Update app.py

app.py

@@ -77,8 +77,12 @@ def make_prediction(image, taxonomic_decision, taxonomic_level):
         predicted_class_index = np.argmax(aggregated_predictions)
         predicted_class_name = aggregated_class_labels[predicted_class_index]
 
-        # Construct the output message without considering confidence
-        output_text = f"<h1 style='font-weight: bold;'>{predicted_class_name}</h1>"
+        # Check if common name should be displayed (only at species level)
+        if taxonomic_levels[current_level_index] == "species":
+            predicted_common_name = taxo_df[taxo_df[taxonomic_levels[current_level_index]] == predicted_class_name]['common_name'].values[0]
+            output_text = f"<h1 style='font-weight: bold;'><span style='font-style: italic;'>{predicted_class_name}</span> ({predicted_common_name})</h1>"
+        else:
+            output_text = f"<h1 style='font-weight: bold;'>{predicted_class_name}</h1>"
         
         # Add the top 5 predictions
         output_text += "<h4 style='font-weight: bold; font-size: 1.2em;'>Top-5 predictions:</h4>"
@@ -148,6 +152,60 @@ def make_prediction(image, taxonomic_decision, taxonomic_level):
     
     return output_text
 
+    # Confidence checking for the automatic model decision
+    # Loop through taxonomic levels if the user lets the model decide
+    while 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)
+        
+        # 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 top_prediction_confidence >= 0.80:
+            break  # Confidence threshold met, exit loop
+
+        current_level_index += 1  # Move to the next taxonomic level
+
+    # Check if a valid prediction was made
+    if current_level_index == len(taxonomic_levels):
+        return "<h1 style='font-weight: bold;'>Unknown animal</h1>"  # No valid predictions met the confidence criteria
+
+    # Get the predicted class name 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_levels[current_level_index] == "species":
+        predicted_common_name = taxo_df[taxo_df[taxonomic_levels[current_level_index]] == predicted_class_name]['common_name'].values[0]
+        output_text = f"<h1 style='font-weight: bold;'><span style='font-style: italic;'>{predicted_class_name}</span> ({predicted_common_name})</h1>"
+    else:
+        output_text = f"<h1 style='font-weight: bold;'>{predicted_class_name}</h1>"
+
+    # Add the top 5 predictions
+    output_text += "<h4 style='font-weight: bold; font-size: 1.2em;'>Top-5 predictions:</h4>"
+    
+    top_indices = np.argsort(aggregated_predictions[0])[-5:][::-1]  # Get top 5 predictions
+
+    for i in top_indices:
+        class_name = aggregated_class_labels[i]
+        
+        if taxonomic_levels[current_level_index] == "species":
+            # Display common names only at species level and make it italic
+            common_name = taxo_df[taxo_df[taxonomic_levels[current_level_index]] == class_name]['common_name'].values[0]
+            confidence_percentage = aggregated_predictions[0][i] * 100
+            output_text += f"<div style='display: flex; justify-content: space-between;'>" \
+                           f"<span style='font-style: italic;'>{class_name}</span>&nbsp;(<span>{common_name}</span>)" \
+                           f"<span style='margin-left: auto;'>{confidence_percentage:.2f}%</span></div>"
+        else:
+            # No common names at higher taxonomic levels
+            confidence_percentage = aggregated_predictions[0][i] * 100
+            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
 interface = gr.Interface(
     fn=make_prediction,  # Function to be called for predictions