Update app.py

app.py

@@ -29,8 +29,7 @@ device = 'cpu'
 le = LabelEncoder()
 le = joblib.load("SVD/le.gz")
 len_classes = len(le.classes_) + 1
-# Global variable to store predictions for dynamic zoom
-global_predictions = None
+
 
 class ModelPre(torch.nn.Module):
     def __init__(self):
@@ -147,7 +146,7 @@ def create_label_output(predictions):
     results, cell_ids = predictions
     fig = create_map_figure(results, cell_ids)
     return fig
-
+    
 def predict_and_plot(input_img, selected_prediction):
     predictions = predict(input_img)
 
@@ -166,15 +165,12 @@ def predict_and_plot(input_img, selected_prediction):
 with gr.Blocks() as gradio_app:
     with gr.Column():
         input_image = gr.Image(label="Upload an Image", type="pil")
+        selected_prediction = gr.Dropdown(choices=[f"Prediction {i+1}" for i in range(10)], label="Select Prediction to Zoom", value=None)
         output_map = gr.Plot(label="Predicted Location on Map")
         btn_predict = gr.Button("Predict")
-        selected_prediction = gr.Dropdown(choices=[f"Prediction {i+1}" for i in range(10)], label="Select Prediction to Zoom", value=None)
-
-        # Perform the prediction and plot the initial map
-        btn_predict.click(predict, inputs=input_image, outputs=output_map)
 
-        # Allow the user to zoom in on a selected prediction after the prediction is made
-        selected_prediction.change(zoom_on_prediction, inputs=selected_prediction, outputs=output_map)
+        # Update click function to include selected prediction
+        btn_predict.click(predict_and_plot, inputs=[input_image, selected_prediction], outputs=output_map)
 
         examples = ["GB.PNG", "IT.PNG", "NL.PNG", "NZ.PNG"]
         gr.Examples(examples=examples, inputs=input_image)