app.py

from fastapi import FastAPI
from fastapi.responses import RedirectResponse
import fitz  # PyMuPDF
import docx
import openpyxl
import pptx
import io
import os
import tempfile
from PIL import Image
import gradio as gr
from transformers import pipeline

# Load models
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
image_captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")

app = FastAPI()

# -------------------------
# Extraction Functions
# -------------------------
def extract_text_from_pdf(file_bytes):
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
            tmp.write(file_bytes)
            tmp_path = tmp.name

        with fitz.open(tmp_path) as doc:
            text = "\n".join(page.get_text() for page in doc)
        os.unlink(tmp_path)
        return text
    except Exception as e:
        return f"❌ PDF extraction error: {e}"

def extract_text_from_docx(file_bytes):
    try:
        doc = docx.Document(io.BytesIO(file_bytes))
        return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
    except Exception as e:
        return f"❌ DOCX extraction error: {e}"

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

def extract_text_from_xlsx(file_bytes):
    try:
        wb = openpyxl.load_workbook(io.BytesIO(file_bytes), read_only=True, data_only=True)
        text = []
        for sheet in wb.sheetnames:
            ws = wb[sheet]
            for row in ws.iter_rows(values_only=True):
                line = " ".join(str(cell) for cell in row if cell)
                text.append(line)
        return "\n".join(text)
    except Exception as e:
        return f"❌ XLSX extraction error: {e}"

# -------------------------
# Main Logic
# -------------------------
def summarize_document(file):
    file_bytes = file.read()
    filename = getattr(file, "name", "").lower()

    if filename.endswith(".pdf"):
        text = extract_text_from_pdf(file_bytes)
    elif filename.endswith(".docx"):
        text = extract_text_from_docx(file_bytes)
    elif filename.endswith(".pptx"):
        text = extract_text_from_pptx(file_bytes)
    elif filename.endswith(".xlsx"):
        text = extract_text_from_xlsx(file_bytes)
    else:
        return "❌ Unsupported file format."

    if not text or not text.strip():
        return "❗ No extractable text found."

    try:
        summary = summarizer(text[:3000], max_length=150, min_length=30, do_sample=False)
        return f"📄 Summary:\n{summary[0]['summary_text']}"
    except Exception as e:
        return f"⚠️ Summarization error: {e}"

def interpret_image(image):
    try:
        return f"🖼️ Caption:\n{image_captioner(image)[0]['generated_text']}"
    except Exception as e:
        return f"⚠️ Image captioning error: {e}"

# -------------------------
# Gradio Interfaces
# -------------------------
doc_summary = gr.Interface(
    fn=summarize_document,
    inputs=gr.File(label="Upload a Document"),
    outputs="text",
    title="📄 Document Summarizer"
)

img_caption = gr.Interface(
    fn=interpret_image,
    inputs=gr.Image(type="pil", label="Upload an Image"),
    outputs="text",
    title="🖼️ Image Interpreter"
)

# -------------------------
# Launch with FastAPI
# -------------------------
demo = gr.TabbedInterface([doc_summary, img_caption], ["Document Summary", "Image Captioning"])
app = gr.mount_gradio_app(app, demo, path="/")

@app.get("/")
def home():
    return RedirectResponse(url="/")