Update app.py

app.py

@@ -87,19 +87,40 @@ def predict(input_img):
         prediction = MMS.inverse_transform(res.cpu().numpy()).flatten()
         return prediction
 
-# Function to generate HTML for map
-def create_map_html(lat, lon):
-    m = folium.Map(location=[lat, lon], zoom_start=12)
-    folium.Marker([lat, lon]).add_to(m)
-    data = BytesIO()
-    m.save(data, close_file=False)
-    return data.getvalue().decode()
+# Function to generate Plotly map figure
+def create_map_figure(lat, lon):
+    fig = go.Figure(go.Scattermapbox(
+        lat=[lat],
+        lon=[lon],
+        mode='markers',
+        marker=go.scattermapbox.Marker(
+            size=14
+        ),
+        text=[f'Lat: {lat}, Lon: {lon}'],
+        hoverinfo='text'
+    ))
+
+    fig.update_layout(
+        mapbox_style="open-street-map",
+        hovermode='closest',
+        mapbox=dict(
+            bearing=0,
+            center=go.layout.mapbox.Center(
+                lat=lat,
+                lon=lon
+            ),
+            pitch=0,
+            zoom=10
+        ),
+    )
+
+    return fig
 
 # Create label output function
 def create_label_output(predictions):
     lat, lon = predictions
-    map_html = create_map_html(lat, lon)
-    return f"<div><h3>Predicted coordinates: ({lat:.6f}, {lon:.6f})</h3>{map_html}</div>"
+    fig = create_map_figure(lat, lon)
+    return fig
 
 # Predict and plot function
 def predict_and_plot(input_img):
@@ -107,13 +128,12 @@ def predict_and_plot(input_img):
     return create_label_output(predictions)
 
 # Gradio app definition
-gradio_app = Interface(
-    fn=predict_and_plot,
-    inputs=Image(label="Upload an Image", type="pil"),
-    examples=["GB.PNG", "IT.PNG", "NL.PNG", "NZ.PNG"],             
-    outputs=HTML(),
-    title="Predict the Location of this Image"
-)
+with gr.Blocks() as gradio_app:
+    with gr.Column():
+        input_image = gr.Image(label="Upload an Image", type="pil")
+        output_map = gr.Plot(label="Predicted Location on Map")
+        btn_predict = gr.Button("Predict")
+
+        btn_predict.click(predict_and_plot, inputs=input_image, outputs=output_map)
 
-if __name__ == "__main__":
     gradio_app.launch()