Update app.py

app.py

@@ -2,6 +2,11 @@ import torch
 import gradio as gr
 from transformers import pipeline
 
+# Add version control
+import pkg_resources
+gradio_version = pkg_resources.get_distribution("gradio").version
+print(f"Gradio version: {gradio_version}")
+
 MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
 
@@ -20,7 +25,6 @@ def transcribe(audio_file, task):
 
     result = pipe(audio_file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
     
-    # Format the output with timestamps
     output = ""
     for chunk in result["chunks"]:
         start_time = chunk["timestamp"][0]
@@ -35,14 +39,16 @@ def format_timestamp(seconds):
     hours, minutes = divmod(minutes, 60)
     return f"{int(hours):02d}:{int(minutes):02d}:{seconds:.2f}"
 
+# Use specific Gradio components
+audio_input = gr.components.File(label="Audio file", file_types=["audio"])
+task_input = gr.components.Radio(["transcribe", "translate"], label="Task", default="transcribe")
+output = gr.components.Textbox(label="Transcription with Timestamps")
+
 demo = gr.Interface(
     fn=transcribe,
-    inputs=[
-        gr.File(label="Audio file", file_types=["audio"]),
-        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-    ],
-    outputs=gr.Textbox(label="Transcription with Timestamps"),
-    title="Whisper Large V3: Transcribe Audio with Timestamps",
+    inputs=[audio_input, task_input],
+    outputs=output,
+    title=f"Whisper Large V3: Transcribe Audio with Timestamps (Gradio v{gradio_version})",
     description=(
         f"Transcribe audio files with Whisper Large V3 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}). "
         "Upload an audio file and choose whether to transcribe or translate. "