Update app.py

app.py

@@ -51,36 +51,47 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
 def make_prediction(image, taxonomic_decision, 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())
-    
+
     # Make a prediction
     prediction = model.predict(img_array)
-    
+
     # Initialize variables for aggregated predictions and level index
     aggregated_predictions = None
     current_level_index = 0  # Start from the species level
-    
+
     # Determine the initial taxonomic level based on the user's decision
     if taxonomic_decision == "No, I will let the model decide":
         current_level_index = 0  # Start at species level if letting the model decide
     else:
         current_level_index = taxonomic_levels.index(taxonomic_level)  # Use specified level
 
-    # Loop through taxonomic levels to check confidence
+    # Aggregate predictions based on the current taxonomic level
+    aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_levels[current_level_index], class_names)
+
+    # Check if the confidence of the top prediction meets the threshold
+    top_prediction_index = np.argmax(aggregated_predictions)
+    top_prediction_confidence = aggregated_predictions[0][top_prediction_index]
+
+    # If the user specified a taxonomic level, do not automatically promote to a higher level
+    if taxonomic_decision == "Yes, I want to specify the taxonomic level":
+        if current_level_index == 0 and top_prediction_confidence < 0.80:
+            return "<h1 style='font-weight: bold;'>Confidence too low for specified taxonomic level</h1>"
+
+    # Proceed to the next level if the confidence threshold is not met and the user allows it
     while current_level_index < len(taxonomic_levels):
-        # Aggregate predictions based on the current taxonomic level
-        aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_levels[current_level_index], class_names)
-        
-        # Check if the confidence of the top prediction meets the threshold
-        top_prediction_index = np.argmax(aggregated_predictions)
-        top_prediction_confidence = aggregated_predictions[0][top_prediction_index]
-        
+        # Check if confidence is above the threshold at the current level
         if top_prediction_confidence >= 0.80:
             break  # Confidence threshold met, exit loop
-        
+
         current_level_index += 1  # Move to the next taxonomic level
+        if current_level_index < len(taxonomic_levels):
+            # Aggregate predictions for the next level
+            aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_levels[current_level_index], class_names)
+            top_prediction_index = np.argmax(aggregated_predictions)
+            top_prediction_confidence = aggregated_predictions[0][top_prediction_index]
 
     # Check if a valid prediction was made
     if current_level_index == len(taxonomic_levels):