gradio fix

app.py

@@ -62,21 +62,25 @@ def predict(text):
     return "Positive" if predictions.item() == 1 else "Negative"
 '''
 # Create Gradio interface
-iface = gr.Interface(
-    fine_tune_model,
-    inputs=[
-        gr.inputs.Textbox(label="Model Name (e.g., 'google/t5-efficient-tiny-nh8')"),
-        gr.inputs.Textbox(label="Dataset Name (e.g., 'imdb')"),
-        gr.inputs.Textbox(label="HF hub to push to after training"),
-        gr.inputs.Slider(minimum=1, maximum=10, default=3, label="Number of Epochs"),
-        gr.inputs.Slider(minimum=1, maximum=16, default=4, label="Batch Size"),
-        gr.inputs.Slider(minimum=1, maximum=1000, default=50, label="Learning Rate (e-6)"),
-        gr.inputs.Slider(minimum=1, maximum=100, default=1, label="Gradient accumulation (e-1)"), 
-    ],
-    outputs="text",
-    title="Fine-Tune Hugging Face Model",
-    description="This interface allows you to fine-tune a Hugging Face model on a specified dataset."
-)
+try:
+    
+    iface = gr.Interface(
+        fine_tune_model,
+        inputs=[
+            gr.inputs.Textbox(label="Model Name (e.g., 'google/t5-efficient-tiny-nh8')"),
+            gr.inputs.Textbox(label="Dataset Name (e.g., 'imdb')"),
+            gr.inputs.Textbox(label="HF hub to push to after training"),
+            gr.inputs.Slider(minimum=1, maximum=10, default=3, label="Number of Epochs"),
+            gr.inputs.Slider(minimum=1, maximum=16, default=4, label="Batch Size"),
+            gr.inputs.Slider(minimum=1, maximum=1000, default=50, label="Learning Rate (e-6)"),
+            gr.inputs.Slider(minimum=1, maximum=100, default=1, label="Gradient accumulation (e-1)"), 
+        ],
+        outputs="text",
+        title="Fine-Tune Hugging Face Model",
+        description="This interface allows you to fine-tune a Hugging Face model on a specified dataset."
+    )
+except Exception as e:
+    print(f"An error occurred: {str(e)}, TB: {traceback.format_exc()}")
 
 # Launch the interface
 iface.launch()