Update app.py

app.py

@@ -1,116 +1,16 @@
-# 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
-
-with gr.Blocks() as demo:
-    # Define the input and output components for predictions
-    image_input = gr.Image(type="pil", label="Upload Image")  # Input type: Image (PIL format)
-    taxonomic_level_input = gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")  # Dropdown for taxonomic level
-    output_html = gr.HTML(label="Prediction Result")  # Output type: HTML for formatting
-    
-    # Create the prediction button
-    predict_button = gr.Button("Make Prediction")
-
-    # Define what happens when the button is clicked
-    predict_button.click(make_prediction, inputs=[image_input, taxonomic_level_input], outputs=output_html)
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=make_prediction,            # Function to be called for predictions
+    inputs=[gr.Image(type="pil", label="Upload Image"),   # Input type: Image (PIL format)
+            gr.Dropdown(choices=taxonomic_levels, label="Taxonomic level", value="species")],  # Dropdown for taxonomic level
+    outputs="html",                # Output type: HTML for formatting
+    title="Amazon arboreal species classification",
+    description="Upload an image and select the taxonomic level to classify the species."
+)
 
 # Launch the Gradio interface with authentication for the specified users
-demo.launch(auth=[
+interface.launch(auth=[
+    ("Andrea Zampetti", "andreazampetti"),
     ("Luca Santini", "lucasantini"), 
     ("Ana Benítez López", "anabenítezlópez")
 ])