Update app.py

app.py

@@ -2,25 +2,39 @@ import gradio as gr
 import os
 from transformers import pipeline
 
-# Load ASR pipeline
+# Load ASR (Speech-to-Text) pipeline
 asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en")
 
-# Define function to process audio input
-def transcribe(audio_file):
+# Load Summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Function to transcribe and summarize
+def transcribe_and_summarize(audio_file):
     if audio_file is None:
-        return "Error: No audio file provided."
-    
+        return "Error: No audio file provided.", ""
+
     try:
-        result = asr(audio_file)  # Process audio
-        return result['text']  # Extract the transcribed text
+        # Transcribe audio
+        transcription_result = asr(audio_file)
+        transcribed_text = transcription_result['text']
+
+        # Summarize the transcribed text
+        summary_result = summarizer(transcribed_text, max_length=100, min_length=30, do_sample=False)
+        summarized_text = summary_result[0]['summary_text']
+
+        return transcribed_text, summarized_text
+
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error: {str(e)}", ""
 
 # Create Gradio interface
 iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(type="filepath"),  # Expect an audio file path
-    outputs="text"
+    fn=transcribe_and_summarize,
+    inputs=gr.Audio(type="filepath"),  # Accepts an audio file
+    outputs=[
+        gr.Textbox(label="Transcribed Text"),  
+        gr.Textbox(label="Summarized Text")
+    ]
 )
 
 # Get port safely (default to 7860 if not set)