Update app.py

app.py

@@ -51,23 +51,45 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
 def make_prediction(image, taxonomic_level):
     # Preprocess the image
     img_array = load_and_preprocess_image(image)
-    
+
     # Get the class names from the 'species' column
-    class_names = sorted(taxo_df['species'].unique())  # Add this line to define class_names
-    
+    class_names = sorted(taxo_df['species'].unique())
+
     # Make a prediction
     prediction = model.predict(img_array)
-    
-    # Aggregate predictions based on the selected taxonomic level
-    aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_level, class_names)
-    
+
+    # Initialize the current taxonomic level index based on the user selection
+    current_taxonomic_level_index = taxonomic_levels.index(taxonomic_level)
+
+    # If the user chose to let the model decide, check the confidence levels
+    if taxonomic_level == "No, I will let the model decide":
+        aggregated_predictions = prediction
+        while current_taxonomic_level_index < len(taxonomic_levels):
+            # Aggregate predictions based on the current taxonomic level
+            aggregated_predictions, aggregated_class_labels = aggregate_predictions(
+                aggregated_predictions, taxonomic_levels[current_taxonomic_level_index], class_names
+            )
+
+            # Check if the max confidence in the aggregated predictions is >= 0.80
+            if np.max(aggregated_predictions) >= 0.80:
+                break
+
+            # Move to the next higher taxonomic level
+            current_taxonomic_level_index += 1
+
+        # Update the taxonomic level for output
+        taxonomic_level = taxonomic_levels[current_taxonomic_level_index]
+    else:
+        # Aggregate predictions based on the selected taxonomic level
+        aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_level, class_names)
+
     # Get the top 5 predictions
     top_indices = np.argsort(aggregated_predictions[0])[-5:][::-1]
-    
+
     # Get predicted class for the top prediction
     predicted_class_index = np.argmax(aggregated_predictions)
     predicted_class_name = aggregated_class_labels[predicted_class_index]
-    
+
     # Check if common name should be displayed (only at species level)
     if taxonomic_level == "species":
         predicted_common_name = taxo_df[taxo_df[taxonomic_level] == predicted_class_name]['common_name'].values[0]
@@ -77,10 +99,10 @@ def make_prediction(image, taxonomic_level):
 
     # Add the top 5 predictions
     output_text += "<h4 style='font-weight: bold; font-size: 1.2em;'>Top 5 Predictions:</h4>"
-    
+
     for i in top_indices:
         class_name = aggregated_class_labels[i]
-        
+
         if taxonomic_level == "species":
             # Display common names only at species level and make it italic
             common_name = taxo_df[taxo_df[taxonomic_level] == class_name]['common_name'].values[0]
@@ -94,7 +116,7 @@ def make_prediction(image, taxonomic_level):
             output_text += f"<div style='display: flex; justify-content: space-between;'>" \
                            f"<span>{class_name}</span>" \
                            f"<span style='margin-left: auto;'>{confidence_percentage:.2f}%</span></div>"
-    
+
     return output_text
 
 # Define the Gradio interface
@@ -103,7 +125,7 @@ interface = gr.Interface(
     inputs=[
         gr.Image(type="pil", label="Upload Image"),  # Input type: Image (PIL format)
         gr.Radio(choices=["Yes, I want to specify the taxonomic level", "No, I will let the model decide"],
-                 label="Do you want to specify the taxonomic resolution for predictions?<br><br>NOTE: if you select 'No', the next drop-down menu will be bypassed",
+                 label="Do you want to specify the taxonomic resolution for predictions? If you select 'No', the next drop-down menu will be bypassed.",
                  value="No, I will let the model decide"),  # Radio button for taxonomic resolution choice
         gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")  # Dropdown for taxonomic level
     ],