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import gradio as gr
from transformers import pipeline
from PIL import Image
from fastapi import FastAPI
from starlette.responses import RedirectResponse
# Load models
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
image_captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
# FastAPI app
app = FastAPI()
def analyze_input(file, question=None):
if file is None:
return "Please upload a document or image."
filename = file.name.lower()
if filename.endswith((".png", ".jpg", ".jpeg")):
image = Image.open(file)
caption = image_captioner(image)[0]['generated_text']
return f"📷 Image Interpretation:\n{caption}"
elif filename.endswith((".pdf", ".docx", ".pptx", ".xlsx")):
from PyPDF2 import PdfReader
import docx
import pptx
import pandas as pd
try:
text = ""
if filename.endswith(".pdf"):
reader = PdfReader(file)
text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
elif filename.endswith(".docx"):
doc = docx.Document(file)
text = "\n".join([p.text for p in doc.paragraphs if p.text.strip()])
elif filename.endswith(".pptx"):
prs = pptx.Presentation(file)
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
text += shape.text + "\n"
elif filename.endswith(".xlsx"):
df = pd.read_excel(file, sheet_name=None)
text = "\n".join([df[sheet].to_string() for sheet in df])
if not text.strip():
return "Could not extract meaningful text from the document."
summary = summarizer(text[:3000], max_length=200, min_length=30, do_sample=False)
return f"📄 Document Summary:\n{summary[0]['summary_text']}"
except Exception as e:
return f"❌ Error processing document: {str(e)}"
else:
return "Unsupported file type. Please upload a valid image or document."
# Gradio Interface
iface = gr.Interface(
fn=analyze_input,
inputs=gr.File(label="Upload Document or Image"),
outputs=gr.Textbox(label="Result", lines=10),
title="Document & Image Analysis Web Service",
description="Upload a document (PDF, DOCX, PPTX, XLSX) to get a summary or an image to get a caption. Runs fully on CPU."
)
# Wrap in TabbedInterface (even if only one for now)
demo = gr.TabbedInterface([iface], ["Docs and Images"])
# Mount to FastAPI app
app = gr.mount_gradio_app(app, demo, path="/")
@app.get("/")
def home():
return RedirectResponse(url="/")
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