Create app.py

app.py

@@ -0,0 +1,104 @@
+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)}")