Update app.py

app.py

@@ -1,23 +1,12 @@
 import streamlit as st
 from transformers import BartForConditionalGeneration, BartTokenizer
 from youtube_transcript_api import YouTubeTranscriptApi
-from fastapi import FastAPI, Request
-from fastapi.responses import JSONResponse
-import uvicorn
-
-# Initialize Streamlit app
-st.title("YouTube Video Transcription Summarizer")
-video_url = st.text_input("Enter YouTube Video URL:")
-
-# Initialize FastAPI app
-app = FastAPI()
 
 # Load BART model and tokenizer
 model_name = 'facebook/bart-large-cnn'
 tokenizer = BartTokenizer.from_pretrained(model_name)
 model = BartForConditionalGeneration.from_pretrained(model_name)
 
-# Function to fetch transcript from YouTube URL
 @st.cache
 def get_transcript(url):
     try:
@@ -28,9 +17,8 @@ def get_transcript(url):
             transcript_text += item['text'] + "\n"
         return transcript_text
     except Exception as e:
-        return None
+        return "Error fetching transcript: " + str(e)
 
-# Function to summarize transcript
 @st.cache
 def summarize_transcript(transcript):
     input_ids = tokenizer.encode("summarize: " + transcript, return_tensors="pt", max_length=1024, truncation=True)
@@ -38,18 +26,19 @@ def summarize_transcript(transcript):
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
-# API endpoint to summarize transcript
-@app.post("/summarize_transcript")
-async def summarize_transcript_api(request: Request):
-    data = await request.json()
-    video_url = data.get('video_url')
-    transcript = get_transcript(video_url)
-    if transcript:
-        summary = summarize_transcript(transcript)
-        return JSONResponse(content={"summary": summary})
-    else:
-        return JSONResponse(content={"error": "Error fetching transcript."}, status_code=400)
+def main():
+    st.title("YouTube Video Transcription Summarizer")
+
+    video_url = st.text_input("Enter YouTube Video URL:")
+    
+    if st.button("Summarize Transcript"):
+        transcript = get_transcript(video_url)
+        if not transcript:
+            st.error("Error fetching transcript.")
+        else:
+            summary = summarize_transcript(transcript)
+            st.subheader("Summary:")
+            st.write(summary)
 
-# Run Streamlit app
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    main()