Update app.py

app.py

@@ -64,25 +64,37 @@ def make_prediction(image, taxonomic_level):
     # Get the top 5 predictions
     top_indices = np.argsort(aggregated_predictions[0])[-5:][::-1]
     
-    # Get predicted class and common name for the top prediction
+    # Get predicted class for the top prediction
     predicted_class_index = np.argmax(aggregated_predictions)
     predicted_class_name = aggregated_class_labels[predicted_class_index]
-    predicted_common_name = taxo_df[taxo_df[taxonomic_level] == predicted_class_name]['common_name'].values[0]
-    confidence = aggregated_predictions[0][predicted_class_index] * 100  # Confidence of the predicted class
     
-    # Create output text with HTML formatting
-    output_text = f"<h1 style='font-weight: bold;'><span style='font-style: italic;'>{predicted_class_name}</span> ({predicted_common_name})</h1>"
+    # 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]
+        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>"
     
     for i in top_indices:
         class_name = aggregated_class_labels[i]
-        common_name = taxo_df[taxo_df[taxonomic_level] == 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>"
-
+        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]
+            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