cmmit

app.py

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+import streamlit as st
+from transformers import BartForConditionalGeneration, BartTokenizer
+from youtube_transcript_api import YouTubeTranscriptApi
+
+# Load BART model and tokenizer
+model_name = 'facebook/bart-large-cnn'
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+@st.cache
+def get_transcript(url):
+    try:
+        video_id = url.split('=')[1]
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
+        transcript_text = ""
+        for item in transcript_list:
+            transcript_text += item['text'] + "\n"
+        return transcript_text
+    except Exception as e:
+        return "Error fetching transcript: " + str(e)
+
+@st.cache
+def summarize_transcript(transcript):
+    input_ids = tokenizer.encode("summarize: " + transcript, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(input_ids, num_beams=4, min_length=30, max_length=200, early_stopping=True)
+    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)
+
+if __name__ == "__main__":
+    main()