",
"Clean Energy Initiatives",
"Cultural Exchange",
"Modern Sculpture",
"Climate Mitigation",
"Mindfulness",
"Literary Criticism",
"Wildlife Conservation",
"Renewable Energy Innovations",
"History of Mathematics",
"Human-Computer Interaction",
"Global Health",
"Cultural Appropriation",
"Traditional cuisine and culinary arts",
"Local music and dance traditions",
"History of the region and historical landmarks",
"Traditional crafts and artisanal skills",
"Wildlife and conservation efforts",
"Local sports and athletic competitions",
"Fashion trends and clothing styles",
"Education systems and advancements",
"Healthcare services and medical innovations",
"Family values and social dynamics",
"Travel destinations and tourist attractions",
"Environmental sustainability projects",
"Technological developments and innovations",
"Entrepreneurship and business ventures",
"Youth empowerment initiatives",
"Art exhibitions and cultural events",
"Philanthropy and community development projects"
]
two_people_scenarios = [
"Booking a table at a restaurant",
"Making a doctor's appointment",
"Asking for directions to a tourist attraction",
"Inquiring about public transportation options",
"Discussing weekend plans with a friend",
"Ordering food at a café",
"Renting a bicycle for a day",
"Arranging a meeting with a colleague",
"Talking to a real estate agent about renting an apartment",
"Discussing travel plans for an upcoming vacation",
"Checking the availability of a hotel room",
"Talking to a car rental service",
"Asking for recommendations at a library",
"Inquiring about opening hours at a museum",
"Discussing the weather forecast",
"Shopping for groceries",
"Renting a movie from a video store",
"Booking a flight ticket",
"Discussing a school assignment with a classmate",
"Making a reservation for a spa appointment",
"Talking to a customer service representative about a product issue",
"Discussing household chores with a family member",
"Planning a surprise party for a friend",
"Talking to a coworker about a project deadline",
"Inquiring about a gym membership",
"Discussing the menu options at a fast-food restaurant",
"Talking to a neighbor about a community event",
"Asking for help with computer problems",
"Discussing a recent sports game with a sports enthusiast",
"Talking to a pet store employee about buying a pet",
"Asking for information about a local farmer's market",
"Discussing the details of a home renovation project",
"Talking to a coworker about office supplies",
"Making plans for a family picnic",
"Inquiring about admission requirements at a university",
"Discussing the features of a new smartphone with a salesperson",
"Talking to a mechanic about car repairs",
"Making arrangements for a child's birthday party",
"Discussing a new diet plan with a nutritionist",
"Asking for information about a music concert",
"Talking to a hairdresser about getting a haircut",
"Inquiring about a language course at a language school",
"Discussing plans for a weekend camping trip",
"Talking to a bank teller about opening a new account",
"Ordering a drink at a coffee shop",
"Discussing a new book with a book club member",
"Talking to a librarian about library services",
"Asking for advice on finding a job",
"Discussing plans for a garden makeover with a landscaper",
"Talking to a travel agent about a cruise vacation",
"Inquiring about a fitness class at a gym",
"Ordering flowers for a special occasion",
"Discussing a new exercise routine with a personal trainer",
"Talking to a teacher about a child's progress in school",
"Asking for information about a local art exhibition",
"Discussing a home improvement project with a contractor",
"Talking to a babysitter about childcare arrangements",
"Making arrangements for a car service appointment",
"Inquiring about a photography workshop at a studio",
"Discussing plans for a family reunion with a relative",
"Talking to a tech support representative about computer issues",
"Asking for recommendations on pet grooming services",
"Discussing weekend plans with a significant other",
"Talking to a counselor about personal issues",
"Inquiring about a music lesson with a music teacher",
"Ordering a pizza for delivery",
"Making a reservation for a taxi",
"Discussing a new recipe with a chef",
"Talking to a fitness trainer about weight loss goals",
"Inquiring about a dance class at a dance studio",
"Ordering a meal at a food truck",
"Discussing plans for a weekend getaway with a partner",
"Talking to a florist about wedding flower arrangements",
"Asking for advice on home decorating",
"Discussing plans for a charity fundraiser event",
"Talking to a pet sitter about taking care of pets",
"Making arrangements for a spa day with a friend",
"Asking for recommendations on home improvement stores",
"Discussing weekend plans with a travel enthusiast",
"Talking to a car mechanic about car maintenance",
"Inquiring about a cooking class at a culinary school",
"Ordering a sandwich at a deli",
"Discussing plans for a family holiday party",
"Talking to a personal assistant about organizing tasks",
"Asking for information about a local theater production",
"Discussing a new DIY project with a home improvement expert",
"Talking to a wine expert about wine pairing",
"Making arrangements for a pet adoption",
"Asking for advice on planning a wedding"
]
social_monologue_contexts = [
"A guided tour of a historical museum",
"An introduction to a new city for tourists",
"An orientation session for new university students",
"A safety briefing for airline passengers",
"An explanation of the process of recycling",
"A lecture on the benefits of a healthy diet",
"A talk on the importance of time management",
"A monologue about wildlife conservation",
"An overview of local public transportation options",
"A presentation on the history of cinema",
"An introduction to the art of photography",
"A discussion about the effects of climate change",
"An overview of different types of cuisine",
"A lecture on the principles of financial planning",
"A monologue about sustainable energy sources",
"An explanation of the process of online shopping",
"A guided tour of a botanical garden",
"An introduction to a local wildlife sanctuary",
"A safety briefing for hikers in a national park",
"A talk on the benefits of physical exercise",
"A lecture on the principles of effective communication",
"A monologue about the impact of social media",
"An overview of the history of a famous landmark",
"An introduction to the world of fashion design",
"A discussion about the challenges of global poverty",
"An explanation of the process of organic farming",
"A presentation on the history of space exploration",
"An overview of traditional music from different cultures",
"A lecture on the principles of effective leadership",
"A monologue about the influence of technology",
"A guided tour of a famous archaeological site",
"An introduction to a local wildlife rehabilitation center",
"A safety briefing for visitors to a science museum",
"A talk on the benefits of learning a new language",
"A lecture on the principles of architectural design",
"A monologue about the impact of renewable energy",
"An explanation of the process of online banking",
"A presentation on the history of a famous art movement",
"An overview of traditional clothing from various regions",
"A lecture on the principles of sustainable agriculture",
"A discussion about the challenges of urban development",
"A monologue about the influence of social norms",
"A guided tour of a historical battlefield",
"An introduction to a local animal shelter",
"A safety briefing for participants in a charity run",
"A talk on the benefits of community involvement",
"A lecture on the principles of sustainable tourism",
"A monologue about the impact of alternative medicine",
"An explanation of the process of wildlife tracking",
"A presentation on the history of a famous inventor",
"An overview of traditional dance forms from different cultures",
"A lecture on the principles of ethical business practices",
"A discussion about the challenges of healthcare access",
"A monologue about the influence of cultural traditions",
"A guided tour of a famous lighthouse",
"An introduction to a local astronomy observatory",
"A safety briefing for participants in a team-building event",
"A talk on the benefits of volunteering",
"A lecture on the principles of wildlife protection",
"A monologue about the impact of space exploration",
"An explanation of the process of wildlife photography",
"A presentation on the history of a famous musician",
"An overview of traditional art forms from different cultures",
"A lecture on the principles of effective education",
"A discussion about the challenges of sustainable development",
"A monologue about the influence of cultural diversity",
"A guided tour of a famous national park",
"An introduction to a local marine conservation project",
"A safety briefing for participants in a hot air balloon ride",
"A talk on the benefits of cultural exchange programs",
"A lecture on the principles of wildlife conservation",
"A monologue about the impact of technological advancements",
"An explanation of the process of wildlife rehabilitation",
"A presentation on the history of a famous explorer",
"A lecture on the principles of effective marketing",
"A discussion about the challenges of environmental sustainability",
"A monologue about the influence of social entrepreneurship",
"A guided tour of a famous historical estate",
"An introduction to a local marine life research center",
"A safety briefing for participants in a zip-lining adventure",
"A talk on the benefits of cultural preservation",
"A lecture on the principles of wildlife ecology",
"A monologue about the impact of space technology",
"An explanation of the process of wildlife conservation",
"A presentation on the history of a famous scientist",
"An overview of traditional crafts and artisans from different cultures",
"A lecture on the principles of effective intercultural communication"
]
four_people_scenarios = [
"A university lecture on history",
"A physics class discussing Newton's laws",
"A medical school seminar on anatomy",
"A training session on computer programming",
"A business school lecture on marketing strategies",
"A chemistry lab experiment and discussion",
"A language class practicing conversational skills",
"A workshop on creative writing techniques",
"A high school math lesson on calculus",
"A training program for customer service representatives",
"A lecture on environmental science and sustainability",
"A psychology class exploring human behavior",
"A music theory class analyzing compositions",
"A nursing school simulation for patient care",
"A computer science class on algorithms",
"A workshop on graphic design principles",
"A law school lecture on constitutional law",
"A geology class studying rock formations",
"A vocational training program for electricians",
"A history seminar focusing on ancient civilizations",
"A biology class dissecting specimens",
"A financial literacy course for adults",
"A literature class discussing classic novels",
"A training session for emergency response teams",
"A sociology lecture on social inequality",
"An art class exploring different painting techniques",
"A medical school seminar on diagnosis",
"A programming bootcamp teaching web development",
"An economics class analyzing market trends",
"A chemistry lab experiment on chemical reactions",
"A language class practicing pronunciation",
"A workshop on public speaking skills",
"A high school physics lesson on electromagnetism",
"A training program for IT professionals",
"A lecture on climate change and its effects",
"A psychology class studying cognitive psychology",
"A music class composing original songs",
"A nursing school simulation for patient assessment",
"A computer science class on data structures",
"A workshop on 3D modeling and animation",
"A law school lecture on contract law",
"A geography class examining world maps",
"A vocational training program for plumbers",
"A history seminar discussing revolutions",
"A biology class exploring genetics",
"A financial literacy course for teens",
"A literature class analyzing poetry",
"A training session for public speaking coaches",
"A sociology lecture on cultural diversity",
"An art class creating sculptures",
"A medical school seminar on surgical techniques",
"A programming bootcamp teaching app development",
"An economics class on global trade policies",
"A chemistry lab experiment on chemical bonding",
"A language class discussing idiomatic expressions",
"A workshop on conflict resolution",
"A high school biology lesson on evolution",
"A training program for project managers",
"A lecture on renewable energy sources",
"A psychology class on abnormal psychology",
"A music class rehearsing for a performance",
"A nursing school simulation for emergency response",
"A computer science class on cybersecurity",
"A workshop on digital marketing strategies",
"A law school lecture on intellectual property",
"A geology class analyzing seismic activity",
"A vocational training program for carpenters",
"A history seminar on the Renaissance",
"A chemistry class synthesizing compounds",
"A financial literacy course for seniors",
"A literature class interpreting Shakespearean plays",
"A training session for negotiation skills",
"A sociology lecture on urbanization",
"An art class creating digital art",
"A medical school seminar on patient communication",
"A programming bootcamp teaching mobile app development",
"An economics class on fiscal policy",
"A physics lab experiment on electromagnetism",
"A language class on cultural immersion",
"A workshop on time management",
"A high school chemistry lesson on stoichiometry",
"A training program for HR professionals",
"A lecture on space exploration and astronomy",
"A psychology class on human development",
"A music class practicing for a recital",
"A nursing school simulation for triage",
"A computer science class on web development frameworks",
"A workshop on team-building exercises",
"A law school lecture on criminal law",
"A geography class studying world cultures",
"A vocational training program for HVAC technicians",
"A history seminar on ancient civilizations",
"A biology class examining ecosystems",
"A financial literacy course for entrepreneurs",
"A literature class analyzing modern literature",
"A training session for leadership skills",
"A sociology lecture on gender studies",
"An art class exploring multimedia art",
"A medical school seminar on patient diagnosis",
"A programming bootcamp teaching software architecture"
]
academic_subjects = [
"Astrophysics",
"Microbiology",
"Political Science",
"Environmental Science",
"Literature",
"Biochemistry",
"Sociology",
"Art History",
"Geology",
"Economics",
"Psychology",
"History of Architecture",
"Linguistics",
"Neurobiology",
"Anthropology",
"Quantum Mechanics",
"Urban Planning",
"Philosophy",
"Marine Biology",
"International Relations",
"Medieval History",
"Geophysics",
"Finance",
"Educational Psychology",
"Graphic Design",
"Paleontology",
"Macroeconomics",
"Cognitive Psychology",
"Renaissance Art",
"Archaeology",
"Microeconomics",
"Social Psychology",
"Contemporary Art",
"Meteorology",
"Political Philosophy",
"Space Exploration",
"Cognitive Science",
"Classical Music",
"Oceanography",
"Public Health",
"Gender Studies",
"Baroque Art",
"Volcanology",
"Business Ethics",
"Music Composition",
"Environmental Policy",
"Media Studies",
"Ancient History",
"Seismology",
"Marketing",
"Human Development",
"Modern Art",
"Astronomy",
"International Law",
"Developmental Psychology",
"Film Studies",
"American History",
"Soil Science",
"Entrepreneurship",
"Clinical Psychology",
"Contemporary Dance",
"Space Physics",
"Political Economy",
"Cognitive Neuroscience",
"20th Century Literature",
"Public Administration",
"European History",
"Atmospheric Science",
"Supply Chain Management",
"Social Work",
"Japanese Literature",
"Planetary Science",
"Labor Economics",
"Industrial-Organizational Psychology",
"French Philosophy",
"Biogeochemistry",
"Strategic Management",
"Educational Sociology",
"Postmodern Literature",
"Public Relations",
"Middle Eastern History",
"Oceanography",
"International Development",
"Human Resources Management",
"Educational Leadership",
"Russian Literature",
"Quantum Chemistry",
"Environmental Economics",
"Environmental Psychology",
"Ancient Philosophy",
"Immunology",
"Comparative Politics",
"Child Development",
"Fashion Design",
"Geological Engineering",
"Macroeconomic Policy",
"Media Psychology",
"Byzantine Art",
"Ecology",
"International Business"
]

6
helper/exam_variant.py
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from enum import Enum
class ExamVariant(Enum):
FULL = "full"
PARTIAL = "partial"

2152
helper/exercises.py
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17
helper/file_helper.py
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import datetime
import os
from pathlib import Path
def delete_files_older_than_one_day(directory):
current_time = datetime.datetime.now()
for entry in os.scandir(directory):
if entry.is_file():
file_path = Path(entry)
file_name = file_path.name
file_modified_time = datetime.datetime.fromtimestamp(file_path.stat().st_mtime)
time_difference = current_time - file_modified_time
if time_difference.days > 1 and "placeholder" not in file_name:
file_path.unlink()
print(f"Deleted file: {file_path}")

65
helper/firebase_helper.py
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import logging
from google.cloud import storage
from pymongo.database import Database
def download_firebase_file(bucket_name, source_blob_name, destination_file_name):
# Downloads a file from Firebase Storage.
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
logging.info(f"File downloaded to {destination_file_name}")
return destination_file_name
def upload_file_firebase(bucket_name, destination_blob_name, source_file_name):
# Uploads a file to Firebase Storage.
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
try:
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
logging.info(f"File uploaded to {destination_blob_name}")
return True
except Exception as e:
import app
app.app.logger.error("Error uploading file to Google Cloud Storage: " + str(e))
return False
def upload_file_firebase_get_url(bucket_name, destination_blob_name, source_file_name):
# Uploads a file to Firebase Storage.
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
try:
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
logging.info(f"File uploaded to {destination_blob_name}")
# Make the file public
blob.make_public()
# Get the public URL
url = blob.public_url
return url
except Exception as e:
import app
app.app.logger.error("Error uploading file to Google Cloud Storage: " + str(e))
return None
def save_to_db_with_id(mongo_db: Database, collection: str, item, id: str):
collection_ref = mongo_db[collection]
document_ref = collection_ref.insert_one({"id": id, **item})
if document_ref:
logging.info(f"Document added with ID: {document_ref.inserted_id}")
return (True, document_ref.inserted_id)
else:
return (False, None)
def get_all(mongo_db: Database, collection: str):
return list(mongo_db[collection].find())

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helper/generate_jwt.py
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import os
import jwt
from dotenv import load_dotenv
load_dotenv()
# Define the payload (data to be included in the token)
payload = {'sub': 'test'}
# Define the secret key
secret_key = os.getenv("JWT_SECRET_KEY")
# Generate the JWT
jwt_token = jwt.encode(payload, secret_key, algorithm='HS256')
print(jwt_token)

5
helper/generate_jwt_secret.py
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import secrets
jwt_secret_key = secrets.token_hex(32)
print(jwt_secret_key)

179
helper/heygen_api.py
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import os
import random
import time
from logging import getLogger
import requests
from dotenv import load_dotenv
from helper.constants import *
from helper.firebase_helper import upload_file_firebase_get_url, save_to_db_with_id
from heygen.AvatarEnum import AvatarEnum
load_dotenv()
logger = getLogger(__name__)
# Get HeyGen token
TOKEN = os.getenv("HEY_GEN_TOKEN")
FIREBASE_BUCKET = os.getenv('FIREBASE_BUCKET')
# POST TO CREATE VIDEO
CREATE_VIDEO_URL = 'https://api.heygen.com/v1/template.generate'
GET_VIDEO_URL = 'https://api.heygen.com/v1/video_status.get'
POST_HEADER = {
'X-Api-Key': TOKEN,
'Content-Type': 'application/json'
}
GET_HEADER = {
'X-Api-Key': TOKEN
}
def create_videos_and_save_to_db(exercises, template, id):
avatar = random.choice(list(AvatarEnum))
# Speaking 1
# Using list comprehension to find the element with the desired value in the 'type' field
found_exercises_1 = [element for element in exercises if element.get('type') == 1]
# Check if any elements were found
if found_exercises_1:
exercise_1 = found_exercises_1[0]
sp1_questions = []
logger.info('Creating video for speaking part 1')
for question in exercise_1["questions"]:
sp1_result = create_video(question, avatar)
if sp1_result is not None:
sound_file_path = VIDEO_FILES_PATH + sp1_result
firebase_file_path = FIREBASE_SPEAKING_VIDEO_FILES_PATH + sp1_result
url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
video = {
"text": question,
"video_path": firebase_file_path,
"video_url": url
}
sp1_questions.append(video)
else:
logger.error("Failed to create video for part 1 question: " + exercise_1["question"])
template["exercises"][0]["prompts"] = sp1_questions
template["exercises"][0]["first_title"] = exercise_1["first_topic"]
template["exercises"][0]["second_title"] = exercise_1["second_topic"]
# Speaking 2
# Using list comprehension to find the element with the desired value in the 'type' field
found_exercises_2 = [element for element in exercises if element.get('type') == 2]
# Check if any elements were found
if found_exercises_2:
exercise_2 = found_exercises_2[0]
logger.info('Creating video for speaking part 2')
sp2_result = create_video(exercise_2["question"], avatar)
if sp2_result is not None:
sound_file_path = VIDEO_FILES_PATH + sp2_result
firebase_file_path = FIREBASE_SPEAKING_VIDEO_FILES_PATH + sp2_result
url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
sp2_video_path = firebase_file_path
sp2_video_url = url
template["exercises"][1]["prompts"] = exercise_2["prompts"]
template["exercises"][1]["text"] = exercise_2["question"]
template["exercises"][1]["title"] = exercise_2["topic"]
template["exercises"][1]["video_url"] = sp2_video_url
template["exercises"][1]["video_path"] = sp2_video_path
else:
logger.error("Failed to create video for part 2 question: " + exercise_2["question"])
# Speaking 3
# Using list comprehension to find the element with the desired value in the 'type' field
found_exercises_3 = [element for element in exercises if element.get('type') == 3]
# Check if any elements were found
if found_exercises_3:
exercise_3 = found_exercises_3[0]
sp3_questions = []
logger.info('Creating videos for speaking part 3')
for question in exercise_3["questions"]:
result = create_video(question, avatar)
if result is not None:
sound_file_path = VIDEO_FILES_PATH + result
firebase_file_path = FIREBASE_SPEAKING_VIDEO_FILES_PATH + result
url = upload_file_firebase_get_url(FIREBASE_BUCKET, firebase_file_path, sound_file_path)
video = {
"text": question,
"video_path": firebase_file_path,
"video_url": url
}
sp3_questions.append(video)
else:
logger.error("Failed to create video for part 3 question: " + question)
template["exercises"][2]["prompts"] = sp3_questions
template["exercises"][2]["title"] = exercise_3["topic"]
if not found_exercises_3:
template["exercises"].pop(2)
if not found_exercises_2:
template["exercises"].pop(1)
if not found_exercises_1:
template["exercises"].pop(0)
save_to_db_with_id("speaking", template, id)
logger.info('Saved speaking to DB with id ' + id + " : " + str(template))
def create_video(text, avatar):
# POST TO CREATE VIDEO
create_video_url = 'https://api.heygen.com/v2/template/' + avatar + '/generate'
data = {
"test": False,
"caption": False,
"title": "video_title",
"variables": {
"script_here": {
"name": "script_here",
"type": "text",
"properties": {
"content": text
}
}
}
}
response = requests.post(create_video_url, headers=POST_HEADER, json=data)
logger.info(response.status_code)
logger.info(response.json())
# GET TO CHECK STATUS AND GET VIDEO WHEN READY
video_id = response.json()["data"]["video_id"]
params = {
'video_id': response.json()["data"]["video_id"]
}
response = {}
status = "processing"
error = None
while status != "completed" and error is None:
response = requests.get(GET_VIDEO_URL, headers=GET_HEADER, params=params)
response_data = response.json()
status = response_data["data"]["status"]
error = response_data["data"]["error"]
if status != "completed" and error is None:
logger.info(f"Status: {status}")
time.sleep(10) # Wait for 10 second before the next request
logger.info(response.status_code)
logger.info(response.json())
# DOWNLOAD VIDEO
download_url = response.json()['data']['video_url']
output_directory = 'download-video/'
output_filename = video_id + '.mp4'
response = requests.get(download_url)
if response.status_code == 200:
os.makedirs(output_directory, exist_ok=True) # Create the directory if it doesn't exist
output_path = os.path.join(output_directory, output_filename)
with open(output_path, 'wb') as f:
f.write(response.content)
logger.info(f"File '{output_filename}' downloaded successfully.")
return output_filename
else:
logger.error(f"Failed to download file. Status code: {response.status_code}")
return None

250
helper/openai_interface.py
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@@ -1,250 +0,0 @@
import json
import os
import re
from dotenv import load_dotenv
from openai import OpenAI
from helper.constants import BLACKLISTED_WORDS, GPT_3_5_TURBO
from helper.token_counter import count_tokens
load_dotenv()
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
MAX_TOKENS = 4097
TOP_P = 0.9
FREQUENCY_PENALTY = 0.5
TRY_LIMIT = 2
try_count = 0
# GRADING SUMMARY
chat_config = {'max_tokens': 1000, 'temperature': 0.2}
section_keys = ['reading', 'listening', 'writing', 'speaking', 'level']
grade_top_limit = 9
tools = [{
"type": "function",
"function": {
"name": "save_evaluation_and_suggestions",
"description": "Saves the evaluation and suggestions requested by input.",
"parameters": {
"type": "object",
"properties": {
"evaluation": {
"type": "string",
"description": "A comment on the IELTS section grade obtained in the specific section and what it could mean without suggestions.",
},
"suggestions": {
"type": "string",
"description": "A small paragraph text with suggestions on how to possibly get a better grade than the one obtained.",
},
"bullet_points": {
"type": "string",
"description": "Text with four bullet points to improve the english speaking ability. Only include text for the bullet points separated by a paragraph. ",
},
},
"required": ["evaluation", "suggestions"],
},
}
}]
def check_fields(obj, fields):
return all(field in obj for field in fields)
def make_openai_call(model, messages, token_count, fields_to_check, temperature, check_blacklisted=True):
global try_count
result = client.chat.completions.create(
model=model,
max_tokens=int(MAX_TOKENS - token_count - 300),
temperature=float(temperature),
messages=messages,
response_format={"type": "json_object"}
)
result = result.choices[0].message.content
if check_blacklisted:
found_blacklisted_word = get_found_blacklisted_words(result)
if found_blacklisted_word is not None and try_count < TRY_LIMIT:
from app import app
app.logger.warning("Result contains blacklisted words: " + str(found_blacklisted_word))
try_count = try_count + 1
return make_openai_call(model, messages, token_count, fields_to_check, temperature)
elif found_blacklisted_word is not None and try_count >= TRY_LIMIT:
return ""
if fields_to_check is None:
return json.loads(result)
if check_fields(result, fields_to_check) is False and try_count < TRY_LIMIT:
try_count = try_count + 1
return make_openai_call(model, messages, token_count, fields_to_check, temperature)
elif try_count >= TRY_LIMIT:
try_count = 0
return json.loads(result)
else:
try_count = 0
return json.loads(result)
# GRADING SUMMARY
def calculate_grading_summary(body):
extracted_sections = extract_existing_sections_from_body(body, section_keys)
ret = []
for section in extracted_sections:
openai_response_dict = calculate_section_grade_summary(section)
ret = ret + [{'code': section['code'], 'name': section['name'], 'grade': section['grade'],
'evaluation': openai_response_dict['evaluation'],
'suggestions': openai_response_dict['suggestions'],
'bullet_points': parse_bullet_points(openai_response_dict['bullet_points'], section['grade'])}]
return {'sections': ret}
def calculate_section_grade_summary(section):
messages = [
{
"role": "user",
"content": "You are a IELTS test section grade evaluator. You will receive a IELTS test section name and the grade obtained in the section. You should offer a evaluation comment on this grade and separately suggestions on how to possibly get a better grade.",
},
{
"role": "user",
"content": "Section: " + str(section['name']) + " Grade: " + str(section['grade']),
},
{"role": "user", "content": "Speak in third person."},
{"role": "user",
"content": "Don't offer suggestions in the evaluation comment. Only in the suggestions section."},
{"role": "user",
"content": "Your evaluation comment on the grade should enunciate the grade, be insightful, be speculative, be one paragraph long. "},
{"role": "user", "content": "Please save the evaluation comment and suggestions generated."},
{"role": "user", "content": f"Offer bullet points to improve the english {str(section['name'])} ability."},
]
if section['code'] == "level":
messages[2:2] = [{
"role": "user",
"content": "This section is comprised of multiple choice questions that measure the user's overall english level. These multiple choice questions are about knowledge on vocabulary, syntax, grammar rules, and contextual usage. The grade obtained measures the ability in these areas and english language overall."
}]
elif section['code'] == "speaking":
messages[2:2] = [{"role": "user",
"content": "This section is s designed to assess the English language proficiency of individuals who want to study or work in English-speaking countries. The speaking section evaluates a candidate's ability to communicate effectively in spoken English."}]
res = client.chat.completions.create(
model="gpt-3.5-turbo",
max_tokens=chat_config['max_tokens'],
temperature=chat_config['temperature'],
tools=tools,
messages=messages)
return parse_openai_response(res)
def parse_openai_response(response):
if 'choices' in response and len(response['choices']) > 0 and 'message' in response['choices'][
0] and 'tool_calls' in response['choices'][0]['message'] and isinstance(
response['choices'][0]['message']['tool_calls'], list) and len(
response['choices'][0]['message']['tool_calls']) > 0 and \
response['choices'][0]['message']['tool_calls'][0]['function']['arguments']:
return json.loads(response['choices'][0]['message']['tool_calls'][0]['function']['arguments'])
else:
return {'evaluation': "", 'suggestions': "", 'bullet_points': []}
def extract_existing_sections_from_body(my_dict, keys_to_extract):
if 'sections' in my_dict and isinstance(my_dict['sections'], list) and len(my_dict['sections']) > 0:
return list(filter(
lambda item: 'code' in item and item['code'] in keys_to_extract and 'grade' in item and 'name' in item,
my_dict['sections']))
def parse_bullet_points(bullet_points_str, grade):
max_grade_for_suggestions = 9
if isinstance(bullet_points_str, str) and grade < max_grade_for_suggestions:
# Split the string by '\n'
lines = bullet_points_str.split('\n')
# Remove '-' and trim whitespace from each line
cleaned_lines = [line.replace('-', '').strip() for line in lines]
# Add '.' to lines that don't end with it
return [line + '.' if line and not line.endswith('.') else line for line in cleaned_lines]
else:
return []
def get_fixed_text(text):
messages = [
{"role": "system", "content": ('You are a helpful assistant designed to output JSON on this format: '
'{"fixed_text": "fixed test with no misspelling errors"}')
},
{"role": "user", "content": (
'Fix the errors in the given text and put it in a JSON. Do not complete the answer, only replace what '
'is wrong. \n The text: "' + text + '"')
}
]
token_count = count_total_tokens(messages)
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, ["fixed_text"], 0.2, False)
return response["fixed_text"]
def get_speaking_corrections(text):
messages = [
{"role": "system", "content": ('You are a helpful assistant designed to output JSON on this format: '
'{"fixed_text": "fixed transcription with no misspelling errors"}')
},
{"role": "user", "content": (
'Fix the errors in the provided transcription and put it in a JSON. Do not complete the answer, only '
'replace what is wrong. \n The text: "' + text + '"')
}
]
token_count = count_total_tokens(messages)
response = make_openai_call(GPT_3_5_TURBO, messages, token_count, ["fixed_text"], 0.2, False)
return response["fixed_text"]
def has_blacklisted_words(text: str):
text_lower = text.lower()
return any(word in text_lower for word in BLACKLISTED_WORDS)
def get_found_blacklisted_words(text: str):
text_lower = text.lower()
for word in BLACKLISTED_WORDS:
if re.search(r'\b' + re.escape(word) + r'\b', text_lower):
return word
return None
def remove_special_characters_from_beginning(string):
cleaned_string = string.lstrip('\n')
if string.startswith("'") or string.startswith('"'):
cleaned_string = string[1:]
if cleaned_string.endswith('"'):
return cleaned_string[:-1]
else:
return cleaned_string
def replace_expression_in_object(obj, expression, replacement):
if isinstance(obj, dict):
for key in obj:
if isinstance(obj[key], str):
obj[key] = obj[key].replace(expression, replacement)
elif isinstance(obj[key], list):
obj[key] = [replace_expression_in_object(item, expression, replacement) for item in obj[key]]
elif isinstance(obj[key], dict):
obj[key] = replace_expression_in_object(obj[key], expression, replacement)
return obj
def count_total_tokens(messages):
total_tokens = 0
for message in messages:
total_tokens += count_tokens(message["content"])["n_tokens"]
return total_tokens

138
helper/speech_to_text_helper.py
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@@ -1,138 +0,0 @@
import os
import random
import boto3
import nltk
import whisper
nltk.download('words')
from nltk.corpus import words
from helper.constants import *
def speech_to_text(file_path):
if os.path.exists(file_path):
model = whisper.load_model("base")
result = model.transcribe(file_path, fp16=False, language='English', verbose=False)
return result["text"]
else:
print("File not found:", file_path)
raise Exception("File " + file_path + " not found.")
def text_to_speech(text: str, file_name: str):
# Initialize the Amazon Polly client
client = boto3.client(
'polly',
region_name='eu-west-1',
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
)
voice = random.choice(ALL_NEURAL_VOICES)['Id']
# Initialize an empty list to store audio segments
audio_segments = []
for part in divide_text(text):
tts_response = client.synthesize_speech(
Engine="neural",
Text=part,
OutputFormat="mp3",
VoiceId=voice
)
audio_segments.append(tts_response['AudioStream'].read())
# Add finish message
audio_segments.append(client.synthesize_speech(
Engine="neural",
Text="This audio recording, for the listening exercise, has finished.",
OutputFormat="mp3",
VoiceId="Stephen"
)['AudioStream'].read())
# Combine the audio segments into a single audio file
combined_audio = b"".join(audio_segments)
# Save the combined audio to a single file
with open(file_name, "wb") as f:
f.write(combined_audio)
print("Speech segments saved to " + file_name)
def conversation_text_to_speech(conversation: list, file_name: str):
# Initialize the Amazon Polly client
client = boto3.client(
'polly',
region_name='eu-west-1',
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY")
)
# Initialize an empty list to store audio segments
audio_segments = []
# Iterate through the text segments, convert to audio segments, and store them
for segment in conversation:
response = client.synthesize_speech(
Engine="neural",
Text=segment["text"],
OutputFormat="mp3",
VoiceId=segment["voice"]
)
audio_segments.append(response['AudioStream'].read())
# Add finish message
audio_segments.append(client.synthesize_speech(
Engine="neural",
Text="This audio recording, for the listening exercise, has finished.",
OutputFormat="mp3",
VoiceId="Stephen"
)['AudioStream'].read())
# Combine the audio segments into a single audio file
combined_audio = b"".join(audio_segments)
# Save the combined audio to a single file
with open(file_name, "wb") as f:
f.write(combined_audio)
print("Speech segments saved to " + file_name)
def has_words(text: str):
if not has_common_words(text):
return False
english_words = set(words.words())
words_in_input = text.split()
return any(word.lower() in english_words for word in words_in_input)
def has_x_words(text: str, quantity):
if not has_common_words(text):
return False
english_words = set(words.words())
words_in_input = text.split()
english_word_count = sum(1 for word in words_in_input if word.lower() in english_words)
return english_word_count >= quantity
def has_common_words(text: str):
english_words = {"the", "be", "to", "of", "and", "a", "in", "that", "have", "i"}
words_in_input = text.split()
english_word_count = sum(1 for word in words_in_input if word.lower() in english_words)
return english_word_count >= 10
def divide_text(text, max_length=3000):
if len(text) <= max_length:
return [text]
divisions = []
current_position = 0
while current_position < len(text):
next_position = min(current_position + max_length, len(text))
next_period_position = text.rfind('.', current_position, next_position)
if next_period_position != -1 and next_period_position > current_position:
divisions.append(text[current_position:next_period_position + 1])
current_position = next_period_position + 1
else:
# If no '.' found in the next chunk, split at max_length
divisions.append(text[current_position:next_position])
current_position = next_position
return divisions

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heygen/AvatarEnum.py
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from enum import Enum
class AvatarEnum(Enum):
MATTHEW_NOAH = "5912afa7c77c47d3883af3d874047aaf"
VERA_CERISE = "9e58d96a383e4568a7f1e49df549e0e4"
EDWARD_TONY = "d2cdd9c0379a4d06ae2afb6e5039bd0c"
TANYA_MOLLY = "045cb5dcd00042b3a1e4f3bc1c12176b"
KAYLA_ABBI = "1ae1e5396cc444bfad332155fdb7a934"
JEROME_RYAN = "0ee6aa7cc1084063a630ae514fccaa31"
TYLER_CHRISTOPHER = "5772cff935844516ad7eeff21f839e43"

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import json
import os
import pathlib
import logging.config
import logging.handlers
import aioboto3
import contextlib
from contextlib import asynccontextmanager
from collections import defaultdict
from typing import List
from http import HTTPStatus
import httpx
from fastapi import FastAPI, Request
from fastapi.encoders import jsonable_encoder
from fastapi.exceptions import RequestValidationError
from fastapi.middleware import Middleware
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import nltk
from starlette import status
from ielts_be.api import router
from ielts_be.configs import DependencyInjector
from ielts_be.exceptions import CustomException
from ielts_be.middlewares import AuthenticationMiddleware, AuthBackend
from ielts_be.services.impl import OpenAIWhisper
@asynccontextmanager
async def lifespan(_app: FastAPI):
"""
Startup and Shutdown logic is in this lifespan method
https://fastapi.tiangolo.com/advanced/events/
"""
# NLTK required datasets download
nltk.download('words')
nltk.download("punkt")
# AWS Polly client instantiation
context_stack = contextlib.AsyncExitStack()
session = aioboto3.Session()
polly_client = await context_stack.enter_async_context(
session.client(
'polly',
region_name='eu-west-1',
aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID")
)
)
http_client = httpx.AsyncClient()
stt = OpenAIWhisper()
DependencyInjector(
polly_client,
http_client,
stt
).inject()
# Setup logging
config_file = pathlib.Path("./ielts_be/configs/logging/logging_config.json")
with open(config_file) as f_in:
config = json.load(f_in)
logging.config.dictConfig(config)
yield
stt.close()
await http_client.aclose()
await polly_client.close()
await context_stack.aclose()
def setup_listeners(_app: FastAPI) -> None:
@_app.exception_handler(RequestValidationError)
async def custom_form_validation_error(request, exc):
"""
Don't delete request param
"""
reformatted_message = defaultdict(list)
for pydantic_error in exc.errors():
loc, msg = pydantic_error["loc"], pydantic_error["msg"]
filtered_loc = loc[1:] if loc[0] in ("body", "query", "path") else loc
field_string = ".".join(filtered_loc)
if field_string == "cookie.refresh_token":
return JSONResponse(
status_code=401,
content={"error_code": 401, "message": HTTPStatus.UNAUTHORIZED.description},
)
reformatted_message[field_string].append(msg)
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content=jsonable_encoder(
{"details": "Invalid request!", "errors": reformatted_message}
),
)
@_app.exception_handler(CustomException)
async def custom_exception_handler(request: Request, exc: CustomException):
"""
Don't delete request param
"""
return JSONResponse(
status_code=exc.code,
content={"error_code": exc.error_code, "message": exc.message},
)
@_app.exception_handler(Exception)
async def default_exception_handler(request: Request, exc: Exception):
"""
Don't delete request param
"""
return JSONResponse(
status_code=500,
content=str(exc),
)
def setup_middleware() -> List[Middleware]:
middleware = [
Middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
),
Middleware(
AuthenticationMiddleware,
backend=AuthBackend()
)
]
return middleware
def create_app() -> FastAPI:
env = os.getenv("ENV")
_app = FastAPI(
docs_url="/docs" if env != "production" else None,
redoc_url="/redoc" if env != "production" else None,
middleware=setup_middleware(),
lifespan=lifespan
)
_app.include_router(router)
setup_listeners(_app)
return _app
app = create_app()

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ielts_be/api/__init__.py Normal file
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from fastapi import APIRouter
from .training import training_router
from .user import user_router
from .exam import exam_router
router = APIRouter(prefix="/api", tags=["Home"])
@router.get('/healthcheck')
async def healthcheck():
return {"healthy": True}
router.include_router(training_router, prefix="/training", tags=["Training"])
router.include_router(user_router, prefix="/user", tags=["Users"])
router.include_router(exam_router)

16
ielts_be/api/exam/__init__.py Normal file
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from fastapi import APIRouter
from .listening import listening_router
from .reading import reading_router
from .speaking import speaking_router
from .writing import writing_router
from .level import level_router
from .grade import grade_router
exam_router = APIRouter()
exam_router.include_router(listening_router, prefix="/listening", tags=["Listening"])
exam_router.include_router(reading_router, prefix="/reading", tags=["Reading"])
exam_router.include_router(speaking_router, prefix="/speaking", tags=["Speaking"])
exam_router.include_router(writing_router, prefix="/writing", tags=["Writing"])
exam_router.include_router(level_router, prefix="/level", tags=["Level"])
exam_router.include_router(grade_router, prefix="/grade", tags=["Grade"])

64
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from dependency_injector.wiring import inject, Provide
from fastapi import APIRouter, Depends, Path, Request, BackgroundTasks
from ielts_be.controllers import IGradeController
from ielts_be.dtos.writing import WritingGradeTaskDTO
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
controller = "grade_controller"
grade_router = APIRouter()
@grade_router.post(
'/writing/{task}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def grade_writing_task(
data: WritingGradeTaskDTO,
background_tasks: BackgroundTasks,
task: int = Path(..., ge=1, le=2),
grade_controller: IGradeController = Depends(Provide[controller])
):
return await grade_controller.grade_writing_task(task, data, background_tasks)
@grade_router.post(
'/speaking/{task}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def grade_speaking_task(
request: Request,
background_tasks: BackgroundTasks,
task: int = Path(..., ge=1, le=3),
grade_controller: IGradeController = Depends(Provide[controller])
):
form = await request.form()
return await grade_controller.grade_speaking_task(task, form, background_tasks)
@grade_router.post(
'/summary',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def grading_summary(
request: Request,
grade_controller: IGradeController = Depends(Provide[controller])
):
data = await request.json()
return await grade_controller.grading_summary(data)
@grade_router.post(
'/short_answers',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def grade_short_answers(
request: Request,
grade_controller: IGradeController = Depends(Provide[controller])
):
data = await request.json()
return await grade_controller.grade_short_answers(data)

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from dependency_injector.wiring import Provide, inject
from fastapi import APIRouter, Depends, UploadFile, Request
from ielts_be.dtos.level import LevelExercisesDTO
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.controllers import ILevelController
controller = "level_controller"
level_router = APIRouter()
@level_router.post(
'/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_exercises(
dto: LevelExercisesDTO,
level_controller: ILevelController = Depends(Provide[controller])
):
return await level_controller.generate_exercises(dto)
@level_router.get(
'/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def get_level_exam(
level_controller: ILevelController = Depends(Provide[controller])
):
return await level_controller.get_level_exam()
@level_router.get(
'/utas',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def get_level_utas(
level_controller: ILevelController = Depends(Provide[controller])
):
return await level_controller.get_level_utas()
@level_router.post(
'/import/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def import_level(
exercises: UploadFile,
solutions: UploadFile = None,
level_controller: ILevelController = Depends(Provide[controller])
):
return await level_controller.upload_level(exercises, solutions)
@level_router.post(
'/custom/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def custom_level(
request: Request,
level_controller: ILevelController = Depends(Provide[controller])
):
data = await request.json()
return await level_controller.get_custom_level(data)

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import random
from dependency_injector.wiring import Provide, inject
from fastapi import APIRouter, Depends, Path, Query, UploadFile
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.controllers import IListeningController
from ielts_be.configs.constants import EducationalContent
from ielts_be.dtos.listening import GenerateListeningExercises, Dialog
controller = "listening_controller"
listening_router = APIRouter()
@listening_router.post(
'/import',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def upload(
exercises: UploadFile,
solutions: UploadFile = None,
listening_controller: IListeningController = Depends(Provide[controller])
):
return await listening_controller.import_exam(exercises, solutions)
@listening_router.get(
'/{section}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_listening_dialog(
section: int = Path(..., ge=1, le=4),
difficulty: str = Query(default=None),
topic: str = Query(default=None),
listening_controller: IListeningController = Depends(Provide[controller])
):
difficulty = random.choice(EducationalContent.DIFFICULTIES) if not difficulty else difficulty
topic = random.choice(EducationalContent.TOPICS) if not topic else topic
return await listening_controller.generate_listening_dialog(section, topic, difficulty)
@listening_router.post(
'/media',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_mp3(
dto: Dialog,
listening_controller: IListeningController = Depends(Provide[controller])
):
return await listening_controller.generate_mp3(dto)
@listening_router.post(
'/transcribe',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def transcribe_dialog(
audio: UploadFile,
listening_controller: IListeningController = Depends(Provide[controller])
):
return await listening_controller.transcribe_dialog(audio)
@listening_router.post(
'/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_listening_exercise(
dto: GenerateListeningExercises,
listening_controller: IListeningController = Depends(Provide[controller])
):
return await listening_controller.get_listening_question(dto)

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ielts_be/api/exam/reading.py Normal file
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import random
from typing import Optional
from dependency_injector.wiring import Provide, inject
from fastapi import APIRouter, Depends, Path, Query, UploadFile
from ielts_be.configs.constants import EducationalContent
from ielts_be.dtos.reading import ReadingDTO
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.controllers import IReadingController
controller = "reading_controller"
reading_router = APIRouter()
@reading_router.post(
'/import',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def upload(
exercises: UploadFile,
solutions: UploadFile = None,
reading_controller: IReadingController = Depends(Provide[controller])
):
return await reading_controller.import_exam(exercises, solutions)
@reading_router.get(
'/{passage}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_passage(
topic: Optional[str] = Query(None),
word_count: Optional[int] = Query(None),
passage: int = Path(..., ge=1, le=3),
reading_controller: IReadingController = Depends(Provide[controller])
):
topic = random.choice(EducationalContent.TOPICS) if not topic else topic
return await reading_controller.generate_reading_passage(passage, topic, word_count)
@reading_router.post(
'/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_reading(
dto: ReadingDTO,
reading_controller: IReadingController = Depends(Provide[controller])
):
return await reading_controller.generate_reading_exercises(dto)

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ielts_be/api/exam/speaking.py Normal file
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import random
from typing import Optional
from dependency_injector.wiring import inject, Provide
from fastapi import APIRouter, Path, Query, Depends
from ielts_be.dtos.speaking import Video
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.configs.constants import EducationalContent
from ielts_be.controllers import ISpeakingController
controller = "speaking_controller"
speaking_router = APIRouter()
@speaking_router.post(
'/media',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_video(
video: Video,
speaking_controller: ISpeakingController = Depends(Provide[controller])
):
return await speaking_controller.generate_video(video.text, video.avatar)
@speaking_router.get(
'/media/{vid_id}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def poll_video(
vid_id: str = Path(...),
speaking_controller: ISpeakingController = Depends(Provide[controller])
):
return await speaking_controller.poll_video(vid_id)
@speaking_router.get(
'/avatars',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def get_avatars(
speaking_controller: ISpeakingController = Depends(Provide[controller])
):
return await speaking_controller.get_avatars()
@speaking_router.get(
'/{task}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def get_speaking_task(
task: int = Path(..., ge=1, le=3),
topic: Optional[str] = Query(None),
first_topic: Optional[str] = Query(None),
second_topic: Optional[str] = Query(None),
difficulty: str = Query(default=random.choice(EducationalContent.DIFFICULTIES)),
speaking_controller: ISpeakingController = Depends(Provide[controller])
):
if not second_topic:
topic_or_first_topic = topic if topic else random.choice(EducationalContent.MTI_TOPICS)
else:
topic_or_first_topic = first_topic if first_topic else random.choice(EducationalContent.MTI_TOPICS)
second_topic = second_topic if second_topic else random.choice(EducationalContent.MTI_TOPICS)
return await speaking_controller.get_speaking_part(task, topic_or_first_topic, second_topic, difficulty)

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ielts_be/api/exam/writing.py Normal file
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import random
from dependency_injector.wiring import inject, Provide
from fastapi import APIRouter, Path, Query, Depends, UploadFile, File
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.configs.constants import EducationalContent
from ielts_be.controllers import IWritingController
controller = "writing_controller"
writing_router = APIRouter()
@writing_router.post(
'/{task}/attachment',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_writing_academic(
task: int = Path(..., ge=1, le=2),
file: UploadFile = File(...),
difficulty: str = Query(default=None),
writing_controller: IWritingController = Depends(Provide[controller])
):
difficulty = random.choice(EducationalContent.DIFFICULTIES) if not difficulty else difficulty
return await writing_controller.get_writing_task_academic_question(task, file, difficulty)
@writing_router.get(
'/{task}',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def generate_writing(
task: int = Path(..., ge=1, le=2),
difficulty: str = Query(default=None),
topic: str = Query(default=None),
writing_controller: IWritingController = Depends(Provide[controller])
):
difficulty = random.choice(EducationalContent.DIFFICULTIES) if not difficulty else difficulty
topic = random.choice(EducationalContent.MTI_TOPICS) if not topic else topic
return await writing_controller.get_writing_task_general_question(task, topic, difficulty)

9
ielts_be/api/home.py Normal file
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from fastapi import APIRouter
home_router = APIRouter()
@home_router.get(
'/healthcheck'
)
async def healthcheck():
return {"healthy": True}

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ielts_be/api/training.py Normal file
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from dependency_injector.wiring import Provide, inject
from fastapi import APIRouter, Depends, Request
from ielts_be.dtos.training import FetchTipsDTO
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.controllers import ITrainingController
controller = "training_controller"
training_router = APIRouter()
@training_router.post(
'/tips',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def get_reading_passage(
data: FetchTipsDTO,
training_controller: ITrainingController = Depends(Provide[controller])
):
return await training_controller.fetch_tips(data)
@training_router.post(
'/',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def training_content(
request: Request,
training_controller: ITrainingController = Depends(Provide[controller])
):
data = await request.json()
return await training_controller.get_training_content(data)

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ielts_be/api/user.py Normal file
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from dependency_injector.wiring import Provide, inject
from fastapi import APIRouter, Depends
from ielts_be.dtos.user_batch import BatchUsersDTO
from ielts_be.middlewares import Authorized, IsAuthenticatedViaBearerToken
from ielts_be.controllers import IUserController
controller = "user_controller"
user_router = APIRouter()
@user_router.post(
'/import',
dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
)
@inject
async def batch_import(
batch: BatchUsersDTO,
user_controller: IUserController = Depends(Provide[controller])
):
return await user_controller.batch_import(batch)

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ielts_be/configs/__init__.py Normal file
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from .dependency_injection import DependencyInjector
__all__ = [
"DependencyInjector"
]

780
ielts_be/configs/constants.py Normal file
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from enum import Enum
########################################################################################################################
# DISCLAIMER #
# #
# All the array and dict "constants" are mutable variables, if somewhere in the app you modify them in any way, shape #
# or form all the other methods that will use these "constants" will also use the modified version. If you're unsure #
# whether a method will modify it use copy's deepcopy: #
# #
# from copy import deepcopy #
# #
# new_ref = deepcopy(CONSTANT) #
# #
# Using a wrapper method that returns a "constant" won't handle nested mutables. #
########################################################################################################################
BLACKLISTED_WORDS = ["jesus", "sex", "gay", "lesbian", "homosexual", "god", "angel", "pornography", "beer", "wine",
"cocaine", "alcohol", "nudity", "lgbt", "casino", "gambling", "catholicism",
"discrimination", "politic", "christianity", "islam", "christian", "christians",
"jews", "jew", "discrimination", "discriminatory"]
class UserDefaults:
DESIRED_LEVELS = {
"reading": 9,
"listening": 9,
"writing": 9,
"speaking": 9,
}
LEVELS = {
"reading": 0,
"listening": 0,
"writing": 0,
"speaking": 0,
}
class ExamVariant(Enum):
FULL = "full"
PARTIAL = "partial"
class ReadingExerciseType(str, Enum):
fillBlanks = "fillBlanks"
writeBlanks = "writeBlanks"
trueFalse = "trueFalse"
paragraphMatch = "paragraphMatch"
ideaMatch = "ideaMatch"
multipleChoice = "multipleChoice"
class ListeningExerciseType(str, Enum):
multipleChoice = "multipleChoice"
multipleChoice3Options = "multipleChoice3Options"
writeBlanksQuestions = "writeBlanksQuestions"
writeBlanksFill = "writeBlanksFill"
writeBlanksForm = "writeBlanksForm"
trueFalse = "trueFalse"
class LevelExerciseType(str, Enum):
multipleChoice = "multipleChoice"
mcBlank = "mcBlank"
mcUnderline = "mcUnderline"
blankSpace = "blankSpaceText"
passageUtas = "passageUtas"
fillBlanksMC = "fillBlanksMC"
class CustomLevelExerciseTypes(Enum):
MULTIPLE_CHOICE_4 = "multiple_choice_4"
MULTIPLE_CHOICE_BLANK_SPACE = "multiple_choice_blank_space"
MULTIPLE_CHOICE_UNDERLINED = "multiple_choice_underlined"
BLANK_SPACE_TEXT = "blank_space_text"
READING_PASSAGE_UTAS = "reading_passage_utas"
WRITING_LETTER = "writing_letter"
WRITING_2 = "writing_2"
SPEAKING_1 = "speaking_1"
SPEAKING_2 = "speaking_2"
SPEAKING_3 = "speaking_3"
READING_1 = "reading_1"
READING_2 = "reading_2"
READING_3 = "reading_3"
LISTENING_1 = "listening_1"
LISTENING_2 = "listening_2"
LISTENING_3 = "listening_3"
LISTENING_4 = "listening_4"
class QuestionType(Enum):
LISTENING_SECTION_1 = "Listening Section 1"
LISTENING_SECTION_2 = "Listening Section 2"
LISTENING_SECTION_3 = "Listening Section 3"
LISTENING_SECTION_4 = "Listening Section 4"
WRITING_TASK_1 = "Writing Task 1"
WRITING_TASK_2 = "Writing Task 2"
SPEAKING_1 = "Speaking Task Part 1"
SPEAKING_2 = "Speaking Task Part 2"
READING_PASSAGE_1 = "Reading Passage 1"
READING_PASSAGE_2 = "Reading Passage 2"
READING_PASSAGE_3 = "Reading Passage 3"
class FilePaths:
AUDIO_FILES_PATH = 'download-audio/'
FIREBASE_LISTENING_AUDIO_FILES_PATH = 'listening_recordings/'
VIDEO_FILES_PATH = 'download-video/'
FIREBASE_SPEAKING_VIDEO_FILES_PATH = 'speaking_videos/'
FIREBASE_FAILED_TRANSCRIPTION_FILES_PATH = 'failed_transcriptions/'
WRITING_ATTACHMENTS = 'writing_attachments/'
class TemperatureSettings:
GRADING_TEMPERATURE = 0.1
TIPS_TEMPERATURE = 0.2
GEN_QUESTION_TEMPERATURE = 0.7
class GPTModels:
GPT_3_5_TURBO = "gpt-3.5-turbo"
GPT_4_TURBO = "gpt-4-turbo"
GPT_4_O = "gpt-4o"
GPT_3_5_TURBO_16K = "gpt-3.5-turbo-16k"
GPT_3_5_TURBO_INSTRUCT = "gpt-3.5-turbo-instruct"
GPT_4_PREVIEW = "gpt-4-turbo-preview"
class FieldsAndExercises:
GRADING_FIELDS = ['comment', 'overall', 'task_response']
GEN_FIELDS = ['topic']
GEN_TEXT_FIELDS = ['title']
LISTENING_GEN_FIELDS = ['transcript', 'exercise']
READING_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch']
READING_3_EXERCISE_TYPES = ['fillBlanks', 'writeBlanks', 'trueFalse', 'paragraphMatch', 'ideaMatch']
LISTENING_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm']
LISTENING_1_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksFill',
'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm', 'writeBlanksForm']
LISTENING_2_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions']
LISTENING_3_EXERCISE_TYPES = ['multipleChoice3Options', 'writeBlanksQuestions']
LISTENING_4_EXERCISE_TYPES = ['multipleChoice', 'writeBlanksQuestions', 'writeBlanksFill', 'writeBlanksForm']
TOTAL_READING_PASSAGE_1_EXERCISES = 13
TOTAL_READING_PASSAGE_2_EXERCISES = 13
TOTAL_READING_PASSAGE_3_EXERCISES = 14
TOTAL_LISTENING_SECTION_1_EXERCISES = 10
TOTAL_LISTENING_SECTION_2_EXERCISES = 10
TOTAL_LISTENING_SECTION_3_EXERCISES = 10
TOTAL_LISTENING_SECTION_4_EXERCISES = 10
class MinTimers:
LISTENING_MIN_TIMER_DEFAULT = 30
WRITING_MIN_TIMER_DEFAULT = 60
SPEAKING_MIN_TIMER_DEFAULT = 14
class Voices:
EN_US_VOICES = [
{'Gender': 'Female', 'Id': 'Salli', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Salli',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Matthew', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Matthew',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Kimberly', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kimberly',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Kendra', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kendra',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Justin', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Justin',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Joey', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Joey',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Joanna', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Joanna',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Ivy', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Ivy',
'SupportedEngines': ['neural', 'standard']}]
EN_GB_VOICES = [
{'Gender': 'Female', 'Id': 'Emma', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Emma',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Male', 'Id': 'Brian', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Brian',
'SupportedEngines': ['neural', 'standard']},
{'Gender': 'Female', 'Id': 'Amy', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Amy',
'SupportedEngines': ['neural', 'standard']}]
EN_GB_WLS_VOICES = [
{'Gender': 'Male', 'Id': 'Geraint', 'LanguageCode': 'en-GB-WLS', 'LanguageName': 'Welsh English', 'Name': 'Geraint',
'SupportedEngines': ['standard']}]
EN_AU_VOICES = [{'Gender': 'Male', 'Id': 'Russell', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Russell', 'SupportedEngines': ['standard']},
{'Gender': 'Female', 'Id': 'Nicole', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Nicole', 'SupportedEngines': ['standard']}]
ALL_VOICES = EN_US_VOICES + EN_GB_VOICES + EN_GB_WLS_VOICES + EN_AU_VOICES
MALE_VOICES = [item for item in ALL_VOICES if item.get('Gender') == 'Male']
FEMALE_VOICES = [item for item in ALL_VOICES if item.get('Gender') == 'Female']
class NeuralVoices:
NEURAL_EN_US_VOICES = [
{'Gender': 'Female', 'Id': 'Danielle', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Danielle',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Gregory', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Gregory',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Kevin', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Kevin',
'SupportedEngines': ['neural']},
{'Gender': 'Female', 'Id': 'Ruth', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Ruth',
'SupportedEngines': ['neural']},
{'Gender': 'Male', 'Id': 'Stephen', 'LanguageCode': 'en-US', 'LanguageName': 'US English', 'Name': 'Stephen',
'SupportedEngines': ['neural']}]
NEURAL_EN_GB_VOICES = [
{'Gender': 'Male', 'Id': 'Arthur', 'LanguageCode': 'en-GB', 'LanguageName': 'British English', 'Name': 'Arthur',
'SupportedEngines': ['neural']}]
NEURAL_EN_AU_VOICES = [
{'Gender': 'Female', 'Id': 'Olivia', 'LanguageCode': 'en-AU', 'LanguageName': 'Australian English',
'Name': 'Olivia', 'SupportedEngines': ['neural']}]
NEURAL_EN_ZA_VOICES = [
{'Gender': 'Female', 'Id': 'Ayanda', 'LanguageCode': 'en-ZA', 'LanguageName': 'South African English',
'Name': 'Ayanda', 'SupportedEngines': ['neural']}]
NEURAL_EN_NZ_VOICES = [
{'Gender': 'Female', 'Id': 'Aria', 'LanguageCode': 'en-NZ', 'LanguageName': 'New Zealand English', 'Name': 'Aria',
'SupportedEngines': ['neural']}]
NEURAL_EN_IN_VOICES = [
{'Gender': 'Female', 'Id': 'Kajal', 'LanguageCode': 'en-IN', 'LanguageName': 'Indian English', 'Name': 'Kajal',
'SupportedEngines': ['neural']}]
NEURAL_EN_IE_VOICES = [
{'Gender': 'Female', 'Id': 'Niamh', 'LanguageCode': 'en-IE', 'LanguageName': 'Irish English', 'Name': 'Niamh',
'SupportedEngines': ['neural']}]
ALL_NEURAL_VOICES = NEURAL_EN_US_VOICES + NEURAL_EN_GB_VOICES + NEURAL_EN_AU_VOICES + NEURAL_EN_ZA_VOICES + NEURAL_EN_NZ_VOICES + NEURAL_EN_IE_VOICES
MALE_NEURAL_VOICES = [item for item in ALL_NEURAL_VOICES if item.get('Gender') == 'Male']
FEMALE_NEURAL_VOICES = [item for item in ALL_NEURAL_VOICES if item.get('Gender') == 'Female']
class EducationalContent:
DIFFICULTIES = ["A1", "A2", "B1", "B2", "C1", "C2"]
MTI_TOPICS = [
"Education",
"Technology",
"Environment",
"Health and Fitness",
"Engineering",
"Work and Careers",
"Travel and Tourism",
"Culture and Traditions",
"Social Issues",
"Arts and Entertainment",
"Climate Change",
"Social Media",
"Sustainable Development",
"Health Care",
"Immigration",
"Artificial Intelligence",
"Consumerism",
"Online Shopping",
"Energy",
"Oil and Gas",
"Poverty and Inequality",
"Cultural Diversity",
"Democracy and Governance",
"Mental Health",
"Ethics and Morality",
"Population Growth",
"Science and Innovation",
"Poverty Alleviation",
"Cybersecurity and Privacy",
"Human Rights",
"Food and Agriculture",
"Cyberbullying and Online Safety",
"Linguistic Diversity",
"Urbanization",
"Artificial Intelligence in Education",
"Youth Empowerment",
"Disaster Management",
"Mental Health Stigma",
"Internet Censorship",
"Sustainable Fashion",
"Indigenous Rights",
"Water Scarcity",
"Social Entrepreneurship",
"Privacy in the Digital Age",
"Sustainable Transportation",
"Gender Equality",
"Automation and Job Displacement",
"Digital Divide",
"Education Inequality"
]
TOPICS = [
"Art and Creativity",
"History of Ancient Civilizations",
"Environmental Conservation",
"Space Exploration",
"Artificial Intelligence",
"Climate Change",
"The Human Brain",
"Renewable Energy",
"Cultural Diversity",
"Modern Technology Trends",
"Sustainable Agriculture",
"Natural Disasters",
"Cybersecurity",
"Philosophy of Ethics",
"Robotics",
"Health and Wellness",
"Literature and Classics",
"World Geography",
"Social Media Impact",
"Food Sustainability",
"Economics and Markets",
"Human Evolution",
"Political Systems",
"Mental Health Awareness",
"Quantum Physics",
"Biodiversity",
"Education Reform",
"Animal Rights",
"The Industrial Revolution",
"Future of Work",
"Film and Cinema",
"Genetic Engineering",
"Climate Policy",
"Space Travel",
"Renewable Energy Sources",
"Cultural Heritage Preservation",
"Modern Art Movements",
"Sustainable Transportation",
"The History of Medicine",
"Artificial Neural Networks",
"Climate Adaptation",
"Philosophy of Existence",
"Augmented Reality",
"Yoga and Meditation",
"Literary Genres",
"World Oceans",
"Social Networking",
"Sustainable Fashion",
"Prehistoric Era",
"Democracy and Governance",
"Postcolonial Literature",
"Geopolitics",
"Psychology and Behavior",
"Nanotechnology",
"Endangered Species",
"Education Technology",
"Renaissance Art",
"Renewable Energy Policy",
"Modern Architecture",
"Climate Resilience",
"Artificial Life",
"Fitness and Nutrition",
"Classic Literature Adaptations",
"Ethical Dilemmas",
"Internet of Things (IoT)",
"Meditation Practices",
"Literary Symbolism",
"Marine Conservation",
"Sustainable Tourism",
"Ancient Philosophy",
"Cold War Era",
"Behavioral Economics",
"Space Colonization",
"Clean Energy Initiatives",
"Cultural Exchange",
"Modern Sculpture",
"Climate Mitigation",
"Mindfulness",
"Literary Criticism",
"Wildlife Conservation",
"Renewable Energy Innovations",
"History of Mathematics",
"Human-Computer Interaction",
"Global Health",
"Cultural Appropriation",
"Traditional cuisine and culinary arts",
"Local music and dance traditions",
"History of the region and historical landmarks",
"Traditional crafts and artisanal skills",
"Wildlife and conservation efforts",
"Local sports and athletic competitions",
"Fashion trends and clothing styles",
"Education systems and advancements",
"Healthcare services and medical innovations",
"Family values and social dynamics",
"Travel destinations and tourist attractions",
"Environmental sustainability projects",
"Technological developments and innovations",
"Entrepreneurship and business ventures",
"Youth empowerment initiatives",
"Art exhibitions and cultural events",
"Philanthropy and community development projects"
]
TWO_PEOPLE_SCENARIOS = [
"Booking a table at a restaurant",
"Making a doctor's appointment",
"Asking for directions to a tourist attraction",
"Inquiring about public transportation options",
"Discussing weekend plans with a friend",
"Ordering food at a café",
"Renting a bicycle for a day",
"Arranging a meeting with a colleague",
"Talking to a real estate agent about renting an apartment",
"Discussing travel plans for an upcoming vacation",
"Checking the availability of a hotel room",
"Talking to a car rental service",
"Asking for recommendations at a library",
"Inquiring about opening hours at a museum",
"Discussing the weather forecast",
"Shopping for groceries",
"Renting a movie from a video store",
"Booking a flight ticket",
"Discussing a school assignment with a classmate",
"Making a reservation for a spa appointment",
"Talking to a customer service representative about a product issue",
"Discussing household chores with a family member",
"Planning a surprise party for a friend",
"Talking to a coworker about a project deadline",
"Inquiring about a gym membership",
"Discussing the menu options at a fast-food restaurant",
"Talking to a neighbor about a community event",
"Asking for help with computer problems",
"Discussing a recent sports game with a sports enthusiast",
"Talking to a pet store employee about buying a pet",
"Asking for information about a local farmer's market",
"Discussing the details of a home renovation project",
"Talking to a coworker about office supplies",
"Making plans for a family picnic",
"Inquiring about admission requirements at a university",
"Discussing the features of a new smartphone with a salesperson",
"Talking to a mechanic about car repairs",
"Making arrangements for a child's birthday party",
"Discussing a new diet plan with a nutritionist",
"Asking for information about a music concert",
"Talking to a hairdresser about getting a haircut",
"Inquiring about a language course at a language school",
"Discussing plans for a weekend camping trip",
"Talking to a bank teller about opening a new account",
"Ordering a drink at a coffee shop",
"Discussing a new book with a book club member",
"Talking to a librarian about library services",
"Asking for advice on finding a job",
"Discussing plans for a garden makeover with a landscaper",
"Talking to a travel agent about a cruise vacation",
"Inquiring about a fitness class at a gym",
"Ordering flowers for a special occasion",
"Discussing a new exercise routine with a personal trainer",
"Talking to a teacher about a child's progress in school",
"Asking for information about a local art exhibition",
"Discussing a home improvement project with a contractor",
"Talking to a babysitter about childcare arrangements",
"Making arrangements for a car service appointment",
"Inquiring about a photography workshop at a studio",
"Discussing plans for a family reunion with a relative",
"Talking to a tech support representative about computer issues",
"Asking for recommendations on pet grooming services",
"Discussing weekend plans with a significant other",
"Talking to a counselor about personal issues",
"Inquiring about a music lesson with a music teacher",
"Ordering a pizza for delivery",
"Making a reservation for a taxi",
"Discussing a new recipe with a chef",
"Talking to a fitness trainer about weight loss goals",
"Inquiring about a dance class at a dance studio",
"Ordering a meal at a food truck",
"Discussing plans for a weekend getaway with a partner",
"Talking to a florist about wedding flower arrangements",
"Asking for advice on home decorating",
"Discussing plans for a charity fundraiser event",
"Talking to a pet sitter about taking care of pets",
"Making arrangements for a spa day with a friend",
"Asking for recommendations on home improvement stores",
"Discussing weekend plans with a travel enthusiast",
"Talking to a car mechanic about car maintenance",
"Inquiring about a cooking class at a culinary school",
"Ordering a sandwich at a deli",
"Discussing plans for a family holiday party",
"Talking to a personal assistant about organizing tasks",
"Asking for information about a local theater production",
"Discussing a new DIY project with a home improvement expert",
"Talking to a wine expert about wine pairing",
"Making arrangements for a pet adoption",
"Asking for advice on planning a wedding"
]
SOCIAL_MONOLOGUE_CONTEXTS = [
"A guided tour of a historical museum",
"An introduction to a new city for tourists",
"An orientation session for new university students",
"A safety briefing for airline passengers",
"An explanation of the process of recycling",
"A lecture on the benefits of a healthy diet",
"A talk on the importance of time management",
"A monologue about wildlife conservation",
"An overview of local public transportation options",
"A presentation on the history of cinema",
"An introduction to the art of photography",
"A discussion about the effects of climate change",
"An overview of different types of cuisine",
"A lecture on the principles of financial planning",
"A monologue about sustainable energy sources",
"An explanation of the process of online shopping",
"A guided tour of a botanical garden",
"An introduction to a local wildlife sanctuary",
"A safety briefing for hikers in a national park",
"A talk on the benefits of physical exercise",
"A lecture on the principles of effective communication",
"A monologue about the impact of social media",
"An overview of the history of a famous landmark",
"An introduction to the world of fashion design",
"A discussion about the challenges of global poverty",
"An explanation of the process of organic farming",
"A presentation on the history of space exploration",
"An overview of traditional music from different cultures",
"A lecture on the principles of effective leadership",
"A monologue about the influence of technology",
"A guided tour of a famous archaeological site",
"An introduction to a local wildlife rehabilitation center",
"A safety briefing for visitors to a science museum",
"A talk on the benefits of learning a new language",
"A lecture on the principles of architectural design",
"A monologue about the impact of renewable energy",
"An explanation of the process of online banking",
"A presentation on the history of a famous art movement",
"An overview of traditional clothing from various regions",
"A lecture on the principles of sustainable agriculture",
"A discussion about the challenges of urban development",
"A monologue about the influence of social norms",
"A guided tour of a historical battlefield",
"An introduction to a local animal shelter",
"A safety briefing for participants in a charity run",
"A talk on the benefits of community involvement",
"A lecture on the principles of sustainable tourism",
"A monologue about the impact of alternative medicine",
"An explanation of the process of wildlife tracking",
"A presentation on the history of a famous inventor",
"An overview of traditional dance forms from different cultures",
"A lecture on the principles of ethical business practices",
"A discussion about the challenges of healthcare access",
"A monologue about the influence of cultural traditions",
"A guided tour of a famous lighthouse",
"An introduction to a local astronomy observatory",
"A safety briefing for participants in a team-building event",
"A talk on the benefits of volunteering",
"A lecture on the principles of wildlife protection",
"A monologue about the impact of space exploration",
"An explanation of the process of wildlife photography",
"A presentation on the history of a famous musician",
"An overview of traditional art forms from different cultures",
"A lecture on the principles of effective education",
"A discussion about the challenges of sustainable development",
"A monologue about the influence of cultural diversity",
"A guided tour of a famous national park",
"An introduction to a local marine conservation project",
"A safety briefing for participants in a hot air balloon ride",
"A talk on the benefits of cultural exchange programs",
"A lecture on the principles of wildlife conservation",
"A monologue about the impact of technological advancements",
"An explanation of the process of wildlife rehabilitation",
"A presentation on the history of a famous explorer",
"A lecture on the principles of effective marketing",
"A discussion about the challenges of environmental sustainability",
"A monologue about the influence of social entrepreneurship",
"A guided tour of a famous historical estate",
"An introduction to a local marine life research center",
"A safety briefing for participants in a zip-lining adventure",
"A talk on the benefits of cultural preservation",
"A lecture on the principles of wildlife ecology",
"A monologue about the impact of space technology",
"An explanation of the process of wildlife conservation",
"A presentation on the history of a famous scientist",
"An overview of traditional crafts and artisans from different cultures",
"A lecture on the principles of effective intercultural communication"
]
FOUR_PEOPLE_SCENARIOS = [
"A university lecture on history",
"A physics class discussing Newton's laws",
"A medical school seminar on anatomy",
"A training session on computer programming",
"A business school lecture on marketing strategies",
"A chemistry lab experiment and discussion",
"A language class practicing conversational skills",
"A workshop on creative writing techniques",
"A high school math lesson on calculus",
"A training program for customer service representatives",
"A lecture on environmental science and sustainability",
"A psychology class exploring human behavior",
"A music theory class analyzing compositions",
"A nursing school simulation for patient care",
"A computer science class on algorithms",
"A workshop on graphic design principles",
"A law school lecture on constitutional law",
"A geology class studying rock formations",
"A vocational training program for electricians",
"A history seminar focusing on ancient civilizations",
"A biology class dissecting specimens",
"A financial literacy course for adults",
"A literature class discussing classic novels",
"A training session for emergency response teams",
"A sociology lecture on social inequality",
"An art class exploring different painting techniques",
"A medical school seminar on diagnosis",
"A programming bootcamp teaching web development",
"An economics class analyzing market trends",
"A chemistry lab experiment on chemical reactions",
"A language class practicing pronunciation",
"A workshop on public speaking skills",
"A high school physics lesson on electromagnetism",
"A training program for IT professionals",
"A lecture on climate change and its effects",
"A psychology class studying cognitive psychology",
"A music class composing original songs",
"A nursing school simulation for patient assessment",
"A computer science class on data structures",
"A workshop on 3D modeling and animation",
"A law school lecture on contract law",
"A geography class examining world maps",
"A vocational training program for plumbers",
"A history seminar discussing revolutions",
"A biology class exploring genetics",
"A financial literacy course for teens",
"A literature class analyzing poetry",
"A training session for public speaking coaches",
"A sociology lecture on cultural diversity",
"An art class creating sculptures",
"A medical school seminar on surgical techniques",
"A programming bootcamp teaching app development",
"An economics class on global trade policies",
"A chemistry lab experiment on chemical bonding",
"A language class discussing idiomatic expressions",
"A workshop on conflict resolution",
"A high school biology lesson on evolution",
"A training program for project managers",
"A lecture on renewable energy sources",
"A psychology class on abnormal psychology",
"A music class rehearsing for a performance",
"A nursing school simulation for emergency response",
"A computer science class on cybersecurity",
"A workshop on digital marketing strategies",
"A law school lecture on intellectual property",
"A geology class analyzing seismic activity",
"A vocational training program for carpenters",
"A history seminar on the Renaissance",
"A chemistry class synthesizing compounds",
"A financial literacy course for seniors",
"A literature class interpreting Shakespearean plays",
"A training session for negotiation skills",
"A sociology lecture on urbanization",
"An art class creating digital art",
"A medical school seminar on patient communication",
"A programming bootcamp teaching mobile app development",
"An economics class on fiscal policy",
"A physics lab experiment on electromagnetism",
"A language class on cultural immersion",
"A workshop on time management",
"A high school chemistry lesson on stoichiometry",
"A training program for HR professionals",
"A lecture on space exploration and astronomy",
"A psychology class on human development",
"A music class practicing for a recital",
"A nursing school simulation for triage",
"A computer science class on web development frameworks",
"A workshop on team-building exercises",
"A law school lecture on criminal law",
"A geography class studying world cultures",
"A vocational training program for HVAC technicians",
"A history seminar on ancient civilizations",
"A biology class examining ecosystems",
"A financial literacy course for entrepreneurs",
"A literature class analyzing modern literature",
"A training session for leadership skills",
"A sociology lecture on gender studies",
"An art class exploring multimedia art",
"A medical school seminar on patient diagnosis",
"A programming bootcamp teaching software architecture"
]
ACADEMIC_SUBJECTS = [
"Astrophysics",
"Microbiology",
"Political Science",
"Environmental Science",
"Literature",
"Biochemistry",
"Sociology",
"Art History",
"Geology",
"Economics",
"Psychology",
"History of Architecture",
"Linguistics",
"Neurobiology",
"Anthropology",
"Quantum Mechanics",
"Urban Planning",
"Philosophy",
"Marine Biology",
"International Relations",
"Medieval History",
"Geophysics",
"Finance",
"Educational Psychology",
"Graphic Design",
"Paleontology",
"Macroeconomics",
"Cognitive Psychology",
"Renaissance Art",
"Archaeology",
"Microeconomics",
"Social Psychology",
"Contemporary Art",
"Meteorology",
"Political Philosophy",
"Space Exploration",
"Cognitive Science",
"Classical Music",
"Oceanography",
"Public Health",
"Gender Studies",
"Baroque Art",
"Volcanology",
"Business Ethics",
"Music Composition",
"Environmental Policy",
"Media Studies",
"Ancient History",
"Seismology",
"Marketing",
"Human Development",
"Modern Art",
"Astronomy",
"International Law",
"Developmental Psychology",
"Film Studies",
"American History",
"Soil Science",
"Entrepreneurship",
"Clinical Psychology",
"Contemporary Dance",
"Space Physics",
"Political Economy",
"Cognitive Neuroscience",
"20th Century Literature",
"Public Administration",
"European History",
"Atmospheric Science",
"Supply Chain Management",
"Social Work",
"Japanese Literature",
"Planetary Science",
"Labor Economics",
"Industrial-Organizational Psychology",
"French Philosophy",
"Biogeochemistry",
"Strategic Management",
"Educational Sociology",
"Postmodern Literature",
"Public Relations",
"Middle Eastern History",
"Oceanography",
"International Development",
"Human Resources Management",
"Educational Leadership",
"Russian Literature",
"Quantum Chemistry",
"Environmental Economics",
"Environmental Psychology",
"Ancient Philosophy",
"Immunology",
"Comparative Politics",
"Child Development",
"Fashion Design",
"Geological Engineering",
"Macroeconomic Policy",
"Media Psychology",
"Byzantine Art",
"Ecology",
"International Business"
]

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ielts_be/configs/dependency_injection.py Normal file
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import json
import os
from dependency_injector import providers, containers
from firebase_admin import credentials
from motor.motor_asyncio import AsyncIOMotorClient
from openai import AsyncOpenAI
from httpx import AsyncClient as HTTPClient
from dotenv import load_dotenv
from sentence_transformers import SentenceTransformer
from ielts_be.repositories.impl import *
from ielts_be.services.impl import *
from ielts_be.controllers.impl import *
load_dotenv()
class DependencyInjector:
def __init__(self, polly_client: any, http_client: HTTPClient, stt: OpenAIWhisper):
self._container = containers.DynamicContainer()
self._polly_client = polly_client
self._http_client = http_client
self._stt = stt
def inject(self):
self._setup_clients()
self._setup_third_parties()
self._setup_repositories()
self._setup_services()
self._setup_controllers()
self._container.wire(
packages=["ielts_be"]
)
return self
def _setup_clients(self):
self._container.openai_client = providers.Singleton(AsyncOpenAI)
self._container.polly_client = providers.Object(self._polly_client)
self._container.http_client = providers.Object(self._http_client)
self._container.stt = providers.Object(self._stt)
def _setup_third_parties(self):
self._container.llm = providers.Factory(OpenAI, client=self._container.openai_client)
self._container.tts = providers.Factory(AWSPolly, client=self._container.polly_client)
"""
with open('ielts_be/services/impl/third_parties/elai/conf.json', 'r') as file:
elai_conf = json.load(file)
with open('ielts_be/services/impl/third_parties/elai/avatars.json', 'r') as file:
elai_avatars = json.load(file)
"""
with open('ielts_be/services/impl/third_parties/heygen/avatars.json', 'r') as file:
heygen_avatars = json.load(file)
self._container.vid_gen = providers.Factory(
Heygen, client=self._container.http_client, token=os.getenv("HEY_GEN_TOKEN"), avatars=heygen_avatars
)
self._container.ai_detector = providers.Factory(
GPTZero, client=self._container.http_client, gpt_zero_key=os.getenv("GPT_ZERO_API_KEY")
)
def _setup_repositories(self):
cred = credentials.Certificate(os.getenv("GOOGLE_APPLICATION_CREDENTIALS"))
firebase_token = cred.get_access_token().access_token
self._container.document_store = providers.Factory(
MongoDB, mongo_db=AsyncIOMotorClient(os.getenv("MONGODB_URI"))[os.getenv("MONGODB_DB")]
)
self._container.firebase_instance = providers.Factory(
FirebaseStorage,
client=self._container.http_client, token=firebase_token, bucket=os.getenv("FIREBASE_BUCKET")
)
def _setup_services(self):
self._container.listening_service = providers.Factory(
ListeningService,
llm=self._container.llm,
stt=self._container.stt,
tts=self._container.tts,
file_storage=self._container.firebase_instance,
document_store=self._container.document_store
)
self._container.reading_service = providers.Factory(ReadingService, llm=self._container.llm)
self._container.speaking_service = providers.Factory(
SpeakingService, llm=self._container.llm,
file_storage=self._container.firebase_instance,
stt=self._container.stt
)
self._container.writing_service = providers.Factory(
WritingService, llm=self._container.llm, ai_detector=self._container.ai_detector, file_storage=self._container.firebase_instance
)
with open('ielts_be/services/impl/exam/level/mc_variants.json', 'r') as file:
mc_variants = json.load(file)
self._container.level_service = providers.Factory(
LevelService, llm=self._container.llm, document_store=self._container.document_store,
mc_variants=mc_variants, reading_service=self._container.reading_service,
writing_service=self._container.writing_service, speaking_service=self._container.speaking_service,
listening_service=self._container.listening_service
)
self._container.grade_service = providers.Factory(
GradeService, llm=self._container.llm
)
embeddings = SentenceTransformer('all-MiniLM-L6-v2')
self._container.training_kb = providers.Factory(
TrainingContentKnowledgeBase, embeddings=embeddings
)
self._container.training_service = providers.Factory(
TrainingService, llm=self._container.llm,
document_store=self._container.document_store, training_kb=self._container.training_kb
)
self._container.user_service = providers.Factory(
UserService, document_store=self._container.document_store
)
self._container.evaluation_service = providers.Factory(
EvaluationService, db=self._container.document_store,
writing_service=self._container.writing_service,
speaking_service=self._container.speaking_service
)
def _setup_controllers(self):
self._container.grade_controller = providers.Factory(
GradeController, grade_service=self._container.grade_service,
evaluation_service=self._container.evaluation_service
)
self._container.user_controller = providers.Factory(
UserController, user_service=self._container.user_service
)
self._container.training_controller = providers.Factory(
TrainingController, training_service=self._container.training_service
)
self._container.level_controller = providers.Factory(
LevelController, level_service=self._container.level_service
)
self._container.listening_controller = providers.Factory(
ListeningController, listening_service=self._container.listening_service
)
self._container.reading_controller = providers.Factory(
ReadingController, reading_service=self._container.reading_service
)
self._container.speaking_controller = providers.Factory(
SpeakingController, speaking_service=self._container.speaking_service, vid_gen=self._container.vid_gen
)
self._container.writing_controller = providers.Factory(
WritingController, writing_service=self._container.writing_service
)

7
ielts_be/configs/logging/__init__.py Normal file
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from .filters import ErrorAndAboveFilter
from .queue_handler import QueueListenerHandler
__all__ = [
"ErrorAndAboveFilter",
"QueueListenerHandler"
]

6
ielts_be/configs/logging/filters.py Normal file
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import logging
class ErrorAndAboveFilter(logging.Filter):
def filter(self, record: logging.LogRecord) -> bool | logging.LogRecord:
return record.levelno < logging.ERROR

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import datetime as dt
import json
import logging
LOG_RECORD_BUILTIN_ATTRS = {
"args",
"asctime",
"created",
"exc_info",
"exc_text",
"filename",
"funcName",
"levelname",
"levelno",
"lineno",
"module",
"msecs",
"message",
"msg",
"name",
"pathname",
"process",
"processName",
"relativeCreated",
"stack_info",
"thread",
"threadName",
"taskName",
}
"""
This isn't being used since the app will be run on gcloud run but this can be used for future apps.
If you want to test it:
formatters:
"json": {
"()": "json_formatter.JSONFormatter",
"fmt_keys": {
"level": "levelname",
"message": "message",
"timestamp": "timestamp",
"logger": "name",
"module": "module",
"function": "funcName",
"line": "lineno",
"thread_name": "threadName"
}
}
handlers:
"file_json": {
"class": "logging.handlers.RotatingFileHandler",
"level": "DEBUG",
"formatter": "json",
"filename": "logs/log",
"maxBytes": 1000000,
"backupCount": 3
}
and add "cfg://handlers.file_json" to queue handler
"""
# From this video https://www.youtube.com/watch?v=9L77QExPmI0
# Src here: https://github.com/mCodingLLC/VideosSampleCode/blob/master/videos/135_modern_logging/mylogger.py
class JSONFormatter(logging.Formatter):
def __init__(
self,
*,
fmt_keys: dict[str, str] | None = None,
):
super().__init__()
self.fmt_keys = fmt_keys if fmt_keys is not None else {}
def format(self, record: logging.LogRecord) -> str:
message = self._prepare_log_dict(record)
return json.dumps(message, default=str)
def _prepare_log_dict(self, record: logging.LogRecord):
always_fields = {
"message": record.getMessage(),
"timestamp": dt.datetime.fromtimestamp(
record.created, tz=dt.timezone.utc
).isoformat(),
}
if record.exc_info is not None:
always_fields["exc_info"] = self.formatException(record.exc_info)
if record.stack_info is not None:
always_fields["stack_info"] = self.formatStack(record.stack_info)
message = {
key: msg_val
if (msg_val := always_fields.pop(val, None)) is not None
else getattr(record, val)
for key, val in self.fmt_keys.items()
}
message.update(always_fields)
for key, val in record.__dict__.items():
if key not in LOG_RECORD_BUILTIN_ATTRS:
message[key] = val
return message

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ielts_be/configs/logging/logging_config.json Normal file
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{
"version": 1,
"objects": {
"queue": {
"class": "queue.Queue",
"maxsize": 1000
}
},
"disable_existing_loggers": false,
"formatters": {
"simple": {
"format": "[%(levelname)s] (%(module)s|L: %(lineno)d) %(asctime)s: %(message)s",
"datefmt": "%Y-%m-%dT%H:%M:%S%z"
}
},
"filters": {
"error_and_above": {
"()": "ielts_be.configs.logging.ErrorAndAboveFilter"
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"level": "INFO",
"formatter": "simple",
"stream": "ext://sys.stdout",
"filters": ["error_and_above"]
},
"error": {
"class": "logging.StreamHandler",
"level": "ERROR",
"formatter": "simple",
"stream": "ext://sys.stderr"
},
"queue_handler": {
"class": "ielts_be.configs.logging.QueueListenerHandler",
"handlers": [
"cfg://handlers.console",
"cfg://handlers.error"
],
"queue": "cfg://objects.queue",
"respect_handler_level": true
}
},
"loggers": {
"root": {
"level": "DEBUG",
"handlers": [
"queue_handler"
]
}
}
}

61
ielts_be/configs/logging/queue_handler.py Normal file
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from logging.config import ConvertingList, ConvertingDict, valid_ident
from logging.handlers import QueueHandler, QueueListener
from queue import Queue
import atexit
class QueueHandlerHelper:
@staticmethod
def resolve_handlers(l):
if not isinstance(l, ConvertingList):
return l
# Indexing the list performs the evaluation.
return [l[i] for i in range(len(l))]
@staticmethod
def resolve_queue(q):
if not isinstance(q, ConvertingDict):
return q
if '__resolved_value__' in q:
return q['__resolved_value__']
cname = q.pop('class')
klass = q.configurator.resolve(cname)
props = q.pop('.', None)
kwargs = {k: q[k] for k in q if valid_ident(k)}
result = klass(**kwargs)
if props:
for name, value in props.items():
setattr(result, name, value)
q['__resolved_value__'] = result
return result
# The guy from this video https://www.youtube.com/watch?v=9L77QExPmI0 is using logging features only available in 3.12
# This article had the class required to build the queue handler in 3.11
# https://rob-blackbourn.medium.com/how-to-use-python-logging-queuehandler-with-dictconfig-1e8b1284e27a
class QueueListenerHandler(QueueHandler):
def __init__(self, handlers, respect_handler_level=False, auto_run=True, queue=Queue(-1)):
queue = QueueHandlerHelper.resolve_queue(queue)
super().__init__(queue)
handlers = QueueHandlerHelper.resolve_handlers(handlers)
self._listener = QueueListener(
self.queue,
*handlers,
respect_handler_level=respect_handler_level)
if auto_run:
self.start()
atexit.register(self.stop)
def start(self):
self._listener.start()
def stop(self):
self._listener.stop()
def emit(self, record):
return super().emit(record)

2512
helper/question_templates.py → ielts_be/configs/question_templates.py
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3
ielts_be/controllers/__init__.py Normal file
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from .abc import *
__all__ = abc.__all__

11
ielts_be/controllers/abc/__init__.py Normal file
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from .grade import IGradeController
from .training import ITrainingController
from .user import IUserController
from .exam import *
__all__ = [
"IGradeController",
"ITrainingController",
"IUserController",
]
__all__.extend(exam.__all__)

13
ielts_be/controllers/abc/exam/__init__.py Normal file
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from .level import ILevelController
from .listening import IListeningController
from .reading import IReadingController
from .writing import IWritingController
from .speaking import ISpeakingController
__all__ = [
"IListeningController",
"IReadingController",
"IWritingController",
"ISpeakingController",
"ILevelController",
]

27
ielts_be/controllers/abc/exam/level.py Normal file
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from abc import ABC, abstractmethod
from fastapi import UploadFile
from typing import Dict, Optional
class ILevelController(ABC):
@abstractmethod
async def generate_exercises(self, dto):
pass
@abstractmethod
async def get_level_exam(self):
pass
@abstractmethod
async def get_level_utas(self):
pass
@abstractmethod
async def upload_level(self, file: UploadFile, solutions: Optional[UploadFile] = None):
pass
@abstractmethod
async def get_custom_level(self, data: Dict):
pass

26
ielts_be/controllers/abc/exam/listening.py Normal file
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from abc import ABC, abstractmethod
from fastapi import UploadFile
class IListeningController(ABC):
@abstractmethod
async def import_exam(self, exercises: UploadFile, solutions: UploadFile = None):
pass
@abstractmethod
async def generate_listening_dialog(self, section_id: int, topic: str, difficulty: str):
pass
@abstractmethod
async def get_listening_question(self, dto):
pass
@abstractmethod
async def generate_mp3(self, dto):
pass
@abstractmethod
async def transcribe_dialog(self, audio: UploadFile):
pass

20
ielts_be/controllers/abc/exam/reading.py Normal file
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from abc import ABC, abstractmethod
from typing import Optional
from fastapi import UploadFile
class IReadingController(ABC):
@abstractmethod
async def import_exam(self, exercises: UploadFile, solutions: UploadFile = None):
pass
@abstractmethod
async def generate_reading_passage(self, passage: int, topic: Optional[str], word_count: Optional[int]):
pass
@abstractmethod
async def generate_reading_exercises(self, dto):
pass

20
ielts_be/controllers/abc/exam/speaking.py Normal file
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from abc import ABC, abstractmethod
class ISpeakingController(ABC):
@abstractmethod
async def get_speaking_part(self, task: int, topic: str, second_topic: str, difficulty: str):
pass
@abstractmethod
async def get_avatars(self):
pass
@abstractmethod
async def generate_video(self, text: str, avatar: str):
pass
@abstractmethod
async def poll_video(self, vid_id: str):
pass

14
ielts_be/controllers/abc/exam/writing.py Normal file
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from abc import ABC, abstractmethod
from fastapi.datastructures import UploadFile
class IWritingController(ABC):
@abstractmethod
async def get_writing_task_general_question(self, task: int, topic: str, difficulty: str):
pass
@abstractmethod
async def get_writing_task_academic_question(self, task: int, attachment: UploadFile, difficulty: str):
pass

30
ielts_be/controllers/abc/grade.py Normal file
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from abc import ABC, abstractmethod
from typing import Dict
from fastapi import BackgroundTasks
from fastapi.datastructures import FormData
class IGradeController(ABC):
@abstractmethod
async def grade_writing_task(
self,
task: int, dto: any,
background_tasks: BackgroundTasks
):
pass
@abstractmethod
async def grade_speaking_task(
self, task: int, form: FormData, background_tasks: BackgroundTasks
):
pass
@abstractmethod
async def grade_short_answers(self, data: Dict):
pass
@abstractmethod
async def grading_summary(self, data: Dict):
pass

12
ielts_be/controllers/abc/training.py Normal file
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from abc import ABC, abstractmethod
class ITrainingController(ABC):
@abstractmethod
async def fetch_tips(self, data):
pass
@abstractmethod
async def get_training_content(self, data):
pass

8
ielts_be/controllers/abc/user.py Normal file
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from abc import ABC, abstractmethod
class IUserController(ABC):
@abstractmethod
async def batch_import(self, batch):
pass

12
ielts_be/controllers/impl/__init__.py Normal file
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from .training import TrainingController
from .grade import GradeController
from .user import UserController
from .exam import *
__all__ = [
"TrainingController",
"GradeController",
"UserController"
]
__all__.extend(exam.__all__)

13
ielts_be/controllers/impl/exam/__init__.py Normal file
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from .level import LevelController
from .listening import ListeningController
from .reading import ReadingController
from .speaking import SpeakingController
from .writing import WritingController
__all__ = [
"LevelController",
"ListeningController",
"ReadingController",
"SpeakingController",
"WritingController",
]

26
ielts_be/controllers/impl/exam/level.py Normal file
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from fastapi import UploadFile
from typing import Dict, Optional
from ielts_be.controllers import ILevelController
from ielts_be.services import ILevelService
class LevelController(ILevelController):
def __init__(self, level_service: ILevelService):
self._service = level_service
async def generate_exercises(self, dto):
return await self._service.generate_exercises(dto)
async def get_level_exam(self):
return await self._service.get_level_exam()
async def get_level_utas(self):
return await self._service.get_level_utas()
async def upload_level(self, exercises: UploadFile, solutions: Optional[UploadFile] = None):
return await self._service.upload_level(exercises, solutions)
async def get_custom_level(self, data: Dict):
return await self._service.get_custom_level(data)

46
ielts_be/controllers/impl/exam/listening.py Normal file
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import io
from fastapi import UploadFile
from fastapi.responses import StreamingResponse, Response
from ielts_be.controllers import IListeningController
from ielts_be.services import IListeningService
from ielts_be.dtos.listening import GenerateListeningExercises, Dialog
class ListeningController(IListeningController):
def __init__(self, listening_service: IListeningService):
self._service = listening_service
async def import_exam(self, exercises: UploadFile, solutions: UploadFile = None):
res = await self._service.import_exam(exercises, solutions)
if not res:
return Response(status_code=500)
else:
return res
async def generate_listening_dialog(self, section_id: int, topic: str, difficulty: str):
return await self._service.generate_listening_dialog(section_id, topic, difficulty)
async def get_listening_question(self, dto: GenerateListeningExercises):
return await self._service.get_listening_question(dto)
async def generate_mp3(self, dto: Dialog):
mp3 = await self._service.generate_mp3(dto)
return StreamingResponse(
content=io.BytesIO(mp3),
media_type="audio/mpeg",
headers={
"Content-Type": "audio/mpeg",
"Content-Disposition": "attachment;filename=speech.mp3"
}
)
async def transcribe_dialog(self, audio: UploadFile):
dialog = await self._service.transcribe_dialog(audio)
if dialog is None:
return Response(status_code=500)
return dialog

28
ielts_be/controllers/impl/exam/reading.py Normal file
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import logging
from typing import Optional
from fastapi import UploadFile, Response
from ielts_be.controllers import IReadingController
from ielts_be.services import IReadingService
from ielts_be.dtos.reading import ReadingDTO
class ReadingController(IReadingController):
def __init__(self, reading_service: IReadingService):
self._service = reading_service
self._logger = logging.getLogger(__name__)
async def import_exam(self, exercises: UploadFile, solutions: UploadFile = None):
res = await self._service.import_exam(exercises, solutions)
if not res:
return Response(status_code=500)
else:
return res
async def generate_reading_passage(self, passage: int, topic: Optional[str], word_count: Optional[int]):
return await self._service.generate_reading_passage(passage, topic, word_count)
async def generate_reading_exercises(self, dto: ReadingDTO):
return await self._service.generate_reading_exercises(dto)

24
ielts_be/controllers/impl/exam/speaking.py Normal file
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import logging
from ielts_be.controllers import ISpeakingController
from ielts_be.services import ISpeakingService, IVideoGeneratorService
class SpeakingController(ISpeakingController):
def __init__(self, speaking_service: ISpeakingService, vid_gen: IVideoGeneratorService):
self._service = speaking_service
self._vid_gen = vid_gen
self._logger = logging.getLogger(__name__)
async def get_speaking_part(self, task: int, topic: str, second_topic: str, difficulty: str):
return await self._service.get_speaking_part(task, topic, second_topic, difficulty)
async def get_avatars(self):
return await self._vid_gen.get_avatars()
async def generate_video(self, text: str, avatar: str):
return await self._vid_gen.create_video(text, avatar)
async def poll_video(self, vid_id: str):
return await self._vid_gen.poll_status(vid_id)

18
ielts_be/controllers/impl/exam/writing.py Normal file
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from fastapi import UploadFile, HTTPException
from ielts_be.controllers import IWritingController
from ielts_be.services import IWritingService
class WritingController(IWritingController):
def __init__(self, writing_service: IWritingService):
self._service = writing_service
async def get_writing_task_general_question(self, task: int, topic: str, difficulty: str):
return await self._service.get_writing_task_general_question(task, topic, difficulty)
async def get_writing_task_academic_question(self, task: int, attachment: UploadFile, difficulty: str):
if attachment.content_type not in ['image/jpeg', 'image/png']:
raise HTTPException(status_code=400, detail="Invalid file type. Only JPEG and PNG allowed.")
return await self._service.get_writing_task_academic_question(task, attachment, difficulty)

113
ielts_be/controllers/impl/grade.py Normal file
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import logging
from typing import Dict
from fastapi import BackgroundTasks, Response, HTTPException
from fastapi.datastructures import FormData
from ielts_be.controllers import IGradeController
from ielts_be.services import IGradeService, IEvaluationService
from ielts_be.dtos.evaluation import EvaluationType
from ielts_be.dtos.speaking import GradeSpeakingItem
from ielts_be.dtos.writing import WritingGradeTaskDTO
class GradeController(IGradeController):
def __init__(
self,
grade_service: IGradeService,
evaluation_service: IEvaluationService,
):
self._service = grade_service
self._evaluation_service = evaluation_service
self._logger = logging.getLogger(__name__)
async def grade_writing_task(
self,
task: int, dto: WritingGradeTaskDTO, background_tasks: BackgroundTasks
):
await self._evaluation_service.create_evaluation(
dto.userId, dto.sessionId, dto.exerciseId, EvaluationType.WRITING, task
)
await self._evaluation_service.begin_evaluation(
dto.userId, dto.sessionId, task, dto.exerciseId, EvaluationType.WRITING, dto, background_tasks
)
return Response(status_code=200)
async def grade_speaking_task(self, task: int, form: FormData, background_tasks: BackgroundTasks):
answers: Dict[int, Dict] = {}
user_id = form.get("userId")
session_id = form.get("sessionId")
exercise_id = form.get("exerciseId")
if not session_id or not exercise_id:
raise HTTPException(
status_code=400,
detail="Fields sessionId and exerciseId are required!"
)
for key, value in form.items():
if '_' not in key:
continue
field_name, index = key.rsplit('_', 1)
index = int(index)
if index not in answers:
answers[index] = {}
if field_name == 'question':
answers[index]['question'] = value
elif field_name == 'audio':
answers[index]['answer'] = value
for i, answer in answers.items():
if 'question' not in answer or 'answer' not in answer:
raise HTTPException(
status_code=400,
detail=f"Incomplete data for answer {i}. Both question and audio required."
)
items = [
GradeSpeakingItem(
question=answers[i]['question'],
answer=answers[i]['answer']
)
for i in sorted(answers.keys())
]
ex_type = EvaluationType.SPEAKING if task == 2 else EvaluationType.SPEAKING_INTERACTIVE
await self._evaluation_service.create_evaluation(
user_id, session_id, exercise_id, ex_type, task
)
await self._evaluation_service.begin_evaluation(
user_id, session_id, task, exercise_id, ex_type, items, background_tasks
)
return Response(status_code=200)
async def grade_short_answers(self, data: Dict):
return await self._service.grade_short_answers(data)
async def grading_summary(self, data: Dict):
section_keys = ['reading', 'listening', 'writing', 'speaking', 'level']
extracted_sections = self._extract_existing_sections_from_body(data, section_keys)
return await self._service.calculate_grading_summary(extracted_sections)
@staticmethod
def _extract_existing_sections_from_body(my_dict, keys_to_extract):
if 'sections' in my_dict and isinstance(my_dict['sections'], list) and len(my_dict['sections']) > 0:
return list(
filter(
lambda item:
'code' in item and
item['code'] in keys_to_extract and
'grade' in item and
'name' in item,
my_dict['sections']
)
)

17
ielts_be/controllers/impl/training.py Normal file
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from typing import Dict
from ielts_be.controllers import ITrainingController
from ielts_be.services import ITrainingService
from ielts_be.dtos.training import FetchTipsDTO
class TrainingController(ITrainingController):
def __init__(self, training_service: ITrainingService):
self._service = training_service
async def fetch_tips(self, data: FetchTipsDTO):
return await self._service.fetch_tips(data.context, data.question, data.answer, data.correct_answer)
async def get_training_content(self, data: Dict):
return await self._service.get_training_content(data)

12
ielts_be/controllers/impl/user.py Normal file
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from ielts_be.controllers import IUserController
from ielts_be.services import IUserService
from ielts_be.dtos.user_batch import BatchUsersDTO
class UserController(IUserController):
def __init__(self, user_service: IUserService):
self._service = user_service
async def batch_import(self, batch: BatchUsersDTO):
return await self._service.batch_users(batch)

0
ielts_be/dtos/__init__.py Normal file
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18
ielts_be/dtos/evaluation.py Normal file
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from enum import Enum
from typing import Dict, Optional
from pydantic import BaseModel
class EvaluationType(str, Enum):
WRITING = "writing"
SPEAKING_INTERACTIVE = "speaking_interactive"
SPEAKING = "speaking"
class EvaluationRecord(BaseModel):
id: str
session_id: str
exercise_id: str
type: EvaluationType
task: int
status: str = "pending"
result: Optional[Dict] = None

0
ielts_be/dtos/exams/__init__.py Normal file
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117
modules/upload_level/exam_dtos.py → ielts_be/dtos/exams/level.py
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@@ -1,57 +1,60 @@
from pydantic import BaseModel, Field
from typing import List, Dict, Union, Optional, Any
from uuid import uuid4, UUID
class Option(BaseModel):
id: str
text: str
class MultipleChoiceQuestion(BaseModel):
id: str
prompt: str
variant: str = "text"
solution: str
options: List[Option]
class MultipleChoiceExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str = "multipleChoice"
prompt: str = "Select the appropriate option."
questions: List[MultipleChoiceQuestion]
userSolutions: List = Field(default_factory=list)
class FillBlanksWord(BaseModel):
id: str
options: Dict[str, str]
class FillBlanksSolution(BaseModel):
id: str
solution: str
class FillBlanksExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str = "fillBlanks"
variant: str = "mc"
prompt: str = "Click a blank to select the appropriate word for it."
text: str
solutions: List[FillBlanksSolution]
words: List[FillBlanksWord]
userSolutions: List = Field(default_factory=list)
Exercise = Union[MultipleChoiceExercise, FillBlanksExercise]
class Part(BaseModel):
exercises: List[Exercise]
context: Optional[str] = Field(default=None)
class Exam(BaseModel):
parts: List[Part]
from pydantic import BaseModel, Field
from typing import List, Dict, Union, Optional
from uuid import uuid4, UUID
class Option(BaseModel):
id: str
text: str
class MultipleChoiceQuestion(BaseModel):
id: str
prompt: str
variant: str = "text"
solution: str
options: List[Option]
class MultipleChoiceExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str = "multipleChoice"
prompt: str = "Select the appropriate option."
questions: List[MultipleChoiceQuestion]
userSolutions: List = Field(default_factory=list)
class FillBlanksWord(BaseModel):
id: str
options: Dict[str, str]
class FillBlanksSolution(BaseModel):
id: str
solution: str
class FillBlanksExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str = "fillBlanks"
variant: str = "mc"
prompt: str = "Click a blank to select the appropriate word for it."
text: str
solutions: List[FillBlanksSolution]
words: List[FillBlanksWord]
userSolutions: List = Field(default_factory=list)
Exercise = Union[MultipleChoiceExercise, FillBlanksExercise]
class Text(BaseModel):
content: str
title: str
class Part(BaseModel):
exercises: List[Exercise]
text: Optional[Text] = Field(default=None)
class Exam(BaseModel):
parts: List[Part]

92
ielts_be/dtos/exams/listening.py Normal file
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from enum import Enum
from pydantic import BaseModel, Field
from typing import List, Union, Optional, Literal, Any
from uuid import uuid4, UUID
from ielts_be.dtos.listening import Dialog
class ExerciseBase(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str
prompt: str
class TrueFalseSolution(str, Enum):
TRUE = "true"
FALSE = "false"
NOT_GIVEN = "not_given"
class TrueFalseQuestions(BaseModel):
prompt: str
solution: TrueFalseSolution
id: str
class TrueFalseExercise(ExerciseBase):
type: Literal["trueFalse"]
questions: List[TrueFalseQuestions]
class MCOption(BaseModel):
id: str
text: str
class MCQuestion(BaseModel):
id: str
prompt: str
options: List[MCOption]
solution: str
variant: str = "text"
class MultipleChoiceExercise(ExerciseBase):
type: Literal["multipleChoice"]
questions: List[MCQuestion]
class WriteBlankQuestion(BaseModel):
id: str
prompt: str
solution: List[str]
class WriteBlanksVariant(str, Enum):
QUESTIONS = "questions"
FILL = "fill"
FORM = "form"
class WriteBlanksQuestionExercise(ExerciseBase):
type: Literal["writeBlanks"]
maxWords: int
questions: List[WriteBlankQuestion]
variant: WriteBlanksVariant
class WriteBlankSolution(BaseModel):
id: str
solution: List[str]
class WriteBlanksExercise(ExerciseBase):
type: Literal["writeBlanks"]
maxWords: int
solutions: List[WriteBlankSolution]
text: str
variant: Optional[WriteBlanksVariant]
ListeningExercise = Union[
TrueFalseExercise,
MultipleChoiceExercise,
WriteBlanksExercise
]
class ListeningSection(BaseModel):
exercises: List[ListeningExercise]
script: Optional[Union[List[Any] | str]] = None
class ListeningExam(BaseModel):
module: str = "listening"
minTimer: Optional[int]
parts: List[ListeningSection]

107
ielts_be/dtos/exams/reading.py Normal file
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from enum import Enum
from pydantic import BaseModel, Field
from typing import List, Union, Optional
from uuid import uuid4, UUID
class WriteBlanksSolution(BaseModel):
id: str
solution: List[str]
class WriteBlanksExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
type: str = "writeBlanks"
maxWords: int
solutions: List[WriteBlanksSolution]
text: str
prompt: str
class MatchSentencesOption(BaseModel):
id: str
sentence: str
class MatchSentencesSentence(MatchSentencesOption):
solution: str
class MatchSentencesVariant(str, Enum):
HEADING = "heading"
IDEAMATCH = "ideaMatch"
class MCOption(BaseModel):
id: str
text: str
class MCQuestion(BaseModel):
id: str
prompt: str
options: List[MCOption]
solution: str
variant: Optional[str] = None
class MultipleChoice(BaseModel):
questions: List[MCQuestion]
type: str
prompt: str
class MatchSentencesExercise(BaseModel):
options: List[MatchSentencesOption]
sentences: List[MatchSentencesSentence]
type: str = "matchSentences"
variant: MatchSentencesVariant
prompt: str
class TrueFalseSolution(str, Enum):
TRUE = "true"
FALSE = "false"
NOT_GIVEN = "not_given"
class TrueFalseQuestions(BaseModel):
prompt: str
solution: TrueFalseSolution
id: str
class TrueFalseExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
questions: List[TrueFalseQuestions]
type: str = "trueFalse"
prompt: str = "Do the following statements agree with the information given in the Reading Passage?"
class FillBlanksSolution(BaseModel):
id: str
solution: str
class FillBlanksWord(BaseModel):
letter: str
word: str
class FillBlanksExercise(BaseModel):
id: UUID = Field(default_factory=uuid4)
solutions: List[FillBlanksSolution]
text: str
type: str = "fillBlanks"
words: List[FillBlanksWord]
allowRepetition: bool = False
prompt: str
Exercise = Union[FillBlanksExercise, TrueFalseExercise, MatchSentencesExercise, WriteBlanksExercise, MultipleChoice]
class Context(BaseModel):
title: str
content: str
class Part(BaseModel):
exercises: List[Exercise]
text: Context
class Exam(BaseModel):
id: UUID = Field(default_factory=uuid4)
module: str = "reading"
minTimer: int
isDiagnostic: bool = False
parts: List[Part]

18
ielts_be/dtos/level.py Normal file
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from typing import List, Optional
from pydantic import BaseModel
from ielts_be.configs.constants import LevelExerciseType
class LevelExercises(BaseModel):
type: LevelExerciseType
quantity: int
text_size: Optional[int] = None
sa_qty: Optional[int] = None
mc_qty: Optional[int] = None
topic: Optional[str] = None
class LevelExercisesDTO(BaseModel):
exercises: List[LevelExercises]
difficulty: Optional[str] = None

34
ielts_be/dtos/listening.py Normal file
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import random
import uuid
from typing import List, Dict, Optional
from pydantic import BaseModel, Field
from ielts_be.configs.constants import MinTimers, EducationalContent, ListeningExerciseType
class SaveListeningDTO(BaseModel):
parts: List[Dict]
minTimer: int = MinTimers.LISTENING_MIN_TIMER_DEFAULT
difficulty: str = random.choice(EducationalContent.DIFFICULTIES)
id: str = str(uuid.uuid4())
class ListeningExercises(BaseModel):
type: ListeningExerciseType
quantity: int
class GenerateListeningExercises(BaseModel):
text: str
exercises: List[ListeningExercises]
difficulty: Optional[str]
class ConversationPayload(BaseModel):
name: str
gender: str
text: str
voice: str
class Dialog(BaseModel):
conversation: Optional[List[ConversationPayload]] = Field(default_factory=list)
monologue: Optional[str] = None

17
ielts_be/dtos/reading.py Normal file
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import random
from typing import List, Optional
from pydantic import BaseModel, Field
from ielts_be.configs.constants import ReadingExerciseType, EducationalContent
class ReadingExercise(BaseModel):
type: ReadingExerciseType
quantity: int
num_random_words: Optional[int] = Field(1)
max_words: Optional[int] = Field(3)
class ReadingDTO(BaseModel):
text: str = Field(...)
exercises: List[ReadingExercise] = Field(...)
difficulty: str = Field(random.choice(EducationalContent.DIFFICULTIES))

58
modules/upload_level/sheet_dtos.py → ielts_be/dtos/sheet.py
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@@ -1,29 +1,29 @@
from pydantic import BaseModel
from typing import List, Dict, Union, Any, Optional
class Option(BaseModel):
id: str
text: str
class MultipleChoiceQuestion(BaseModel):
type: str = "multipleChoice"
id: str
prompt: str
variant: str = "text"
options: List[Option]
class FillBlanksWord(BaseModel):
type: str = "fillBlanks"
id: str
options: Dict[str, str]
Component = Union[MultipleChoiceQuestion, FillBlanksWord, Dict[str, Any]]
class Sheet(BaseModel):
batch: Optional[int] = None
components: List[Component]
from pydantic import BaseModel
from typing import List, Dict, Union, Any, Optional
class Option(BaseModel):
id: str
text: str
class MultipleChoiceQuestion(BaseModel):
type: str = "multipleChoice"
id: str
prompt: str
variant: str = "text"
options: List[Option]
class FillBlanksWord(BaseModel):
type: str = "fillBlanks"
id: str
options: Dict[str, str]
Component = Union[MultipleChoiceQuestion, FillBlanksWord, Dict[str, Any]]
class Sheet(BaseModel):
batch: Optional[int] = None
components: List[Component]

11
ielts_be/dtos/speaking.py Normal file
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from fastapi import UploadFile
from pydantic import BaseModel
class Video(BaseModel):
text: str
avatar: str
class GradeSpeakingItem(BaseModel):
question: str
answer: UploadFile

66
modules/training_content/dtos.py → ielts_be/dtos/training.py
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@@ -1,29 +1,37 @@
from pydantic import BaseModel
from typing import List
class QueryDTO(BaseModel):
category: str
text: str
class DetailsDTO(BaseModel):
exam_id: str
date: int
performance_comment: str
detailed_summary: str
class WeakAreaDTO(BaseModel):
area: str
comment: str
class TrainingContentDTO(BaseModel):
details: List[DetailsDTO]
weak_areas: List[WeakAreaDTO]
queries: List[QueryDTO]
class TipsDTO(BaseModel):
tip_ids: List[str]
from pydantic import BaseModel
from typing import List
class FetchTipsDTO(BaseModel):
context: str
question: str
answer: str
correct_answer: str
class QueryDTO(BaseModel):
category: str
text: str
class DetailsDTO(BaseModel):
exam_id: str
date: int
performance_comment: str
detailed_summary: str
class WeakAreaDTO(BaseModel):
area: str
comment: str
class TrainingContentDTO(BaseModel):
details: List[DetailsDTO]
weak_areas: List[WeakAreaDTO]
queries: List[QueryDTO]
class TipsDTO(BaseModel):
tip_ids: List[str]

35
ielts_be/dtos/user_batch.py Normal file
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import uuid
from typing import Optional
from pydantic import BaseModel, Field
class DemographicInfo(BaseModel):
phone: str
passport_id: Optional[str] = None
country: Optional[str] = None
class Entity(BaseModel):
id: str
role: str
class UserDTO(BaseModel):
id: uuid.UUID = Field(default_factory=uuid.uuid4)
email: str
name: str
type: str
passport_id: str
passwordHash: str
passwordSalt: str
groupName: Optional[str] = None
corporate: Optional[str] = None
studentID: Optional[str] = None
expiryDate: Optional[str] = None
demographicInformation: Optional[DemographicInfo] = None
entities: list[dict] = Field(default_factory=list)
class BatchUsersDTO(BaseModel):
makerID: str
users: list[UserDTO]

14
ielts_be/dtos/video.py Normal file
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from enum import Enum
from typing import Optional
from pydantic import BaseModel
class TaskStatus(Enum):
STARTED = "STARTED"
IN_PROGRESS = "IN_PROGRESS"
COMPLETED = "COMPLETED"
ERROR = "ERROR"
class Task(BaseModel):
status: TaskStatus
result: Optional[str] = None

12
ielts_be/dtos/writing.py Normal file
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from typing import Optional
from pydantic import BaseModel
class WritingGradeTaskDTO(BaseModel):
userId: str
sessionId: str
exerciseId: str
question: str
answer: str
attachment: Optional[str] = None

6
ielts_be/exceptions/__init__.py Normal file
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from .exceptions import CustomException, UnauthorizedException
__all__ = [
"CustomException",
"UnauthorizedException"
]

21
ielts_be/exceptions/exceptions.py Normal file
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from http import HTTPStatus
class CustomException(Exception):
code = HTTPStatus.INTERNAL_SERVER_ERROR
error_code = HTTPStatus.INTERNAL_SERVER_ERROR
message = HTTPStatus.INTERNAL_SERVER_ERROR.description
def __init__(self, message=None):
if message:
self.message = message
class UnauthorizedException(CustomException):
code = HTTPStatus.UNAUTHORIZED
error_code = HTTPStatus.UNAUTHORIZED
message = HTTPStatus.UNAUTHORIZED.description
class TranscriptionException(CustomException):
code = HTTPStatus.INTERNAL_SERVER_ERROR
error_code = HTTPStatus.INTERNAL_SERVER_ERROR

11
ielts_be/helpers/__init__.py Normal file
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from .file import FileHelper
from .text import TextHelper
from .token_counter import count_tokens
from .exercises import ExercisesHelper
__all__ = [
"FileHelper",
"TextHelper",
"count_tokens",
"ExercisesHelper",
]

249
ielts_be/helpers/exercises.py Normal file
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import queue
import random
import re
import string
from wonderwords import RandomWord
from .text import TextHelper
class ExercisesHelper:
@staticmethod
def divide_number_into_parts(number, parts):
if number < parts:
return None
part_size = number // parts
remaining = number % parts
q = queue.Queue()
for i in range(parts):
if i < remaining:
q.put(part_size + 1)
else:
q.put(part_size)
return q
@staticmethod
def fix_exercise_ids(exercise, start_id):
# Initialize the starting ID for the first exercise
current_id = start_id
questions = exercise["questions"]
# Iterate through questions and update the "id" value
for question in questions:
question["id"] = str(current_id)
current_id += 1
return exercise
@staticmethod
def replace_first_occurrences_with_placeholders(text: str, words_to_replace: list, start_id):
for i, word in enumerate(words_to_replace, start=start_id):
# Create a case-insensitive regular expression pattern
pattern = re.compile(r'\b' + re.escape(word) + r'\b', re.IGNORECASE)
placeholder = '{{' + str(i) + '}}'
text = pattern.sub(placeholder, text, 1)
return text
@staticmethod
def replace_first_occurrences_with_placeholders_notes(notes: list, words_to_replace: list, start_id):
replaced_notes = []
for i, note in enumerate(notes, start=0):
word = words_to_replace[i]
pattern = re.compile(r'\b' + re.escape(word) + r'\b', re.IGNORECASE)
placeholder = '{{' + str(start_id + i) + '}}'
note = pattern.sub(placeholder, note, 1)
replaced_notes.append(note)
return replaced_notes
@staticmethod
def add_random_words_and_shuffle(word_array, num_random_words):
r = RandomWord()
random_words_selected = r.random_words(num_random_words)
combined_array = word_array + random_words_selected
random.shuffle(combined_array)
result = []
for i, word in enumerate(combined_array):
letter = chr(65 + i) # chr(65) is 'A'
result.append({"letter": letter, "word": word})
return result
@staticmethod
def fillblanks_build_solutions_array(words, start_id):
solutions = []
for i, word in enumerate(words, start=start_id):
solutions.append(
{
"id": str(i),
"solution": word
}
)
return solutions
@staticmethod
def remove_excess_questions(questions: [], quantity):
count_true = 0
result = []
for item in reversed(questions):
if item.get('solution') == 'true' and count_true < quantity:
count_true += 1
else:
result.append(item)
result.reverse()
return result
@staticmethod
def build_write_blanks_text(questions: [], start_id):
result = ""
for i, q in enumerate(questions, start=start_id):
placeholder = '{{' + str(i) + '}}'
result = result + q["question"] + placeholder + "\\n"
return result
@staticmethod
def build_write_blanks_text_form(form: [], start_id):
result = ""
replaced_words = []
for i, entry in enumerate(form, start=start_id):
placeholder = '{{' + str(i) + '}}'
# Use regular expression to find the string after ':'
match = re.search(r'(?<=:)\s*(.*)', entry)
# Extract the matched string
original_string = match.group(1)
# Split the string into words
words = re.findall(r'\b\w+\b', original_string)
# Remove words with only one letter
filtered_words = [word for word in words if len(word) > 1]
# Choose a random word from the list of words
selected_word = random.choice(filtered_words)
pattern = re.compile(r'\b' + re.escape(selected_word) + r'\b', re.IGNORECASE)
# Replace the chosen word with the placeholder
replaced_string = pattern.sub(placeholder, original_string, 1)
# Construct the final replaced string
replaced_string = entry.replace(original_string, replaced_string)
result = result + replaced_string + "\\n"
# Save the replaced word or use it as needed
# For example, you can save it to a file or a list
replaced_words.append(selected_word)
return result, replaced_words
@staticmethod
def build_write_blanks_solutions(questions: [], start_id):
solutions = []
for i, q in enumerate(questions, start=start_id):
solution = [q["possible_answers"]] if isinstance(q["possible_answers"], str) else q["possible_answers"]
solutions.append(
{
"id": str(i),
"solution": solution
}
)
return solutions
@staticmethod
def build_write_blanks_solutions_listening(words: [], start_id):
solutions = []
for i, word in enumerate(words, start=start_id):
solution = [word] if isinstance(word, str) else word
solutions.append(
{
"id": str(i),
"solution": solution
}
)
return solutions
@staticmethod
def answer_word_limit_ok(question):
# Check if any option in any solution has more than three words
return not any(
len(option.split()) > 3
for solution in question["solutions"]
for option in solution["solution"]
)
@staticmethod
def assign_letters_to_paragraphs(paragraphs):
result = []
letters = iter(string.ascii_uppercase)
for paragraph in paragraphs.split("\n\n"):
if TextHelper.has_x_words(paragraph, 10):
result.append({'paragraph': paragraph.strip(), 'letter': next(letters)})
return result
@staticmethod
def contains_empty_dict(arr):
return any(elem == {} for elem in arr)
@staticmethod
def fix_writing_overall(overall: float, task_response: dict):
grades = [category["grade"] for category in task_response.values()]
if overall > max(grades) or overall < min(grades):
total_sum = sum(grades)
average = total_sum / len(grades)
rounded_average = round(average, 0)
return rounded_average
return overall
@staticmethod
def build_options(ideas):
options = []
letters = iter(string.ascii_uppercase)
for idea in ideas:
options.append({
"id": next(letters),
"sentence": idea["from"]
})
return options
@staticmethod
def build_sentences(ideas, start_id):
sentences = []
letters = iter(string.ascii_uppercase)
for idea in ideas:
sentences.append({
"solution": next(letters),
"sentence": idea["idea"]
})
random.shuffle(sentences)
for i, sentence in enumerate(sentences, start=start_id):
sentence["id"] = i
return sentences
@staticmethod
def randomize_mc_options_order(questions):
option_ids = ['A', 'B', 'C', 'D']
for question in questions:
# Store the original solution text
original_solution_text = next(
option['text'] for option in question['options'] if option['id'] == question['solution'])
# Shuffle the options
random.shuffle(question['options'])
# Update the option ids and find the new solution id
for idx, option in enumerate(question['options']):
option['id'] = option_ids[idx]
if option['text'] == original_solution_text:
question['solution'] = option['id']
return questions

222
modules/helper/file_helper.py → ielts_be/helpers/file.py
View File

@@ -1,97 +1,125 @@
import base64
import io
import os
import shutil
import subprocess
import uuid
from typing import Optional, Tuple
import numpy as np
import pypandoc
from PIL import Image
class FileHelper:
# Supposedly pandoc covers a wide range of file extensions only tested with docx
@staticmethod
def convert_file_to_pdf(input_path: str, output_path: str):
pypandoc.convert_file(input_path, 'pdf', outputfile=output_path, extra_args=[
'-V', 'geometry:paperwidth=5.5in',
'-V', 'geometry:paperheight=8.5in',
'-V', 'geometry:margin=0.5in',
'-V', 'pagestyle=empty'
])
@staticmethod
def convert_file_to_html(input_path: str, output_path: str):
pypandoc.convert_file(input_path, 'html', outputfile=output_path)
@staticmethod
def pdf_to_png(path_id: str):
to_png = f"pdftoppm -png exercises.pdf page"
result = subprocess.run(to_png, shell=True, cwd=f'./tmp/{path_id}', capture_output=True, text=True)
if result.returncode != 0:
raise Exception(
f"Couldn't convert pdf to png. Failed to run command '{to_png}' -> ```cmd {result.stderr}```")
@staticmethod
def is_page_blank(image_bytes: bytes, image_threshold=10) -> bool:
with Image.open(io.BytesIO(image_bytes)) as img:
img_gray = img.convert('L')
img_array = np.array(img_gray)
non_white_pixels = np.sum(img_array < 255)
return non_white_pixels <= image_threshold
@classmethod
def _encode_image(cls, image_path: str, image_threshold=10) -> Optional[str]:
with open(image_path, "rb") as image_file:
image_bytes = image_file.read()
if cls.is_page_blank(image_bytes, image_threshold):
return None
return base64.b64encode(image_bytes).decode('utf-8')
@classmethod
def b64_pngs(cls, path_id: str, files: list[str]):
png_messages = []
for filename in files:
b64_string = cls._encode_image(os.path.join(f'./tmp/{path_id}', filename))
if b64_string:
png_messages.append({
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{b64_string}"
}
})
return png_messages
@staticmethod
def remove_directory(path):
try:
if os.path.exists(path):
if os.path.isdir(path):
shutil.rmtree(path)
except Exception as e:
print(f"An error occurred while trying to remove {path}: {str(e)}")
@staticmethod
def remove_file(file_path):
try:
if os.path.exists(file_path):
if os.path.isfile(file_path):
os.remove(file_path)
except Exception as e:
print(f"An error occurred while trying to remove the file {file_path}: {str(e)}")
@staticmethod
def save_upload(file) -> Tuple[str, str]:
ext = file.filename.split('.')[-1]
path_id = str(uuid.uuid4())
os.makedirs(f'./tmp/{path_id}', exist_ok=True)
tmp_filename = f'./tmp/{path_id}/uploaded.{ext}'
file.save(tmp_filename)
return ext, path_id
import base64
import io
import os
import shutil
import subprocess
import uuid
import datetime
from pathlib import Path
from typing import Optional, Tuple
import aiofiles
import numpy as np
import pypandoc
from PIL import Image
from fastapi import UploadFile
class FileHelper:
@staticmethod
def delete_files_older_than_one_day(directory: str):
current_time = datetime.datetime.now()
for entry in os.scandir(directory):
if entry.is_file():
file_path = Path(entry)
file_name = file_path.name
file_modified_time = datetime.datetime.fromtimestamp(file_path.stat().st_mtime)
time_difference = current_time - file_modified_time
if time_difference.days > 1 and "placeholder" not in file_name:
file_path.unlink()
print(f"Deleted file: {file_path}")
# Supposedly pandoc covers a wide range of file extensions only tested with docx
@staticmethod
def convert_file_to_pdf(input_path: str, output_path: str):
pypandoc.convert_file(input_path, 'pdf', outputfile=output_path, extra_args=[
'-V', 'geometry:paperwidth=5.5in',
'-V', 'geometry:paperheight=8.5in',
'-V', 'geometry:margin=0.5in',
'-V', 'pagestyle=empty'
])
@staticmethod
def convert_file_to_html(input_path: str, output_path: str):
pypandoc.convert_file(input_path, 'html', outputfile=output_path)
@staticmethod
def pdf_to_png(path_id: str):
to_png = f"pdftoppm -png exercises.pdf page"
result = subprocess.run(to_png, shell=True, cwd=f'./tmp/{path_id}', capture_output=True, text=True)
if result.returncode != 0:
raise Exception(
f"Couldn't convert pdf to png. Failed to run command '{to_png}' -> ```cmd {result.stderr}```")
@staticmethod
def is_page_blank(image_bytes: bytes, image_threshold=10) -> bool:
with Image.open(io.BytesIO(image_bytes)) as img:
img_gray = img.convert('L')
img_array = np.array(img_gray)
non_white_pixels = np.sum(img_array < 255)
return non_white_pixels <= image_threshold
@classmethod
async def _encode_image(cls, image_path: str, image_threshold=10) -> Optional[str]:
async with aiofiles.open(image_path, "rb") as image_file:
image_bytes = await image_file.read()
if cls.is_page_blank(image_bytes, image_threshold):
return None
return base64.b64encode(image_bytes).decode('utf-8')
@classmethod
async def b64_pngs(cls, path_id: str, files: list[str]):
png_messages = []
for filename in files:
b64_string = await cls._encode_image(os.path.join(f'./tmp/{path_id}', filename))
if b64_string:
png_messages.append({
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{b64_string}"
}
})
return png_messages
@staticmethod
def remove_directory(path):
try:
if os.path.exists(path):
if os.path.isdir(path):
shutil.rmtree(path)
except Exception as e:
print(f"An error occurred while trying to remove {path}: {str(e)}")
@staticmethod
def remove_file(file_path):
try:
if os.path.exists(file_path):
if os.path.isfile(file_path):
os.remove(file_path)
except Exception as e:
print(f"An error occurred while trying to remove the file {file_path}: {str(e)}")
@staticmethod
async def save_upload(file: UploadFile, name: str = "upload", path_id: str = None) -> Tuple[str, str]:
ext = file.filename.split('.')[-1]
path_id = str(uuid.uuid4()) if path_id is None else path_id
os.makedirs(f'./tmp/{path_id}', exist_ok=True)
tmp_filename = f'./tmp/{path_id}/{name}.{ext}'
file_bytes: bytes = await file.read()
async with aiofiles.open(tmp_filename, 'wb') as file:
await file.write(file_bytes)
return ext, path_id
@staticmethod
async def encode_image(image_path: str) -> str:
async with aiofiles.open(image_path, "rb") as image_file:
img = await image_file.read()
return base64.b64encode(img).decode('utf-8')

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