main.py

import os
import json
from typing import List
from fastapi import FastAPI, UploadFile, File, HTTPException
from pydantic import BaseModel
from langchain_groq import ChatGroq
from langchain.document_loaders import PyPDFLoader

# Load API key securely from environment variable
API_KEY = os.getenv("GROQ_API_KEY")
if not API_KEY:
    raise ValueError("GROQ_API_KEY environment variable not set.")

app = FastAPI(title="PDF Question Extractor", version="1.0")

# Pydantic model for response
class ExtractionResult(BaseModel):
    answers: List[str]

# Initialize LLM
def get_llm():
    return ChatGroq(
        model="llama-3.3-70b-versatile",
        temperature=0,
        max_tokens=1024,
        api_key=API_KEY
    )

llm = get_llm()

@app.post("/extract-answers/")
async def extract_answers(file: UploadFile = File(...)):
    try:
        # Save the uploaded file temporarily
        file_path = f"./temp_{file.filename}"
        with open(file_path, "wb") as buffer:
            buffer.write(file.file.read())

        # Load and extract text from PDF
        loader = PyPDFLoader(file_path)
        pages = loader.load_and_split()
        all_page_content = "\n".join(page.page_content for page in pages)

        # JSON schema definition
        schema_dict = ExtractionResult.model_json_schema()
        schema = json.dumps(schema_dict, indent=2)

        # System message
        system_message = (
            "You are a document analysis tool that extracts the options and correct answers from the provided document content. "
            "The output must be a JSON object that strictly follows the schema: " + schema
        )

        # User message
        user_message = (
            "Please extract the correct answers and options (A, B, C, D, E) from the following document content:\n\n"
            + all_page_content
        )

        # Construct final prompt
        prompt = system_message + "\n\n" + user_message

        # Get LLM response
        response = llm.invoke(prompt, response_format={"type": "json_object"})

        # Parse and validate response
        result = ExtractionResult.model_validate_json(response.content)

        # Cleanup
        os.remove(file_path)

        return result.model_dump()

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))