app.py

"""from fastapi import FastAPI, Form, File, UploadFile
from fastapi.responses import RedirectResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from transformers import pipeline
import os
from PIL import Image
import io
import pdfplumber
import docx
import openpyxl
import pytesseract
from io import BytesIO
import fitz  # PyMuPDF
import easyocr
from fastapi.templating import Jinja2Templates
from starlette.requests import Request

# Initialize the app
app = FastAPI()

# Mount the static directory to serve HTML, CSS, JS files
app.mount("/static", StaticFiles(directory="static"), name="static")

# Initialize transformers pipelines
qa_pipeline = pipeline("question-answering", model="microsoft/phi-2", tokenizer="microsoft/phi-2")
image_qa_pipeline = pipeline("vqa", model="Salesforce/blip-vqa-base")

# Initialize EasyOCR for image-based text extraction
reader = easyocr.Reader(['en'])

# Define a template for rendering HTML
templates = Jinja2Templates(directory="templates")

# Ensure temp_files directory exists
temp_dir = "temp_files"
os.makedirs(temp_dir, exist_ok=True)

# Function to process PDFs
def extract_pdf_text(file_path: str):
    with pdfplumber.open(file_path) as pdf:
        text = ""
        for page in pdf.pages:
            text += page.extract_text()
    return text

# Function to process DOCX files
def extract_docx_text(file_path: str):
    doc = docx.Document(file_path)
    text = "\n".join([para.text for para in doc.paragraphs])
    return text

# Function to process PPTX files
def extract_pptx_text(file_path: str):
    from pptx import Presentation
    prs = Presentation(file_path)
    text = "\n".join([shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")])
    return text

# Function to extract text from images using OCR
def extract_text_from_image(image: Image):
    return pytesseract.image_to_string(image)

# Home route
@app.get("/")
def home():
    return RedirectResponse(url="/docs")

# Function to answer questions based on document content
@app.post("/question-answering-doc")
async def question_answering_doc(question: str = Form(...), file: UploadFile = File(...)):
    file_path = os.path.join(temp_dir, file.filename)
    with open(file_path, "wb") as f:
        f.write(await file.read())

    if file.filename.endswith(".pdf"):
        text = extract_pdf_text(file_path)
    elif file.filename.endswith(".docx"):
        text = extract_docx_text(file_path)
    elif file.filename.endswith(".pptx"):
        text = extract_pptx_text(file_path)
    else:
        return {"error": "Unsupported file format"}

    qa_result = qa_pipeline(question=question, context=text)
    return {"answer": qa_result['answer']}

# Function to answer questions based on images
@app.post("/question-answering-image")
async def question_answering_image(question: str = Form(...), image_file: UploadFile = File(...)):
    image = Image.open(BytesIO(await image_file.read()))
    image_text = extract_text_from_image(image)

    image_qa_result = image_qa_pipeline({"image": image, "question": question})
    
    return {"answer": image_qa_result[0]['answer'], "image_text": image_text}

# Serve the application in Hugging Face space
@app.get("/docs")
async def get_docs(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})
"""
from fastapi import FastAPI
import gradio as gr
from transformers import pipeline
import pdfplumber, docx
from pptx import Presentation
from PIL import Image
import pytesseract
import fitz
import easyocr
import os

# Initialize FastAPI app
app = FastAPI()

# Load models
qa_pipeline = pipeline("question-answering", model="microsoft/phi-2", tokenizer="microsoft/phi-2")
image_qa_pipeline = pipeline("vqa", model="Salesforce/blip-vqa-base")
reader = easyocr.Reader(['en'])

# File parsing
def extract_text_from_pdf(file):
    with pdfplumber.open(file) as pdf:
        return "\n".join(page.extract_text() for page in pdf.pages if page.extract_text())

def extract_text_from_docx(file):
    doc = docx.Document(file)
    return "\n".join(para.text for para in doc.paragraphs)

def extract_text_from_pptx(file):
    prs = Presentation(file)
    return "\n".join(shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text"))

def extract_text_from_image(file):
    image = Image.open(file)
    return pytesseract.image_to_string(image)

# Main QA logic
def answer_question(question, file):
    file_ext = os.path.splitext(file.name)[-1].lower()
    
    if file_ext == ".pdf":
        context = extract_text_from_pdf(file)
    elif file_ext == ".docx":
        context = extract_text_from_docx(file)
    elif file_ext == ".pptx":
        context = extract_text_from_pptx(file)
    elif file_ext in [".png", ".jpg", ".jpeg", ".bmp"]:
        context = extract_text_from_image(file)
    else:
        return "❌ Unsupported file format."

    if not context.strip():
        return "⚠️ No readable text found in the document."

    result = qa_pipeline(question=question, context=context)
    return result["answer"]

# Gradio interface
gr_interface = gr.Interface(
    fn=answer_question,
    inputs=[
        gr.Textbox(label="Ask a question"),
        gr.File(label="Upload a document or image")
    ],
    outputs=gr.Textbox(label="Answer"),
    title="AI Question Answering (Text & Image)",
    description="Upload a file (PDF, DOCX, PPTX, Image) and ask a question. Get instant answers from document content.",
)

# Mount Gradio app in FastAPI
@app.get("/")
def redirect_root():
    return {"message": "Visit /gradio for the interface."}

app = gr.mount_gradio_app(app, gr_interface, path="/gradio")