Update app.py

app.py

@@ -1,31 +1,44 @@
 import gradio as gr
 from transformers import pipeline
+import traceback
 
 # 1) Load ASR pipeline
 asr = pipeline(
     "automatic-speech-recognition",
     model="tacab/ASR_SOMALI",
-    chunk_length_s=30,   # split long audio into 30 s chunks
-    device=-1            # use CPU; set to 0 if you enable GPU in your Space
+    chunk_length_s=30,   # split long audio into 30 s chunks if needed
+    device=-1            # CPU; set to 0 if you enable GPU in your Space settings
 )
 
 def transcribe(audio_filepath):
-    """Run the ASR model on the given WAV file path and return the text."""
-    result = asr(audio_filepath, return_timestamps=False)
-    return result["text"]
+    """
+    audio_filepath: local path to the recorded WAV.
+    Returns the transcription or an error message.
+    """
+    try:
+        result = asr(audio_filepath, return_timestamps=False)
+        return result["text"]
+    except Exception as e:
+        # log full stack trace to Space logs
+        traceback.print_exc()
+        # return a user-friendly error (you can also include str(e) if you want detail)
+        return f"Error during transcription:\n{e}"
 
 with gr.Blocks() as demo:
     gr.Markdown("## Tacab Somali ASR Module")
     with gr.Row():
         mic = gr.Audio(
             sources=["microphone"],    # allow recording from mic
-            type="filepath",           # give us a local file path
+            type="filepath",           # we get a local file path
             label="Record Somali Input"
         )
         btn = gr.Button("Transcribe")
-    txt_out = gr.Textbox(label="Transcription", interactive=False)
+    txt_out = gr.Textbox(
+        label="Transcription",
+        interactive=False,
+        placeholder="Your transcript will appear here…"
+    )
 
-    # wire up the button: take mic as input, produce txt_out
     btn.click(fn=transcribe, inputs=[mic], outputs=[txt_out])
 
 if __name__ == "__main__":