Update app.py

app.py

@@ -49,73 +49,55 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
 
 # Function to make predictions
 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
-    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]
+    try:
+        # Preprocess the image
+        img_array = load_and_preprocess_image(image)
         
-        if top_prediction_confidence >= 0.80:
-            break  # Confidence threshold met, exit loop
+        # Get the class names from the 'species' column
+        class_names = sorted(taxo_df['species'].unique())
         
-        current_level_index += 1  # Move to the next taxonomic level
+        # Make a prediction
+        prediction = model.predict(img_array)
 
-    # 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
+        # Check the shape of predictions
+        print(f"Prediction shape: {prediction.shape}")
 
-    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>"
+        # 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
         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>"
+            current_level_index = taxonomic_levels.index(taxonomic_level)
+
+        # Loop through taxonomic levels
+        while current_level_index < len(taxonomic_levels):
+            aggregated_predictions, aggregated_class_labels = aggregate_predictions(
+                prediction, taxonomic_levels[current_level_index], class_names
+            )
+            
+            # Check if predictions are valid
+            if aggregated_predictions is None or aggregated_predictions.size == 0:
+                return "<h2>No valid predictions available.</h2>"
+
+            top_prediction_index = np.argmax(aggregated_predictions)
+            top_prediction_confidence = aggregated_predictions[0][top_prediction_index]
+
+            if top_prediction_confidence >= 0.80:
+                break
+
+            current_level_index += 1  # Move to the next taxonomic level
+
+        # 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]
+        
+        # Continue with generating output text...
     
-    return output_text
+    except Exception as e:
+        return f"<h2>Error during prediction: {str(e)}</h2>"
 
 # Define the Gradio interface
 interface = gr.Interface(