Update app.py

app.py

@@ -1,26 +1,77 @@
-with st.spinner("Processing paper..."):
-    try:
-        temp_dir = tempfile.mkdtemp()
-        file_path = os.path.join(temp_dir, uploaded_file.name)
+# app.py (Streamlit-only version for Hugging Face Spaces)
 
-        with open(file_path, "wb") as f:
-            f.write(uploaded_file.read())
+import os
+import tempfile
+from typing import List
 
-        text = extract_text_from_pdf(file_path)
-        topic_list = [t.strip() for t in topic_input.split(",") if t.strip()]
-        classified_topic = classify_topic(text, topic_list)
-        summary = summarize_text(text)
+import fitz  # PyMuPDF
+import requests
+from transformers import pipeline
+from gtts import gTTS
+import streamlit as st
 
-        st.markdown(f"### 🧠 Classified Topic: `{classified_topic}`")
-        st.markdown("### ✍️ Summary:")
-        st.write(summary)
+# ---------- CONFIG ----------
+def summarize_text(text: str) -> str:
+    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+    return summarizer(text, max_length=200, min_length=30, do_sample=False)[0]['summary_text']
 
-        audio_path = os.path.join(temp_dir, "summary.mp3")
-        generate_audio(summary, audio_path)
+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
 
-        st.markdown("### 🔊 Audio Summary")
-        st.audio(audio_path)
-        st.success("Done! Audio summary is ready.")
+def classify_topic(text: str, topics: List[str]) -> str:
+    classifier = pipeline("zero-shot-classification", model="valhalla/distilbart-mnli-12-3")
+    result = classifier(text[:1000], candidate_labels=topics)
+    return result['labels'][0]
+
+def generate_audio(text: str, output_path: str):
+    tts = gTTS(text)
+    tts.save(output_path)
+
+# ---------- 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)
+            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)}")
 
-    except Exception as e:
-        st.error(f"❌ Error: {str(e)}")