main.py

from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional
import os
import tempfile
from transformers import pipeline
import torch
from PIL import Image
import pytesseract
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import HuggingFaceHub

# Initialize FastAPI app
app = FastAPI(
    title="AI-Powered Web Application API",
    description="API for document analysis, image captioning, and question answering",
    version="1.0.0"
)

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

# Initialize AI models (lazy loading)
summarizer = None
image_captioner = None
qa_chain = None

class SummaryRequest(BaseModel):
    file: UploadFile = File(...)

class CaptionRequest(BaseModel):
    file: UploadFile = File(...)

class QARequest(BaseModel):
    file: UploadFile = File(...)
    question: str = Form(...)

def initialize_models():
    """Initialize AI models with optimized prompts"""
    global summarizer, image_captioner, qa_chain
    
    # Document summarization model
    if summarizer is None:
        summarizer = pipeline(
            "summarization", 
            model="facebook/bart-large-cnn",
            device=0 if torch.cuda.is_available() else -1
        )
    
    # Image captioning model
    if image_captioner is None:
        image_captioner = pipeline(
            "image-to-text",
            model="nlpconnect/vit-gpt2-image-captioning",
            device=0 if torch.cuda.is_available() else -1
        )
    
    # Question answering chain
    if qa_chain is None:
        llm = HuggingFaceHub(
            repo_id="google/flan-t5-large",
            model_kwargs={"temperature": 0.1, "max_length": 512}
        )
        
        qa_prompt = PromptTemplate(
            input_variables=["document", "question"],
            template="""
            Using the provided document, answer the following question precisely. 
            If the answer cannot be determined from the document, respond with 
            'The answer cannot be determined from the provided document.'

            Question: {question}

            Rules:
            1. Provide a concise answer (1-3 sentences maximum)
            2. When possible, reference the specific section of the document that supports your answer
            3. Maintain numerical precision when answering quantitative questions
            4. For comparison questions, highlight both items being compared

            Document: {document}
            """
        )
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)

def extract_text_from_file(file: UploadFile) -> str:
    """Extract text from various file formats"""
    # Create a temporary file
    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
        temp_file.write(file.file.read())
        temp_path = temp_file.name
    
    try:
        # PDF, DOCX, PPTX, XLSX would need appropriate libraries here
        # For simplicity, we'll just read text files in this example
        if file.filename.endswith('.txt'):
            with open(temp_path, 'r', encoding='utf-8') as f:
                return f.read()
        else:
            # In a real implementation, use libraries like PyPDF2, python-docx, etc.
            raise HTTPException(
                status_code=415, 
                detail="File type not supported in this example implementation"
            )
    finally:
        os.unlink(temp_path)

@app.post("/api/summarize")
async def summarize_document(file: UploadFile = File(...)):
    """Summarize a document"""
    initialize_models()
    
    try:
        # Extract text from the document
        document_text = extract_text_from_file(file)
        
        # Generate summary with optimized prompt
        summary = summarizer(
            document_text,
            max_length=150,
            min_length=30,
            do_sample=False,
            truncation=True
        )
        
        return JSONResponse(
            content={"status": "success", "result": summary[0]['summary_text']},
            status_code=200
        )
    except Exception as e:
        raise HTTPException(
            status_code=500, 
            detail=f"Error processing document: {str(e)}"
        )

@app.post("/api/caption")
async def generate_image_caption(file: UploadFile = File(...)):
    """Generate caption for an image"""
    initialize_models()
    
    try:
        # Save the uploaded image temporarily
        with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as temp_file:
            temp_file.write(file.file.read())
            temp_path = temp_file.name
        
        # Open the image
        image = Image.open(temp_path)
        
        # Generate caption with optimized prompt
        caption = image_captioner(
            image,
            generate_kwargs={
                "max_length": 50,
                "num_beams": 4,
                "early_stopping": True
            }
        )
        
        return JSONResponse(
            content={"status": "success", "result": caption[0]['generated_text']},
            status_code=200
        )
    except Exception as e:
        raise HTTPException(
            status_code=500, 
            detail=f"Error processing image: {str(e)}"
        )
    finally:
        if 'temp_path' in locals() and os.path.exists(temp_path):
            os.unlink(temp_path)

@app.post("/api/qa")
async def answer_question(
    file: UploadFile = File(...),
    question: str = Form(...)
):
    """Answer questions based on document content"""
    initialize_models()
    
    try:
        # Extract text from the document
        document_text = extract_text_from_file(file)
        
        # Get answer using the QA chain
        answer = qa_chain.run(document=document_text, question=question)
        
        return JSONResponse(
            content={"status": "success", "result": answer},
            status_code=200
        )
    except Exception as e:
        raise HTTPException(
            status_code=500, 
            detail=f"Error processing question: {str(e)}"
        )

@app.get("/")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "version": "1.0.0"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)