Update app.py

app.py

@@ -1,33 +1,62 @@
 import gradio as gr
+
+import os
+
+
+
 from transformers import pipeline
 
-# Whisper model
-asr = pipeline("automatic-speech-recognition", model="distil-whisper/distil-small.en")
+# Load ASR (Speech-to-Text) pipeline with timestamp handling
+asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en")
 
-# Summarization model
+
+
+
+# Load Summarization model
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-def process_audio(audio_path):
+# Function to transcribe and summarize audio
+def transcribe_and_summarize(audio_file):
+    if audio_file is None:
+        return "Error: No audio file provided.", ""
+
+
     try:
-        # Transcribe Audio
-        transcription = asr(audio_path, return_timestamps=True)
-        transcribed_text = transcription["text"]
+        # Transcribe audio (handling long-form audio)
+        transcription_result = asr(audio_file, return_timestamps=True)
+        
+        # Extract transcribed text
+        transcribed_text = " ".join([segment['text'] for segment in transcription_result['chunks']])
+
+        # Ensure the transcribed text isn't too short for summarization
+        if len(transcribed_text.split()) < 50:
+            summarized_text = "Text too short to summarize."
+        else:
+            # 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
+
+
 
-        # Summarize Transcription
-        summary = summarizer(transcribed_text, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
 
-        return transcribed_text, summary
     except Exception as e:
-        print(f"Error: {str(e)}")  # Log errors
         return f"Error: {str(e)}", ""
 
-# Gradio Interface
+# Create Gradio interface
+
 iface = gr.Interface(
-    fn=process_audio,
-    inputs=gr.Audio(source="upload", type="filepath"),
-    outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")],
-    title="Audio Summarizer",
-    description="Upload an audio file, and this app will transcribe and summarize its content.",
+    fn=transcribe_and_summarize,
+    inputs=gr.Audio(type="filepath"),  # Accepts an audio file
+    outputs=[
+        gr.Textbox(label="Transcribed Text"),  
+        gr.Textbox(label="Summarized Text")
+    ]
 )
 
-iface.launch()
+# Get port safely (default to 7860 if not set)
+port = int(os.environ.get('PORT1', 7860))
+
+# Launch Gradio app
+iface.launch(share=True, server_port=port)