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import gradio as gr
from transformers import pipeline
import fitz # PyMuPDF
import docx
import pptx
import openpyxl
import os
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
# Load your custom summarization model
pipe = pipeline("text2text-generation", model="FeruzaBoynazarovaas/my_awesome_billsum_model")
# Document text extraction function
def extract_text(file):
ext = file.name.split(".")[-1].lower()
path = file.name
if ext == "pdf":
try:
with fitz.open(path) as doc:
return "\n".join([page.get_text("text") for page in doc])
except Exception as e:
return f"Error reading PDF: {e}"
elif ext == "docx":
try:
doc = docx.Document(path)
return "\n".join([p.text for p in doc.paragraphs])
except Exception as e:
return f"Error reading DOCX: {e}"
elif ext == "pptx":
try:
prs = pptx.Presentation(path)
text = ""
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
text += shape.text + "\n"
return text
except Exception as e:
return f"Error reading PPTX: {e}"
elif ext == "xlsx":
try:
wb = openpyxl.load_workbook(path)
text = ""
for sheet in wb.sheetnames:
for row in wb[sheet].iter_rows(values_only=True):
text += " ".join([str(cell) for cell in row if cell]) + "\n"
return text
except Exception as e:
return f"Error reading XLSX: {e}"
else:
return "Unsupported file format"
# Summarization logic
def summarize_document(file):
text = extract_text(file)
if "Error" in text or "Unsupported" in text:
return text
word_count = len(text.split())
max_summary_len = max(20, int(word_count * 0.2))
try:
summary = pipe(text, max_length=max_summary_len, min_length=int(max_summary_len * 0.6), do_sample=False)
return summary[0]['generated_text']
except Exception as e:
return f"Error during summarization: {e}"
# Gradio Interface
demo = gr.Interface(
fn=summarize_document,
inputs=gr.File(label="Upload a document (PDF, DOCX, PPTX, XLSX)", file_types=[".pdf", ".docx", ".pptx", ".xlsx"]),
outputs=gr.Textbox(label="20% Summary"),
title="📄 Document Summarizer (20% Length)",
description="Upload a document and get a concise summary generated by your custom Hugging Face model."
)
# FastAPI setup
app = FastAPI()
# Mount Gradio at "/"
app = gr.mount_gradio_app(app, demo, path="/")
# Optional root redirect
@app.get("/")
def redirect_to_interface():
return RedirectResponse(url="/")
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