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Updated this to the latest version of develop, got rid of most of the duplication, might be missing some packages in toml, needs testing

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This commit is contained in:
Carlos Mesquita
2024-08-30 02:35:11 +01:00
parent 3cf9fa5cba
commit f92a803d96
73 changed files with 3642 additions and 2703 deletions
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106
app/services/impl/grade.py
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@@ -1,42 +1,47 @@
import json
from typing import List
import copy
from typing import List, Dict
from app.configs.constants import GPTModels, TemperatureSettings
from app.services.abc import ILLMService, IGradeService
class GradeService(IGradeService):
chat_config = {'max_tokens': 1000, 'temperature': 0.2}
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 __init__(self, llm: ILLMService):
self._llm = llm
async def grade_short_answers(self, data: Dict):
json_format = {
"exercises": [
{
"id": 1,
"correct": True,
"correct_answer": " correct answer if wrong"
}
]
}
messages = [
{
"role": "system",
"content": f'You are a helpful assistant designed to output JSON on this format: {json_format}'
},
{
"role": "user",
"content": (
'Grade these answers according to the text content and write a correct answer if they are '
f'wrong. Text, questions and answers:\n {data}'
)
}
]
return await self._llm.prediction(
GPTModels.GPT_4_O,
messages,
["exercises"],
TemperatureSettings.GEN_QUESTION_TEMPERATURE
)
async def calculate_grading_summary(self, extracted_sections: List):
ret = []
@@ -116,8 +121,8 @@ class GradeService(IGradeService):
)
}]
chat_config = copy.deepcopy(self.chat_config)
tools = copy.deepcopy(self.tools)
chat_config = {'max_tokens': 1000, 'temperature': 0.2}
tools = self.get_tools()
res = await self._llm.prediction_override(
model="gpt-3.5-turbo",
@@ -154,3 +159,42 @@ class GradeService(IGradeService):
return [line + '.' if line and not line.endswith('.') else line for line in cleaned_lines]
else:
return []
@staticmethod
def get_tools():
return [
{
"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"],
},
}
}
]