Update app.py

app.py

@@ -1,3 +1,102 @@
+# Import the libraries
+import numpy as np
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.applications.convnext import preprocess_input
+import gradio as gr
+
+# Load the model
+model = load_model('models/ConvNeXtBase_80_tresh_spp.tf')
+
+# Load the taxonomy .csv
+taxo_df = pd.read_csv('taxonomy/taxonomy_mapping.csv')
+taxo_df['species'] = taxo_df['species'].str.replace('_', ' ')
+
+# Available taxonomic levels
+taxonomic_levels = ['species', 'genus', 'family', 'order', 'class']
+
+# Function to map predicted class index to class name at the selected taxonomic level
+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 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)))
+
+    for idx, row in taxo_df.iterrows():
+        species = row['species']
+        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)
+        
+        aggregated_predictions[:, higher_level_index] += predicted_probs[:, species_index]
+    
+    return aggregated_predictions, unique_labels
+
+# Function to load and preprocess the image
+def load_and_preprocess_image(image, target_size=(224, 224)):
+    # Resize the image
+    img_array = img_to_array(image.resize(target_size))
+    # Expand the dimensions to match model input
+    img_array = np.expand_dims(img_array, axis=0)
+    # Preprocess the image
+    img_array = preprocess_input(img_array)
+    return img_array
+
+# Function to make predictions
+def make_prediction(image, 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())  # Add this line to define class_names
+    
+    # Make a prediction
+    prediction = model.predict(img_array)
+    
+    # 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]
+    
+    # Get predicted class 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_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]
+        
+        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
 interface = gr.Interface(
     fn=make_prediction,            # Function to be called for predictions
@@ -11,6 +110,6 @@ interface = gr.Interface(
 # Launch the Gradio interface with authentication for the specified users
 interface.launch(auth=[
     ("Andrea Zampetti", "andreazampetti"),
-    ("Luca Santini", "lucasantini"), 
+    ("Luca Santini", "lucasantini"),
     ("Ana Benítez López", "anabenítezlópez")
 ])