app.py

from fastapi import FastAPI, UploadFile, Form
from fastapi.responses import JSONResponse, FileResponse
import fitz  # PyMuPDF for PDFs
import easyocr  # OCR for images
import openpyxl  # XLSX processing
import pptx  # PPTX processing
import docx  # DOCX processing
from transformers import pipeline
from gtts import gTTS
import tempfile
import os

app = FastAPI()

# Load AI models
qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
reader = easyocr.Reader(['en', 'fr'])

# Text Extraction
def extract_text_from_pdf(pdf_file):
    text = []
    try:
        with fitz.open(pdf_file) as doc:
            for page in doc:
                text.append(page.get_text("text"))
    except Exception as e:
        return f"Error reading PDF: {e}"
    return "\n".join(text)

def extract_text_from_docx(docx_file):
    doc = docx.Document(docx_file)
    return "\n".join(p.text for p in doc.paragraphs if p.text.strip())

def extract_text_from_pptx(pptx_file):
    text = []
    try:
        prs = pptx.Presentation(pptx_file)
        for slide in prs.slides:
            for shape in slide.shapes:
                if hasattr(shape, "text"):
                    text.append(shape.text)
    except Exception as e:
        return f"Error reading PPTX: {e}"
    return "\n".join(text)

def extract_text_from_xlsx(xlsx_file):
    text = []
    try:
        wb = openpyxl.load_workbook(xlsx_file)
        for sheet in wb.sheetnames:
            ws = wb[sheet]
            for row in ws.iter_rows(values_only=True):
                text.append(" ".join(str(cell) for cell in row if cell))
    except Exception as e:
        return f"Error reading XLSX: {e}"
    return "\n".join(text)

# Main QA logic
def answer_question_from_doc(file, question):
    ext = file.filename.split(".")[-1].lower()
    file_path = f"/tmp/{file.filename}"

    with open(file_path, "wb") as f:
        f.write(file.file.read())

    if ext == "pdf":
        context = extract_text_from_pdf(file_path)
    elif ext == "docx":
        context = extract_text_from_docx(file_path)
    elif ext == "pptx":
        context = extract_text_from_pptx(file_path)
    elif ext == "xlsx":
        context = extract_text_from_xlsx(file_path)
    else:
        return "Unsupported file format.", None

    if not context.strip():
        return "No text found in the document.", None

    try:
        result = qa_model({"question": question, "context": context})
        answer = result["answer"]
        tts = gTTS(answer)
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
            tts.save(tmp.name)
            audio_path = tmp.name
        return answer, audio_path
    except Exception as e:
        return f"Error generating answer: {e}", None

# API route for prediction
@app.post("/predict")
async def predict(file: UploadFile, question: str = Form(...)):
    answer, audio_path = answer_question_from_doc(file, question)
    if audio_path:
        return JSONResponse(content={"answer": answer, "audio": f"/audio/{os.path.basename(audio_path)}"})
    else:
        return JSONResponse(content={"answer": answer})

# Route to serve audio
@app.get("/audio/{filename}")
async def get_audio(filename: str):
    file_path = os.path.join(tempfile.gettempdir(), filename)
    return FileResponse(path=file_path, media_type="audio/mpeg")