Writing and speaking rework, some changes to module upload

app/api/grade.py

@@ -1,9 +1,8 @@
 from dependency_injector.wiring import inject, Provide
-from fastapi import APIRouter, Depends, Path, Request
+from fastapi import APIRouter, Depends, Path, Request, BackgroundTasks
 
 from app.controllers.abc import IGradeController
 from app.dtos.writing import WritingGradeTaskDTO
-from app.dtos.speaking import GradeSpeakingAnswersDTO, GradeSpeakingDTO
 from app.middlewares import Authorized, IsAuthenticatedViaBearerToken
 
 controller = "grade_controller"
@@ -17,35 +16,51 @@ grade_router = APIRouter()
 @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)
+    return await grade_controller.grade_writing_task(task, data, background_tasks)
 
 
-@grade_router.post(
-    '/speaking/2',
-    dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
-)
-@inject
-async def grade_speaking_task_2(
-    data: GradeSpeakingDTO,
-    grade_controller: IGradeController = Depends(Provide[controller])
-):
-    return await grade_controller.grade_speaking_task(2, [data.dict()])
-
 
 @grade_router.post(
     '/speaking/{task}',
     dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
 )
 @inject
-async def grade_speaking_task_1_and_3(
+async def grade_speaking_task(
-    data: GradeSpeakingAnswersDTO,
+    request: Request,
+    background_tasks: BackgroundTasks,
     task: int = Path(..., ge=1, le=3),
     grade_controller: IGradeController = Depends(Provide[controller])
 ):
-    return await grade_controller.grade_speaking_task(task, data.answers)
+    form = await request.form()
+    return await grade_controller.grade_speaking_task(task, form, background_tasks)
+
+
+@grade_router.get(
+    '/pending/{sessionId}',
+    dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
+)
+@inject
+async def get_pending_evaluations(
+    session_id: str,
+    grade_controller: IGradeController = Depends(Provide[controller])
+):
+    return await grade_controller.get_evaluations(session_id, "pending")
+
+
+@grade_router.get(
+    '/completed/{sessionId}',
+    dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]
+)
+@inject
+async def get_completed_evaluations(
+    session_id: str,
+    grade_controller: IGradeController = Depends(Provide[controller])
+):
+    return await grade_controller.get_evaluations(session_id, "completed")
 
 
 @grade_router.post(

app/api/level.py

@@ -18,7 +18,6 @@ async def generate_exercises(
     dto: LevelExercisesDTO,
     level_controller: ILevelController = Depends(Provide[controller])
 ):
-    print(dto.dict())
     return await level_controller.generate_exercises(dto)
 
 @level_router.get(

app/api/speaking.py

@@ -3,9 +3,8 @@ from typing import Optional
 
 from dependency_injector.wiring import inject, Provide
 from fastapi import APIRouter, Path, Query, Depends
-from pydantic import BaseModel
 
-from app.dtos.video import Task, TaskStatus
+from app.dtos.speaking import Video
 from app.middlewares import Authorized, IsAuthenticatedViaBearerToken
 from app.configs.constants import EducationalContent
 from app.controllers.abc import ISpeakingController
@@ -13,11 +12,6 @@ from app.controllers.abc import ISpeakingController
 controller = "speaking_controller"
 speaking_router = APIRouter()
 
-
-class Video(BaseModel):
-    text: str
-    avatar: str
-
 @speaking_router.post(
     '/media',
     dependencies=[Depends(Authorized([IsAuthenticatedViaBearerToken]))]

app/configs/dependency_injection.py

@@ -18,11 +18,11 @@ load_dotenv()
 
 class DependencyInjector:
 
-    def __init__(self, polly_client: any, http_client: HTTPClient, whisper_model: any):
+    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._whisper_model = whisper_model
+        self._stt = stt
 
     def inject(self):
         self._setup_clients()
@@ -33,22 +33,25 @@ class DependencyInjector:
         self._container.wire(
             packages=["app"]
         )
+        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.whisper_model = providers.Object(self._whisper_model)
+        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.stt = providers.Factory(OpenAIWhisper, model=self._container.whisper_model)
         self._container.tts = providers.Factory(AWSPolly, client=self._container.polly_client)
+
+        """
         with open('app/services/impl/third_parties/elai/conf.json', 'r') as file:
             elai_conf = json.load(file)
 
         with open('app/services/impl/third_parties/elai/avatars.json', 'r') as file:
             elai_avatars = json.load(file)
+        """
 
         with open('app/services/impl/third_parties/heygen/avatars.json', 'r') as file:
             heygen_avatars = json.load(file)

app/controllers/abc/grade.py

@@ -1,15 +1,27 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List
+from typing import Dict, List, Union
+from fastapi import BackgroundTasks
+from fastapi.datastructures import FormData
 
 
 class IGradeController(ABC):
 
     @abstractmethod
-    async def grade_writing_task(self, task: int, data):
+    async def grade_writing_task(
+        self, session_id: str, exercise_id: str,
+        task: int, dto: any,
+        background_tasks: BackgroundTasks
+    ):
         pass
 
     @abstractmethod
-    async def grade_speaking_task(self, task: int, answers: List[Dict]) -> Dict:
+    async def grade_speaking_task(
+            self, task: int, form: FormData, background_tasks: BackgroundTasks
+    ):
+        pass
+
+    @abstractmethod
+    async def get_evaluations(self, session_id: str, status: str):
         pass
 
     @abstractmethod

app/controllers/impl/grade.py

@@ -1,34 +1,96 @@
 import logging
-from typing import Dict, List
+from typing import Dict, List, Union
+from uuid import uuid4
+
+from fastapi import BackgroundTasks, Response, HTTPException
+from fastapi.datastructures import FormData
 
-from app.configs.constants import FilePaths
 from app.controllers.abc import IGradeController
+from app.dtos.evaluation import EvaluationType
+from app.dtos.speaking import GradeSpeakingItem
 from app.dtos.writing import WritingGradeTaskDTO
-from app.helpers import FileHelper
+from app.services.abc import IGradeService, IEvaluationService
-from app.services.abc import ISpeakingService, IWritingService, IGradeService
-from app.utils import handle_exception
-
 
 class GradeController(IGradeController):
 
     def __init__(
             self,
             grade_service: IGradeService,
-            speaking_service: ISpeakingService,
+            evaluation_service: IEvaluationService,
-            writing_service: IWritingService
     ):
         self._service = grade_service
-        self._speaking_service = speaking_service
+        self._evaluation_service = evaluation_service
-        self._writing_service = writing_service
         self._logger = logging.getLogger(__name__)
 
-    async def grade_writing_task(self, task: int, data: WritingGradeTaskDTO):
+    async def grade_writing_task(
-        return await self._writing_service.grade_writing_task(task, data.question, data.answer)
+        self, session_id: str, exercise_id: str,
+        task: int, dto: WritingGradeTaskDTO, background_tasks: BackgroundTasks
+    ):
+        await self._evaluation_service.create_or_update_evaluation(
+            dto.sessionId, dto.exercise_id, EvaluationType.WRITING, task
+        )
 
-    @handle_exception(400)
+        await self._evaluation_service.begin_evaluation(
-    async def grade_speaking_task(self, task: int, answers: List[Dict]) -> Dict:
+            session_id, task, exercise_id, EvaluationType.WRITING, dto, background_tasks
-        FileHelper.delete_files_older_than_one_day(FilePaths.AUDIO_FILES_PATH)
+        )
-        return await self._speaking_service.grade_speaking_task(task, answers)
+
+        return Response(status_code=200)
+
+    async def grade_speaking_task(self, task: int, form: FormData, background_tasks: BackgroundTasks):
+        answers: Dict[int, Dict] = {}
+        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_or_update_evaluation(
+            session_id, exercise_id, ex_type, task
+        )
+
+        await self._evaluation_service.begin_evaluation(
+            session_id, task, exercise_id, ex_type, items, background_tasks
+        )
+
+        return Response(status_code=200)
+
+    async def get_evaluations(self, session_id: str, status: str):
+        return await self._evaluation_service.get_evaluations(session_id, status)
 
     async def grade_short_answers(self, data: Dict):
         return await self._service.grade_short_answers(data)

app/dtos/evaluation.py

@@ -0,0 +1,18 @@
+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

app/dtos/speaking.py

@@ -1,27 +1,13 @@
-import random
+from typing import List, Dict
-from typing import List, Dict, Optional
 
+from fastapi import UploadFile
 from pydantic import BaseModel
 
-from app.configs.constants import MinTimers
 
+class Video(BaseModel):
+    text: str
+    avatar: str
 
-class SaveSpeakingDTO(BaseModel):
+class GradeSpeakingItem(BaseModel):
-    exercises: List[Dict]
-    minTimer: int = MinTimers.SPEAKING_MIN_TIMER_DEFAULT
-
-
-class GradeSpeakingDTO(BaseModel):
     question: str
-    answer: str
+    answer: UploadFile
-
-
-class GradeSpeakingAnswersDTO(BaseModel):
-    answers: List[Dict]
-
-
-class GenerateVideo1DTO(BaseModel):
-    avatar: str = Optional[str]
-    questions: List[str]
-    first_topic: str
-    second_topic: str

app/dtos/writing.py

@@ -2,5 +2,7 @@ from pydantic import BaseModel
 
 
 class WritingGradeTaskDTO(BaseModel):
+    sessionId: str
     question: str
     answer: str
+    exercise_id: str

app/repositories/impl/document_stores/mongo.py

@@ -35,7 +35,7 @@ class MongoDB(IDocumentStore):
         return [document async for document in cursor]
 
     async def update(self, collection: str, filter_query: Dict, update: Dict) -> Optional[str]:
-        return (await self._mongo_db[collection].update_one(filter_query, update)).upserted_id
+        return (await self._mongo_db[collection].update_one(filter_query, update, upsert=True)).upserted_id
 
     async def get_doc_by_id(self, collection: str, doc_id: str) -> Optional[Dict]:
         return await self._mongo_db[collection].find_one({"id": doc_id})

app/server.py

@@ -12,7 +12,6 @@ from typing import List
 from http import HTTPStatus
 
 import httpx
-import whisper
 from fastapi import FastAPI, Request
 from fastapi.encoders import jsonable_encoder
 from fastapi.exceptions import RequestValidationError
@@ -27,6 +26,7 @@ from app.api import router
 from app.configs import DependencyInjector
 from app.exceptions import CustomException
 from app.middlewares import AuthenticationMiddleware, AuthBackend
+from app.services.impl import OpenAIWhisper
 
 
 @asynccontextmanager
@@ -36,8 +36,6 @@ async def lifespan(_app: FastAPI):
 
         https://fastapi.tiangolo.com/advanced/events/
     """
-    # Whisper model
-    whisper_model = whisper.load_model("base")
 
     # NLTK required datasets download
     nltk.download('words')
@@ -56,11 +54,12 @@ async def lifespan(_app: FastAPI):
     )
 
     http_client = httpx.AsyncClient()
+    stt = OpenAIWhisper()
 
     DependencyInjector(
         polly_client,
         http_client,
-        whisper_model
+        stt
     ).inject()
 
     # Setup logging
@@ -72,6 +71,7 @@ async def lifespan(_app: FastAPI):
 
     yield
 
+    stt.close()
     await http_client.aclose()
     await polly_client.close()
     await context_stack.aclose()

app/services/abc/__init__.py

@@ -2,9 +2,11 @@ from .third_parties import *
 from .exam import *
 from .training import *
 from .user import IUserService
+from .evaluation import IEvaluationService
 
 __all__ = [
-    "IUserService"
+    "IUserService",
+    "IEvaluationService"
 ]
 __all__.extend(third_parties.__all__)
 __all__.extend(exam.__all__)

app/services/abc/evaluation.py

@@ -0,0 +1,33 @@
+from abc import abstractmethod, ABC
+from typing import Union, List, Dict
+
+from fastapi import BackgroundTasks
+
+from app.dtos.evaluation import EvaluationType
+
+class IEvaluationService(ABC):
+
+    @abstractmethod
+    async def create_evaluation(
+        self,
+        session_id: str,
+        exercise_id: str,
+        eval_type: EvaluationType,
+        task: int
+    ):
+        pass
+
+    @abstractmethod
+    async def begin_evaluation(
+        self,
+        session_id: str, task: int,
+        exercise_id: str, exercise_type: str,
+        solution: any,
+        background_tasks: BackgroundTasks
+    ):
+        pass
+
+    @abstractmethod
+    async def get_evaluations(self, session_id: str, status: str) -> List[Dict]:
+        pass
+

app/services/abc/exam/speaking.py

@@ -11,6 +11,6 @@ class ISpeakingService(ABC):
         pass
 
     @abstractmethod
-    async def grade_speaking_task(self, task: int, answers: List[Dict]) -> Dict:
+    async def grade_speaking_task(self, task: int, items: any) -> Dict:
         pass
 

app/services/impl/exam/evaluation.py

@@ -0,0 +1,108 @@
+import logging
+from typing import Union, Dict, List
+
+from fastapi import BackgroundTasks
+
+from app.dtos.evaluation import EvaluationType
+from app.dtos.speaking import GradeSpeakingItem
+from app.dtos.writing import WritingGradeTaskDTO
+from app.repositories.abc import IDocumentStore
+from app.services.abc import IWritingService, ISpeakingService, IEvaluationService
+
+
+class EvaluationService(IEvaluationService):
+
+    def __init__(self, db: IDocumentStore, writing_service: IWritingService, speaking_service: ISpeakingService):
+        self._db = db
+        self._writing_service = writing_service
+        self._speaking_service = speaking_service
+        self._logger = logging.getLogger(__name__)
+
+    async def create_evaluation(
+            self,
+            session_id: str,
+            exercise_id: str,
+            eval_type: EvaluationType,
+            task: int
+    ):
+        await self._db.save_to_db(
+        "evaluation",
+        {
+                "session_id": session_id,
+                "exercise_id": exercise_id,
+                "type": eval_type,
+                "task": task,
+                "status": "pending"
+            }
+        )
+
+    async def begin_evaluation(
+            self,
+            session_id: str, task: int,
+            exercise_id: str, exercise_type: str,
+            solution: Union[WritingGradeTaskDTO, List[GradeSpeakingItem]],
+            background_tasks: BackgroundTasks
+    ):
+        background_tasks.add_task(
+            self._begin_evaluation,
+            session_id, task,
+            exercise_id, exercise_type,
+            solution
+        )
+
+    async def _begin_evaluation(
+        self, session_id: str, task: int,
+        exercise_id: str, exercise_type: str,
+        solution: Union[WritingGradeTaskDTO, List[GradeSpeakingItem]]
+    ):
+        try:
+            if exercise_type == EvaluationType.WRITING:
+                result = await self._writing_service.grade_writing_task(
+                    task,
+                    solution.question,
+                    solution.answer
+                )
+            else:
+                result = await self._speaking_service.grade_speaking_task(
+                    task,
+                    solution
+                )
+
+            await self._db.update(
+                "evaluation",
+                {
+                    "exercise_id": exercise_id,
+                    "session_id": session_id,
+                },
+                {
+                    "$set": {
+                        "status": "completed",
+                        "result": result,
+                    }
+                }
+            )
+
+        except Exception as e:
+            self._logger.error(f"Error processing evaluation {session_id} - {exercise_id}: {str(e)}")
+            await self._db.update(
+                "evaluation",
+                {
+                    "exercise_id": exercise_id,
+                    "session_id": session_id
+                },
+                {
+                    "$set": {
+                        "status": "error",
+                        "error": str(e),
+                    }
+                }
+            )
+
+    async def get_evaluations(self, session_id: str, status: str) -> List[Dict]:
+        return await self._db.find(
+            "evaluation",
+            {
+                "session_id": session_id,
+                "status": status
+            }
+        )

app/services/impl/exam/level/upload.py

@@ -1,8 +1,10 @@
+from uuid import uuid4
+
 import aiofiles
 import os
 from logging import getLogger
 
-from typing import Dict, Any, Coroutine, Optional
+from typing import Dict, Any, Optional
 
 import pdfplumber
 from fastapi import UploadFile
@@ -21,20 +23,19 @@ class UploadLevelModule:
         self._logger = getLogger(__name__)
         self._llm = openai
 
-    async def generate_level_from_file(self, file: UploadFile, solutions: Optional[UploadFile]) -> Dict[str, Any] | None:
+    async def generate_level_from_file(self, exercises: UploadFile, solutions: Optional[UploadFile]) -> Dict[str, Any] | None:
-        ext, path_id = await FileHelper.save_upload(file)
+        path_id = str(uuid4())
-        FileHelper.convert_file_to_pdf(
+        ext, _ = await FileHelper.save_upload(exercises, "exercises", path_id)
-            f'./tmp/{path_id}/upload.{ext}', f'./tmp/{path_id}/exercises.pdf'
+        FileHelper.convert_file_to_html(f'./tmp/{path_id}/exercises.{ext}', f'./tmp/{path_id}/exercises.html')
-        )
-        file_has_images = self._check_pdf_for_images(f'./tmp/{path_id}/exercises.pdf')
 
-        if not file_has_images:
+        if solutions:
-            FileHelper.convert_file_to_html(f'./tmp/{path_id}/upload.{ext}', f'./tmp/{path_id}/exercises.html')
+            ext, _ = await FileHelper.save_upload(solutions, "solutions", path_id)
+            FileHelper.convert_file_to_html(f'./tmp/{path_id}/solutions.{ext}', f'./tmp/{path_id}/solutions.html')
 
-        completion: Coroutine[Any, Any, Exam] = (
+        #completion: Coroutine[Any, Any, Exam] = (
-            self._png_completion(path_id) if file_has_images else self._html_completion(path_id)
+        #    self._png_completion(path_id) if file_has_images else self._html_completion(path_id)
-        )
+        #)
-        response = await completion
+        response = await self._html_completion(path_id)
 
         FileHelper.remove_directory(f'./tmp/{path_id}')
 
@@ -42,6 +43,7 @@ class UploadLevelModule:
             return self.fix_ids(response.model_dump(exclude_none=True))
         return None
 
+
     @staticmethod
     @suppress_loggers()
     def _check_pdf_for_images(pdf_path: str) -> bool:

app/services/impl/exam/reading/import_reading.py

@@ -98,7 +98,7 @@ class ImportReadingModule:
                     ]
                 }
             ],
-            "text": "<numbered questions with format: <question text>{{<question number>}}\\n>",
+            "text": "<numbered questions with format in square brackets: [<question text>{{<question number>}}\\\\n] notice how there is a double backslash before the n -> I want an escaped newline in your output> ",
             "type": "writeBlanks",
             "prompt": "<specific instructions for this exercise section>"
         }

app/services/impl/exam/speaking.py

@@ -1,6 +1,7 @@
+import asyncio
 import logging
 import os
-import random
+import aiofiles
 import re
 import uuid
 from typing import Dict, List, Optional
@@ -9,6 +10,7 @@ from app.configs.constants import (
     FieldsAndExercises, GPTModels, TemperatureSettings,
     FilePaths
 )
+from app.dtos.speaking import GradeSpeakingItem
 from app.helpers import TextHelper
 from app.repositories.abc import IFileStorage, IDocumentStore
 from app.services.abc import ISpeakingService, ILLMService, IVideoGeneratorService, ISpeechToTextService
@@ -165,106 +167,111 @@ class SpeakingService(ISpeakingService):
 
         return response
 
-    async def grade_speaking_task(self, task: int, answers: List[Dict]) -> Dict:
+    async def grade_speaking_task(self, task: int, items: List[GradeSpeakingItem]) -> Dict:
-        request_id = uuid.uuid4()
+        request_id = str(uuid.uuid4())
-        self._logger.info(
+        self._log(task, request_id, f"Received request to grade speaking task {task}.")
-            f'POST - speaking_task_{task} - Received request to grade speaking task {task}. '
-            f'Use this id to track the logs: {str(request_id)} - Request data: {str(answers)}'
-        )
-
-        text_answers = []
-        perfect_answers = []
 
         if task != 2:
-            self._logger.info(
+            self._log(task, request_id, f'Received {len(items)} total answers.')
-                f'POST - speaking_task_{task} - {str(request_id)} - Received {str(len(answers))} total answers.'
-            )
 
-        for item in answers:
+        temp_files = []
-            sound_file_name = FilePaths.AUDIO_FILES_PATH + str(uuid.uuid4())
+        try:
+            # Save all files first
+            temp_files = await asyncio.gather(*[
+                self.save_file(item) for item in items
+            ])
 
-            self._logger.info(f'POST - speaking_task_{task} - {request_id} - Downloading file {item["answer"]}')
+            # Process all transcriptions concurrently (up to 4)
+            self._log(task, request_id, 'Starting batch transcription')
+            text_answers = await asyncio.gather(*[
+                self._stt.speech_to_text(file_path)
+                for file_path in temp_files
+            ])
 
-            await self._file_storage.download_firebase_file(item["answer"], sound_file_name)
+            for answer in text_answers:
-
+                self._log(task, request_id, f'Transcribed answer: {answer}')
-            self._logger.info(
+                if not TextHelper.has_x_words(answer, 20):
-                f'POST - speaking_task_{task} - {request_id} - '
+                    self._log(
-                f'Downloaded file {item["answer"]} to {sound_file_name}'
+                        task, request_id,
-            )
+                        f'The answer had less words than threshold 20 to be graded. Answer: {answer}'
-
-            answer_text = await self._stt.speech_to_text(sound_file_name)
-            self._logger.info(f'POST - speaking_task_{task} - {request_id} - Transcripted answer: {answer_text}')
-
-            text_answers.append(answer_text)
-            item["answer"] = answer_text
-            os.remove(sound_file_name)
-
-            # TODO: This will end the grading of all answers if a single one does not have enough words
-            #   don't know if this is intended
-            if not TextHelper.has_x_words(answer_text, 20):
-                self._logger.info(
-                    f'POST - speaking_task_{task} - {request_id} - '
-                    f'The answer had less words than threshold 20 to be graded. Answer: {answer_text}'
                     )
                     return self._zero_rating("The audio recorded does not contain enough english words to be graded.")
 
-            self._logger.info(
+            # Get perfect answers
-                f'POST - speaking_task_{task} - {request_id} - '
+            self._log(task, request_id, 'Requesting perfect answers')
-                f'Requesting perfect answer for question: {item["question"]}'
+            perfect_answers = await asyncio.gather(*[
-            )
+                self._get_perfect_answer(task, item.question)
-            perfect_answers.append(await self._get_perfect_answer(task, item["question"]))
+                for item in items
+            ])
 
+            # Format the responses
             if task in {1, 3}:
-            self._logger.info(
+                self._log(task, request_id, 'Formatting answers and questions for prompt.')
-                f'POST - speaking_task_{task} - {request_id} - Formatting answers and questions for prompt.'
-            )
 
                 formatted_text = ""
-            for i, entry in enumerate(answers, start=1):
+                for i, (item, transcribed_answer) in enumerate(zip(items, text_answers), start=1):
-                formatted_text += f"**Question {i}:**\n{entry['question']}\n\n"
+                    formatted_text += f"**Question {i}:**\n{item.question}\n\n"
-                formatted_text += f"**Answer {i}:**\n{entry['answer']}\n\n"
+                    formatted_text += f"**Answer {i}:**\n{transcribed_answer}\n\n"
 
-            self._logger.info(
+                self._log(task, request_id, f'Formatted answers and questions for prompt: {formatted_text}')
-                f'POST - speaking_task_{task} - {request_id} - '
-                f'Formatted answers and questions for prompt: {formatted_text}'
-            )
                 questions_and_answers = f'\n\n The questions and answers are: \n\n{formatted_text}'
             else:
-            questions_and_answers = f'\n Question: "{answers[0]["question"]}" \n Answer: "{answers[0]["answer"]}"'
+                questions_and_answers = f'\n Question: "{items[0].question}" \n Answer: "{text_answers[0]}"'
 
-        self._logger.info(f'POST - speaking_task_{task} - {request_id} - Requesting grading of the answer(s).')
+            self._log(task, request_id, 'Requesting grading of the answer(s).')
             response = await self._grade_task(task, questions_and_answers)
-
+            self._log(task, request_id, f'Answer(s) graded: {response}')
-        self._logger.info(f'POST - speaking_task_{task} - {request_id} - Answer(s) graded: {response}')
 
             if task in {1, 3}:
-            self._logger.info(
+                self._log(task, request_id, 'Adding perfect answer(s) to response.')
-                f'POST - speaking_task_{task} - {request_id} - Adding perfect answer(s) to response.')
 
                 # TODO: check if it is answer["answer"] instead
                 for i, answer in enumerate(perfect_answers, start=1):
                     response['perfect_answer_' + str(i)] = answer
 
-            self._logger.info(
+                self._log(task, request_id, 'Getting speaking corrections in parallel')
-                f'POST - speaking_task_{task} - {request_id} - Adding transcript and fixed texts to response.'
+                # Get all corrections in parallel
-            )
+                fixed_texts = await asyncio.gather(*[
+                    self._get_speaking_corrections(answer)
+                    for answer in text_answers
+                ])
 
-            for i, answer in enumerate(text_answers, start=1):
+                self._log(task, request_id, 'Adding transcript and fixed texts to response.')
+                for i, (answer, fixed) in enumerate(zip(text_answers, fixed_texts), start=1):
                     response['transcript_' + str(i)] = answer
-                response['fixed_text_' + str(i)] = await self._get_speaking_corrections(answer)
+                    response['fixed_text_' + str(i)] = fixed
             else:
-            response['transcript'] = answers[0]["answer"]
+                response['transcript'] = text_answers[0]
 
-            self._logger.info(f'POST - speaking_task_{task} - {request_id} - Requesting fixed text.')
+                self._log(task, request_id, 'Requesting fixed text.')
-            response['fixed_text'] = await self._get_speaking_corrections(answers[0]["answer"])
+                response['fixed_text'] = await self._get_speaking_corrections(text_answers[0])
-            self._logger.info(f'POST - speaking_task_{task} - {request_id} - Fixed text: {response["fixed_text"]}')
+                self._log(task, request_id, f'Fixed text: {response["fixed_text"]}')
 
                 response['perfect_answer'] = perfect_answers[0]["answer"]
 
             response["overall"] = self._fix_speaking_overall(response["overall"], response["task_response"])
-        self._logger.info(f'POST - speaking_task_{task} - {request_id} - Final response: {response}')
+            self._log(task, request_id, f'Final response: {response}')
             return response
 
+        finally:
+            for file_path in temp_files:
+                try:
+                    if os.path.exists(file_path):
+                        os.remove(file_path)
+                except Exception as e:
+                    self._log(task, request_id, f'Error cleaning up temp file {file_path}: {str(e)}')
+
+    def _log(self, task: int, request_id: str, message: str):
+        self._logger.info(f'POST - speaking_task_{task} - {request_id} - {message}')
+
+    @staticmethod
+    async def save_file(item: GradeSpeakingItem) -> str:
+        sound_file_name = FilePaths.AUDIO_FILES_PATH + str(uuid.uuid4())
+        content = await item.answer.read()
+        async with aiofiles.open(sound_file_name, 'wb') as f:
+            await f.write(content)
+        return sound_file_name
+
     # ==================================================================================================================
     # grade_speaking_task helpers
     # ==================================================================================================================
@@ -336,7 +343,7 @@ class SpeakingService(ISpeakingService):
                 {
                     "role": "user",
                     "content": (
-                        'For pronunciations act as if you heard the answers and they were transcripted '
+                        'For pronunciations act as if you heard the answers and they were transcribed '
                         'as you heard them.'
                     )
                 },

app/services/impl/exam/writing.py

@@ -1,3 +1,4 @@
+import asyncio
 from typing import List, Dict
 
 from app.services.abc import IWritingService, ILLMService, IAIDetectorService
@@ -126,7 +127,7 @@ class WritingService(IWritingService):
                 TemperatureSettings.GEN_QUESTION_TEMPERATURE
             )
 
-            response = await self._llm.prediction(
+            evaluation_promise = self._llm.prediction(
                 llm_model,
                 messages,
                 ["comment"],
@@ -134,15 +135,27 @@ class WritingService(IWritingService):
             )
 
             perfect_answer_minimum = 150 if task == 1 else 250
-            perfect_answer = await self._get_perfect_answer(question, perfect_answer_minimum)
+            perfect_answer_promise = self._get_perfect_answer(question, perfect_answer_minimum)
+            fixed_text_promise = self._get_fixed_text(answer)
+            ai_detection_promise = self._ai_detector.run_detection(answer)
 
-            response["perfect_answer"] = perfect_answer["perfect_answer"]
+            prediction_result, perfect_answer_result, fixed_text_result, ai_detection_result = await asyncio.gather(
-            response["overall"] = ExercisesHelper.fix_writing_overall(response["overall"], response["task_response"])
+                evaluation_promise,
-            response['fixed_text'] = await self._get_fixed_text(answer)
+                perfect_answer_promise,
+                fixed_text_promise,
+                ai_detection_promise
+            )
 
-            ai_detection = await self._ai_detector.run_detection(answer)
+            response = prediction_result
-            if ai_detection is not None:
+            response["perfect_answer"] = perfect_answer_result["perfect_answer"]
-                response['ai_detection'] = ai_detection
+            response["overall"] = ExercisesHelper.fix_writing_overall(
+                response["overall"],
+                response["task_response"]
+            )
+            response['fixed_text'] = fixed_text_result
+
+            if ai_detection_result is not None:
+                response['ai_detection'] = ai_detection_result
 
             return response
 

app/services/impl/third_parties/whisper.py

@@ -1,22 +1,66 @@
 import os
-
+import threading
-from fastapi.concurrency import run_in_threadpool
+import whisper
-
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict
 from whisper import Whisper
+
 from app.services.abc import ISpeechToTextService
 
+"""
+    The whisper model is not thread safe, a thread pool
+    with 4 whisper models will be created so it can
+    process up to 4 transcriptions at a time. 
     
+    The base model requires ~1GB so 4 instances is the safe bet:
+    https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages
+"""
 class OpenAIWhisper(ISpeechToTextService):
+    def __init__(self, model_name: str = "base", num_models: int = 4):
+        self._model_name = model_name
+        self._num_models = num_models
+        self._models: Dict[int, 'Whisper'] = {}
+        self._lock = threading.Lock()
+        self._next_model_id = 0
+        self._is_closed = False
 
-    def __init__(self, model: Whisper):
+        for i in range(num_models):
-        self._model = model
+            self._models[i] = whisper.load_model(self._model_name)
 
-    async def speech_to_text(self, file_path):
+        self._executor = ThreadPoolExecutor(
-        if os.path.exists(file_path):
+            max_workers=num_models,
-            result = await run_in_threadpool(
+            thread_name_prefix="whisper_worker"
-                self._model.transcribe, file_path, fp16=False, language='English', verbose=False
         )
-            return result["text"]
+
-        else:
+    def get_model(self) -> 'Whisper':
-            print("File not found:", file_path)
+        with self._lock:
-            raise Exception("File " + file_path + " not found.")
+            model_id = self._next_model_id
+            self._next_model_id = (self._next_model_id + 1) % self._num_models
+            return self._models[model_id]
+
+    async def speech_to_text(self, file_path: str) -> str:
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File {file_path} not found.")
+
+        def transcribe():
+            model = self.get_model()
+            return model.transcribe(
+                file_path,
+                fp16=False,
+                language='English',
+                verbose=False
+            )["text"]
+
+        loop = asyncio.get_running_loop()
+        return await loop.run_in_executor(self._executor, transcribe)
+
+    def close(self):
+        with self._lock:
+            if not self._is_closed:
+                self._is_closed = True
+                if self._executor:
+                    self._executor.shutdown(wait=True, cancel_futures=True)
+
+    def __del__(self):
+        self.close()