Update app.py

app.py

@@ -1,35 +1,39 @@
-# app.py
+# app.py (Combined FastAPI + Streamlit UI)
 
 import os
 import uuid
 import tempfile
 from typing import List
-from fastapi import FastAPI, UploadFile, File, Form
-from fastapi.responses import FileResponse
-from pydantic import BaseModel
+
 import fitz  # PyMuPDF
 import requests
 import openai
 from transformers import pipeline
-import torch
 from gtts import gTTS
 import shutil
 
+import streamlit as st
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import FileResponse
+from fastapi.middleware.wsgi import WSGIMiddleware
+from starlette.responses import Response
+from starlette.routing import Mount
+from starlette.applications import Starlette
+from starlette.middleware.cors import CORSMiddleware
+from starlette.staticfiles import StaticFiles
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.wsgi import WSGIMiddleware
+import uvicorn
+
 # ---------- CONFIG ----------
-openai.api_key = os.getenv("OPENAI_API_KEY")  # Set this as HF secret
+openai.api_key = os.getenv("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")
 
 def summarize_text(text: str) -> str:
     summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
     return summarizer(text, max_length=200, min_length=30, do_sample=False)[0]['summary_text']
 
-# ---------- FASTAPI SETUP ----------
-app = FastAPI(title="Research Paper Summarization App")
-
-class SummaryRequest(BaseModel):
-    topic: str
-    urls: List[str] = []
-
-# ---------- HELPERS ----------
 def extract_text_from_pdf(pdf_path: str) -> str:
     doc = fitz.open(pdf_path)
     text = ""
@@ -46,9 +50,10 @@ def generate_audio(text: str, output_path: str):
     tts = gTTS(text)
     tts.save(output_path)
 
-# ---------- ROUTES ----------
+# ---------- FASTAPI BACKEND ----------
+fastapi_app = FastAPI()
 
-@app.post("/upload")
+@fastapi_app.post("/upload")
 def upload_paper(file: UploadFile = File(...), topics: str = Form(...)):
     temp_dir = tempfile.mkdtemp()
     file_path = os.path.join(temp_dir, file.filename)
@@ -63,31 +68,52 @@ def upload_paper(file: UploadFile = File(...), topics: str = Form(...)):
     audio_path = os.path.join(temp_dir, "summary.mp3")
     generate_audio(summary, audio_path)
 
-    result = {
-        "topic": classified_topic,
-        "summary": summary,
-        "audio_file": audio_path
-    }
-
     return FileResponse(audio_path, media_type="audio/mpeg", filename="summary.mp3")
 
-@app.post("/summarize_urls")
-def summarize_from_urls(req: SummaryRequest):
-    summaries = []
-    for url in req.urls:
-        try:
-            response = requests.get(url)
-            if not response.ok:
-                continue
-            text = response.text
-            summary = summarize_text(text)
-            summaries.append(summary)
-        except Exception as e:
-            summaries.append(f"Error: {str(e)}")
-
-    combined_summary = "\n".join(summaries)
-    return {"topic": req.topic, "combined_summary": combined_summary}
-
-@app.get("/")
-def root():
-    return {"message": "Welcome to the Research Paper Summarization API!"}
+# ---------- STREAMLIT UI ----------
+def 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..."):
+            files = {"file": (uploaded_file.name, uploaded_file, "application/pdf")}
+            data = {"topics": topic_input}
+
+            response = requests.post("http://localhost:8000/upload", files=files, data=data)
+
+            if response.status_code == 200:
+                audio_path = "summary.mp3"
+                with open(audio_path, "wb") as f:
+                    f.write(response.content)
+                st.audio(audio_path)
+                st.success("Audio summary generated!")
+            else:
+                st.error("Something went wrong during processing.")
+
+# ---------- ENTRY POINT ----------
+if __name__ == "__main__":
+    import threading
+    from multiprocessing import Process
+
+    def run_api():
+        uvicorn.run(fastapi_app, host="0.0.0.0", port=8000)
+
+    api_process = Process(target=run_api)
+    api_process.start()
+
+    streamlit_ui()
+
+    api_process.terminate()