commit

app.py

@@ -1,12 +1,23 @@
 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:
@@ -17,8 +28,9 @@ def get_transcript(url):
             transcript_text += item['text'] + "\n"
         return transcript_text
     except Exception as e:
-        return "Error fetching transcript: " + str(e)
+        return None
 
+# Function to summarize transcript
 @st.cache
 def summarize_transcript(transcript):
     input_ids = tokenizer.encode("summarize: " + transcript, return_tensors="pt", max_length=1024, truncation=True)
@@ -26,19 +38,18 @@ def summarize_transcript(transcript):
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
-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)
+# 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)
 
+# Run Streamlit app
 if __name__ == "__main__":
-    main()
+    uvicorn.run(app, host="0.0.0.0", port=8000)