Fix Gradio 5.x API endpoints - replace TabbedInterface with Blocks

app.py

@@ -81,51 +81,79 @@ Mean Longitude: {mean_lon:.6f} ± {std_lon:.6f}
     except Exception as e:
         return f"Error during prediction: {str(e)}"
 
-# Create the Gradio interface for simple prediction
-simple_interface = gr.Interface(
-    fn=predict_geolocation,
-    inputs=gr.Image(type="pil", label="Upload an image"),
-    outputs=gr.Textbox(label="Predicted Location", lines=4),
-    title="🗺️ PLONK: Global Visual Geolocation",
-    description="""
-    Upload an image and PLONK will predict where it was taken!
+# Create the Gradio app using Blocks for proper API support
+with gr.Blocks(title="PLONK: Around the World in 80 Timesteps") as demo:
+    gr.Markdown("# 🗺️ PLONK: Around the World in 80 Timesteps")
+    gr.Markdown("A generative approach to global visual geolocation. Upload an image and PLONK will predict where it was taken!")
     
-    This uses the PLONK_YFCC model trained on the YFCC100M dataset.
-    The model predicts latitude and longitude coordinates based on visual content.
-    
-    **Note**: This is running on CPU, so processing may take 300-500ms per image.
-    """,
-    examples=[
-        ["demo/examples/condor.jpg"],
-        ["demo/examples/Kilimanjaro.jpg"], 
-        ["demo/examples/pigeon.png"]
-    ] if any(Path("demo/examples").glob("*")) else None
-)
-
-# Create advanced interface with sampling options
-advanced_interface = gr.Interface(
-    fn=predict_geolocation_with_samples,
-    inputs=[
-        gr.Image(type="pil", label="Upload an image"),
-        gr.Slider(1, 256, value=64, step=1, label="Number of samples"),
-        gr.Slider(0.0, 5.0, value=0.0, step=0.1, label="Guidance scale (CFG)")
-    ],
-    outputs=gr.Textbox(label="Detailed Results", lines=10),
-    title="🗺️ PLONK: Advanced Geolocation with Uncertainty",
-    description="""
-    Advanced interface showing prediction uncertainty through multiple samples.
-    
-    - **Number of samples**: More samples = better uncertainty estimation (but slower)
-    - **Guidance scale**: Higher values = more confident predictions (try 2.0 for best single guess)
-    """,
-)
-
-# Create tabbed interface
-demo = gr.TabbedInterface(
-    [simple_interface, advanced_interface],
-    ["Simple Prediction", "Advanced Analysis"],
-    title="PLONK: Around the World in 80 Timesteps"
-)
+    with gr.Tabs():
+        with gr.TabItem("Simple Prediction"):
+            gr.Markdown("""
+            ### 🗺️ PLONK: Global Visual Geolocation
+            
+            Upload an image and PLONK will predict where it was taken!
+            
+            This uses the PLONK_YFCC model trained on the YFCC100M dataset.
+            The model predicts latitude and longitude coordinates based on visual content.
+            
+            **Note**: This is running on CPU, so processing may take 300-500ms per image.
+            """)
+            
+            with gr.Row():
+                with gr.Column():
+                    image_input = gr.Image(type="pil", label="Upload an image")
+                    predict_btn = gr.Button("Predict Location", variant="primary")
+                
+                with gr.Column():
+                    output_text = gr.Textbox(label="Predicted Location", lines=4)
+            
+            # Add examples if they exist
+            if any(Path("demo/examples").glob("*")):
+                gr.Examples(
+                    examples=[
+                        ["demo/examples/condor.jpg"],
+                        ["demo/examples/Kilimanjaro.jpg"], 
+                        ["demo/examples/pigeon.png"]
+                    ],
+                    inputs=image_input,
+                    outputs=output_text,
+                    fn=predict_geolocation,
+                    cache_examples=False
+                )
+            
+            predict_btn.click(
+                fn=predict_geolocation,
+                inputs=image_input,
+                outputs=output_text,
+                api_name="predict"  # This creates the /api/predict endpoint
+            )
+        
+        with gr.TabItem("Advanced Analysis"):
+            gr.Markdown("""
+            ### 🗺️ PLONK: Advanced Geolocation with Uncertainty
+            
+            Advanced interface showing prediction uncertainty through multiple samples.
+            
+            - **Number of samples**: More samples = better uncertainty estimation (but slower)
+            - **Guidance scale**: Higher values = more confident predictions (try 2.0 for best single guess)
+            """)
+            
+            with gr.Row():
+                with gr.Column():
+                    adv_image_input = gr.Image(type="pil", label="Upload an image")
+                    samples_slider = gr.Slider(1, 256, value=64, step=1, label="Number of samples")
+                    cfg_slider = gr.Slider(0.0, 5.0, value=0.0, step=0.1, label="Guidance scale (CFG)")
+                    advanced_btn = gr.Button("Analyze with Uncertainty", variant="primary")
+                
+                with gr.Column():
+                    advanced_output = gr.Textbox(label="Detailed Results", lines=10)
+            
+            advanced_btn.click(
+                fn=predict_geolocation_with_samples,
+                inputs=[adv_image_input, samples_slider, cfg_slider],
+                outputs=advanced_output,
+                api_name="predict_advanced"  # This creates the /api/predict_advanced endpoint
+            )
 
 if __name__ == "__main__":
     demo.launch()