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

# Securely load your Groq API key from environment variables
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")

# Define the expected JSON response schema
class ExtractionResult(BaseModel):
    answers: List[str]

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

llm = get_llm()

# Root endpoint: Provides a welcome message and instructions
@app.get("/")
async def root():
    return {
        "message": "Welcome to the PDF Question Extractor API.",
        "usage": "POST your PDF to /extract-answers/ to extract answers."
    }

# PDF extraction endpoint: Processes a PDF file upload
@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 split the PDF into pages
        loader = PyPDFLoader(file_path)
        pages = loader.load_and_split()
        all_page_content = "\n".join(page.page_content for page in pages)

        # Generate the JSON schema from the Pydantic model
        schema_dict = ExtractionResult.model_json_schema()
        schema = json.dumps(schema_dict, indent=2)

        # Build the prompt with system and user messages
        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 = (
            "Please extract the correct answers and options (A, B, C, D, E) from the following document content:\n\n"
            + all_page_content
        )
        prompt = system_message + "\n\n" + user_message

        # Invoke the LLM and request a JSON response
        response = llm.invoke(prompt, response_format={"type": "json_object"})

        # Validate and parse the JSON response using Pydantic
        result = ExtractionResult.model_validate_json(response.content)

        # Cleanup the temporary file
        os.remove(file_path)

        return result.model_dump()

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