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import os
import tempfile
from typing import List
import fitz # PyMuPDF
import requests
from transformers import pipeline
from gtts import gTTS
import streamlit as st
# ---------- CONFIG ----------
def summarize_text(text: str) -> str:
if not text.strip():
return "Summary not available (empty text)."
try:
# Truncate long text safely
if len(text) > 2000:
text = text[:2000]
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
result = summarizer(text, max_length=200, min_length=30, do_sample=False)
if result and isinstance(result, list) and 'summary_text' in result[0]:
return result[0]['summary_text']
return "Summary not available (model did not return text)."
except Exception as e:
return f"Summary failed: {str(e)}"
def extract_text_from_pdf(pdf_path: str) -> str:
doc = fitz.open(pdf_path)
text = ""
for page in doc:
text += page.get_text()
return text
def classify_topic(text: str, topics: List[str]) -> str:
if not text.strip():
return "Unknown (no text extracted)"
if not topics:
return "Unknown (no topics provided)"
classifier = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-3")
result = classifier(text[:1000], candidate_labels=topics)
if 'labels' in result and isinstance(result['labels'], list) and len(result['labels']) > 0:
return result['labels'][0]
return "Unknown (classification failed)"
def generate_audio(text: str, output_path: str):
try:
tts = gTTS(text)
tts.save(output_path)
except Exception as e:
raise RuntimeError(f"Audio generation failed: {str(e)}")
# ---------- STREAMLIT UI ----------
st.set_page_config(page_title="Research Paper Summarizer", layout="centered")
st.title("📄 AI Research Paper Summarizer")
st.markdown("""
Upload a research paper (PDF) and a list of topics. The app will:
1. Extract and summarize the paper
2. Classify it into a topic
3. Generate an audio summary 🎧
""")
with st.form("upload_form"):
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
topic_input = st.text_input("Enter comma-separated topics")
submitted = st.form_submit_button("Summarize and Generate Audio")
if submitted and uploaded_file and topic_input:
with st.spinner("Processing paper..."):
try:
temp_dir = tempfile.mkdtemp()
file_path = os.path.join(temp_dir, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.read())
text = extract_text_from_pdf(file_path)
st.info(f"Extracted text length: {len(text)} characters")
if not text.strip():
st.error("❌ No text could be extracted from the PDF. Try another file.")
else:
topic_list = [t.strip() for t in topic_input.split(",") if t.strip()]
classified_topic = classify_topic(text, topic_list)
summary = summarize_text(text)
st.markdown(f"### 🧠 Classified Topic: `{classified_topic}`")
st.markdown("### ✍️ Summary:")
st.write(summary)
audio_path = os.path.join(temp_dir, "summary.mp3")
generate_audio(summary, audio_path)
st.markdown("### 🔊 Audio Summary")
st.audio(audio_path)
st.success("Done! Audio summary is ready.")
except Exception as e:
st.error(f"❌ Error: {str(e)}") |