Update app.py

app.py

@@ -21,7 +21,7 @@ model.eval()
 
 # Load the summarization model once at startup
 #summarizer = pipeline("summarization", model="Falconsai/text_summarization", device="cpu")
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+#summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 
 def get_audio_segment(audio_path, start_second, end_second):
     """
@@ -239,32 +239,32 @@ def get_full_transcript(vis_data):
 # Simple summary function (replace with a real model if needed)
 # Replace the old summarize_transcript function with this one
 @spaces.GPU
-def summarize_transcript(transcript: str) -> str:
+#def summarize_transcript(transcript: str) -> str:
     """
     Summarizes the transcript using the sshleifer/distilbart-cnn-12-6 model.
     """
     # Check for empty or whitespace-only input
-    if not transcript or not transcript.strip():
-        return "No transcript available to summarize."
-    
-    try:
-        gr.Info("Generating summary...", duration=2)
-        # Use the pre-loaded summarizer object to generate the summary
-        result = summarizer(
-            transcript, 
-            max_length=250, 
-            min_length=50, 
-            num_beams=4, 
-            early_stopping=True
-        )
-        # Extract the summary text from the result
-        summary = result[0]['summary_text']
-        return summary
-    except Exception as e:
-        error_message = f"An error occurred during summarization: {e}"
-        print(error_message) # Log the error to the console for debugging
-        gr.Warning("Sorry, the summary could not be generated at this time.")
-        return "" # Return an empty string on failure
+#    if not transcript or not transcript.strip():
+#        return "No transcript available to summarize."
+#    
+#    try:
+#        gr.Info("Generating summary...", duration=2)
+#        # Use the pre-loaded summarizer object to generate the summary
+#        result = summarizer(
+#            transcript, 
+#            max_length=250, 
+#            min_length=50, 
+#            num_beams=4, 
+#           early_stopping=True
+#        )
+#        # Extract the summary text from the result
+#        summary = result[0]['summary_text']
+#        return summary
+#    except Exception as e:
+#        error_message = f"An error occurred during summarization: {e}"
+#        print(error_message) # Log the error to the console for debugging
+#        gr.Warning("Sorry, the summary could not be generated at this time.")
+#        return "" # Return an empty string on failure
 
 # Apply the custom theme