Update app.py

app.py

@@ -30,7 +30,6 @@ le = LabelEncoder()
 le = joblib.load("SVD/le.gz")
 len_classes = len(le.classes_) + 1
 
-
 class ModelPre(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -60,26 +59,16 @@ cmp = transforms.Compose([
 ])
 
 def predict(input_img):
-    global global_predictions
-    results, cell_ids = predict(input_img)
-    global_predictions = (results, cell_ids)  # Store predictions globally for zoom functionality
-    return create_map_figure(global_predictions, global_predictions[1])
-
-def zoom_on_prediction(selected_prediction):
-    global global_predictions
-    if global_predictions is None:
-        return None  # No prediction made yet
-    
-    # Convert dropdown selection into an index (Prediction 1 corresponds to index 0, etc.)
-    if selected_prediction is not None:
-        selected_index = int(selected_prediction.split()[-1]) - 1  # Extract index from "Prediction X"
-    else:
-        selected_index = None  # No selection, default view
-    
-    # Return the updated map with zoom
-    return create_map_figure(global_predictions, global_predictions[1], selected_index=selected_index)
-
-
+    with torch.inference_mode():
+        img = cmp(input_img).unsqueeze(0)
+        res = model(img.to(device))
+        probabilities = torch.softmax(res, dim=1).cpu().numpy().flatten()
+        top_10_indices = np.argsort(probabilities)[-10:][::-1]
+        top_10_probabilities = probabilities[top_10_indices]
+        top_10_predictions = le.inverse_transform(top_10_indices)
+        
+        results = {top_10_predictions[i]: float(top_10_probabilities[i]) for i in range(10)}
+        return results, top_10_predictions
 
 # Function to get S2 cell polygon
 def get_s2_cell_polygon(cell_id):
@@ -146,7 +135,7 @@ 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)
 
@@ -159,8 +148,6 @@ def predict_and_plot(input_img, selected_prediction):
     return create_map_figure(predictions, predictions[1], selected_index=selected_index)
 
 
-
-
 # Gradio app definition
 with gr.Blocks() as gradio_app:
     with gr.Column():