Update app.py

app.py

@@ -1,31 +1,122 @@
+import os
 import gradio as gr
-import jax
 import numpy as np
-# Import your JAX model here
+from PIL import Image
 
-def predict(x: float, y: float):
+# Placeholder for your actual model
+def generate_image(image_idx: int, x: float, y: float) -> Image.Image:
     """
-    Replace this with your actual JAX model inference
+    Replace this with your actual model inference
     """
     # This is just a placeholder - replace with your model
-    return {
-        "prediction": float(np.sin(x * 3.14) * np.cos(y * 3.14)),
-        "x": x,
-        "y": y
-    }
+    # Creating a simple gradient image as example output
+    width, height = 256, 256
+    gradient = np.zeros((height, width, 3), dtype=np.uint8)
+    gradient[:, :, 0] = np.linspace(0, 255 * x, width)
+    gradient[:, :, 1] = np.linspace(0, 255 * y, height)[:, np.newaxis]
+    gradient[:, :, 2] = image_idx * 30  # vary blue channel based on selected image
+    return Image.fromarray(gradient)
 
-# Create Gradio interface with API endpoint
-demo = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Number(label="X coordinate", minimum=0, maximum=1),
-        gr.Number(label="Y coordinate", minimum=0, maximum=1)
-    ],
-    outputs=gr.JSON(),
-    title="Research Visualization ML Model",
-    description="Click on the square to generate predictions",
-    allow_flagging="never"
-)
+def process_click(image_idx: int, evt: gr.SelectData) -> Image.Image:
+    """
+    Process the click event on the coordinate selector
+    """
+    # Extract coordinates from click event
+    x, y = evt.index[0], evt.index[1]
+    # Normalize coordinates to [0, 1]
+    x, y = x/100, y/100
+    # Generate image using the model
+    return generate_image(image_idx, x, y)
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # Interactive Image Generation
+    Choose a reference image and click on the coordinate selector to generate a new image.
+    """)
+    
+    with gr.Row():
+        # Left column: Reference images and coordinate selector
+        with gr.Column(scale=1):
+            # Radio buttons for image selection
+            image_idx = gr.Radio(
+                choices=[i for i in range(4)],  # Replace with your actual number of images
+                value=0,
+                label="Select Reference Image",
+                type="index"
+            )
+            
+            # Display reference images
+            gallery = gr.Gallery(
+                value=[
+                    "image_0.jpg",
+                    "image_0.jpg",
+                    "image_0.jpg",
+                    "image_0.jpg",
+                ],
+                columns=2,
+                rows=2,
+                height=300,
+                label="Reference Images"
+            )
+            
+            # Coordinate selector (displayed as heatmap for click interaction)
+            coord_selector = gr.Plot(
+                value=None,
+                label="Click to select (x, y) coordinates"
+            )
+            
+            # Initialize the coordinate selector
+            def create_selector():
+                import plotly.graph_objects as go
+                fig = go.Figure()
+                
+                # Add a square shape
+                fig.add_trace(go.Scatter(
+                    x=[0, 100, 100, 0, 0],
+                    y=[0, 0, 100, 100, 0],
+                    mode='lines',
+                    line=dict(color='black'),
+                    showlegend=False
+                ))
+                
+                # Update layout
+                fig.update_layout(
+                    width=300,
+                    height=300,
+                    margin=dict(l=0, r=0, t=0, b=0),
+                    xaxis=dict(
+                        range=[-5, 105],
+                        showgrid=False,
+                        zeroline=False,
+                        visible=False
+                    ),
+                    yaxis=dict(
+                        range=[-5, 105],
+                        showgrid=False,
+                        zeroline=False,
+                        visible=False,
+                        scaleanchor='x'
+                    ),
+                    plot_bgcolor='white'
+                )
+                return fig
+            
+            # Initialize the coordinate selector
+            coord_selector.value = create_selector()
+        
+        # Right column: Generated image
+        with gr.Column(scale=1):
+            output_image = gr.Image(
+                label="Generated Output",
+                height=300
+            )
+    
+    # Handle click events
+    coord_selector.select(
+        process_click,
+        inputs=[image_idx],
+        outputs=output_image
+    )
 
-# Enable API endpoint
+# Launch the app
 demo.launch()