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Editing File: /home/ubuntu/combine_ai/lang_learning/app.py

import uuid
from fastapi import FastAPI, HTTPException
import openai
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from dotenv import load_dotenv
import os
from redis import Redis
import re
from difflib import SequenceMatcher
import string

load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

redis_client = Redis(host='localhost', port=6379, db=0, decode_responses=True)

class ExerciseInput(BaseModel):
    user_id: str
    language: str
    level: str

class EvaluationInput(BaseModel):
    user_id: str
    session_id: str
    user_response: str

def set_session(user_id, session_id, data):
    key = f"{user_id}:{session_id}"
    redis_client.set(key, data)

def get_session(user_id, session_id):
    key = f"{user_id}:{session_id}"
    return redis_client.get(key)

def delete_session(user_id, session_id):
    key = f"{user_id}:{session_id}"
    redis_client.delete(key)

def parse_openai_response(response_content):
    patterns = [
        r'^(.*?):\s*(.+?)\s*\n\nEnglish:\s*(.+)$',
        r'^(.+?)\s*\n\nEnglish:\s*(.+)$',
    ]
    for pattern in patterns:
        match = re.search(pattern, response_content, re.DOTALL | re.IGNORECASE)
        if match:
            question = match.group(match.lastindex - 1).strip()
            translation = match.group(match.lastindex).strip()
            return question, translation

parts = response_content.strip().split('\n')
    english_index = next((i for i, part in enumerate(parts) if 'English:' in part), None)
    if english_index and english_index > 0:
        question = parts[english_index - 1].split(': ', 1)[-1].strip()
        translation = parts[english_index].split(': ', 1)[-1].strip()
        return question, translation

return None, None

@app.post("/generate_exercise/")
async def generate_exercise(input: ExerciseInput):
    context_prompts = {
        "Tamil": {
            "beginner": "Create a simple Tamil question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "intermediate": "Create a moderate Tamil sentence and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "advanced": "Create a complex Tamil sentence and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response"
        },
        "French": {
            "beginner": "Create a simple French question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "intermediate": "Create a moderate French question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "advanced": "Create a complex French question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response"
        },
        "Spanish": {
            "beginner": "Create a simple Spanish question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "intermediate": "Create a moderate Spanish question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response",
            "advanced": "Create a complex Spanish question and its English translation for language learner.Try to generate diffrent questions everytime and maintain the format of your response"
        }
    }
    if input.language not in context_prompts or input.level not in context_prompts[input.language]:
        raise HTTPException(status_code=400, detail="Invalid language or level")

prompt = context_prompts[input.language][input.level]
    try:
        response = openai.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "system", "content": "You are a helpful language learning assistant."},
                      {"role": "user", "content": prompt}]
        )
        content = response.choices[0].message.content
        question, correct_answer = parse_openai_response(content)
        if question is None or correct_answer is None:
            raise ValueError("Failed to parse the OpenAI response")

session_id = str(uuid.uuid4())
        set_session(input.user_id, session_id, correct_answer)
        return {"user_id": input.user_id, "session_id": session_id, "question": question}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

def normalize_answer(answer):
    answer = answer.lower().translate(str.maketrans('', '', string.punctuation))
    answer = " ".join(answer.split())
    return answer

@app.post("/evaluate/")
async def evaluate(input: EvaluationInput):
    if not get_session(input.user_id, input.session_id):
        raise HTTPException(status_code=404, detail="Session not found")

correct_answer = get_session(input.user_id, input.session_id)
    normalized_user_response = normalize_answer(input.user_response)
    normalized_correct_answer = normalize_answer(correct_answer)

similarity = SequenceMatcher(None, normalized_user_response, normalized_correct_answer).ratio()
    similarity_threshold = 0.8
    if similarity >= similarity_threshold:
        result = "Correct"
        success = True
    else:
        result = f"Incorrect! Similarity: {similarity:.2f}"
        success = False
    delete_session(input.user_id, input.session_id)

return {"success": success, "result": result, "correct_answer": correct_answer, "similarity": similarity}

if __name__ == "__main__":
    import uvicorn

uvicorn.run(app, host="0.0.0.0", port=8006, ssl_keyfile="privkey.pem", ssl_certfile="fullchain.pem")

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