Update app.py

app.py

@@ -21,8 +21,8 @@ def get_class_name(predicted_class, taxonomic_level):
     unique_labels = sorted(taxo_df[taxonomic_level].unique())
     return unique_labels[predicted_class]
 
-# Function to aggregate predictions to the specified taxonomic level
-def aggregate_predictions(predicted_probs, taxonomic_level):
+# Function to aggregate predictions to a higher taxonomic level
+def aggregate_predictions(predicted_probs, taxonomic_level, class_names):
     unique_labels = sorted(taxo_df[taxonomic_level].unique())
     aggregated_predictions = np.zeros((predicted_probs.shape[0], len(unique_labels)))
 
@@ -31,7 +31,7 @@ def aggregate_predictions(predicted_probs, taxonomic_level):
         higher_level = row[taxonomic_level]
         
         species_index = class_names.index(species)  # Index of the species in the prediction array
-        higher_level_index = unique_labels.index(higher_level)  # Index of the higher taxonomic level
+        higher_level_index = unique_labels.index(higher_level)
         
         aggregated_predictions[:, higher_level_index] += predicted_probs[:, species_index]
     
@@ -47,24 +47,22 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
     img_array = preprocess_input(img_array)
     return img_array
 
-# Function to make predictions at the selected taxonomic level
+# Function to make predictions
 def make_prediction(image, taxonomic_level):
     # Preprocess the image
     img_array = load_and_preprocess_image(image)
-    # Make a prediction at the species level
+    # Make a prediction
     prediction = model.predict(img_array)
     
-    # Aggregate predictions to the selected taxonomic level
-    aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_level)
+    # 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]  # Indices of top 5 predictions
+    top_indices = np.argsort(aggregated_predictions[0])[-5:][::-1]
     
-    # Get the predicted class and common name for the top prediction
+    # Get predicted class and common name for the top prediction
     predicted_class_index = np.argmax(aggregated_predictions)
     predicted_class_name = aggregated_class_labels[predicted_class_index]
-    
-    # Get common name for the top predicted class
     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
     
@@ -76,10 +74,11 @@ def make_prediction(image, taxonomic_level):
         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>"
-    
+
     return output_text
 
 # Define the Gradio interface
@@ -88,7 +87,7 @@ interface = gr.Interface(
     inputs=[gr.Image(type="pil"),   # Input type: Image (PIL format)
             gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")],  # Use 'value' instead of 'default'
     outputs="html",                # Output type: HTML for formatting
-    title="Amazon Arboreal Species Classification",
+    title="Amazon arboreal species classification",
     description="Upload an image and select the taxonomic level to classify the species."
 )