Update app.py

app.py

@@ -10,7 +10,7 @@ import os
 import tempfile
 import gradio.themes as gr_themes
 import csv
-from transformers import pipeline
+from transformers.pipelines import pipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 MODEL_NAME="nvidia/parakeet-tdt-0.6b-v2"
@@ -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):
     """
@@ -236,35 +236,33 @@ def get_full_transcript(vis_data):
         return ""
     return " ".join([row[2] for row in vis_data if len(row) == 3])
 
-# 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
+    # 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
 
 # Apply the custom theme