Fifth commit

app.py

@@ -248,62 +248,60 @@ def train_model(
 
 # Create Gradio interface
 def create_interface():
-    demo = gr.Interface(
-        # Configure Gradio to handle larger file uploads
-        upload_size_limit=100
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            file_input = gr.File(
-                label="Upload Training Data (CSV)",
-                type="binary",
-                file_types=[".csv"]
-            )
-            
-            learning_rate = gr.Slider(
-                minimum=1e-5,
-                maximum=1e-3,
-                value=2e-4,
-                label="Learning Rate"
-            )
-            
-            num_epochs = gr.Slider(
-                minimum=1,
-                maximum=10,
-                value=3,
-                step=1,
-                label="Number of Epochs"
-            )
-            
-            batch_size = gr.Slider(
-                minimum=1,
-                maximum=8,
-                value=4,
-                step=1,
-                label="Batch Size"
-            )
-            
-            train_button = gr.Button("Start Training")
-            
-        with gr.Column():
-            output = gr.Textbox(label="Training Status")
-            
-    train_button.click(
-        fn=train_model,
-        inputs=[file_input, learning_rate, num_epochs, batch_size],
-        outputs=output
-    )
-    
-    gr.Markdown("""
-    ## Instructions
-    1. Upload your training data in CSV format with columns:
-       - chunk_id (questions)
-       - text (answers)
-    2. Adjust training parameters if needed
-    3. Click 'Start Training'
-    4. Wait for training to complete
-    """)
+    # Configure Gradio to handle larger file uploads
+    gr.Config(upload_size_limit=100)
+        
+        with gr.Row():
+            with gr.Column():
+                file_input = gr.File(
+                    label="Upload Training Data (CSV)",
+                    type="binary",
+                    file_types=[".csv"]
+                )
+                
+                learning_rate = gr.Slider(
+                    minimum=1e-5,
+                    maximum=1e-3,
+                    value=2e-4,
+                    label="Learning Rate"
+                )
+                
+                num_epochs = gr.Slider(
+                    minimum=1,
+                    maximum=10,
+                    value=3,
+                    step=1,
+                    label="Number of Epochs"
+                )
+                
+                batch_size = gr.Slider(
+                    minimum=1,
+                    maximum=8,
+                    value=4,
+                    step=1,
+                    label="Batch Size"
+                )
+                
+                train_button = gr.Button("Start Training")
+                
+            with gr.Column():
+                output = gr.Textbox(label="Training Status")
+                
+        train_button.click(
+            fn=train_model,
+            inputs=[file_input, learning_rate, num_epochs, batch_size],
+            outputs=output
+        )
+        
+        gr.Markdown("""
+        ## Instructions
+        1. Upload your training data in CSV format with columns:
+           - chunk_id (questions)
+           - text (answers)
+        2. Adjust training parameters if needed
+        3. Click 'Start Training'
+        4. Wait for training to complete
+        """)
     
     return demo
 
@@ -314,4 +312,4 @@ if __name__ == "__main__":
     
     # Launch Gradio interface
     demo = create_interface()
-    demo.launch(share=True)
+    demo.launch(share=True, server_port=7860, server_name="0.0.0.0", max_upload_size=100)