Create app.py

app.py

@@ -0,0 +1,49 @@
+
+def process_audio(audio_file, min_length, max_length):
+    try:
+
+      # Ensure audio_file is not None and has valid content
+      if audio_file is None:
+        raise ValueError("The audio file is missing or invalid.")
+
+      result = whisper_model.transcribe(audio_file)
+      text = result['text']
+      if not text:
+            raise ValueError("Failed to transcribe the audio. The transcription result is empty.")
+
+      summary_result = summarization(text, max_length=max_length, min_length=min_length)
+      summary = summary_result[0]['summary_text']
+      if not summary:
+            raise ValueError("Failed to summarize the transcript. The summary result is empty.")
+
+      df_results = pd.DataFrame({
+          "Audio File": [audio_file],
+          "Transcript": [text],
+          "Summary": [summary]
+      })
+
+      df_results.to_csv("results.csv", index=False)
+
+      return text, summary
+    except Exception as e:
+        # General error handling
+        error_message = f"An error occurred: {str(e)}"
+        print(error_message)  # Print the error for debugging
+        return error_message, error_message
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=[
+        gr.Audio(sources="upload", type="filepath", label="Upload your audio file"),
+        gr.Slider(minimum=5, maximum=50, value=30, label="Minimum Summary Length"),
+        gr.Slider(minimum=50, maximum=500, value=150, label="Maximum Summary Length")
+    ],
+    outputs=[
+        gr.Textbox(label="Transcript"),
+        gr.Textbox(label="Summary")
+    ],
+    title="Audio to Summarized Transcript",
+    description="Upload an audio file and adjust summary length to get both the transcript and summary."
+)
+
+iface.launch()