Update app.py

app.py

@@ -16,6 +16,11 @@ 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())
@@ -42,8 +47,8 @@ def load_and_preprocess_image(image, target_size=(224, 224)):
     img_array = preprocess_input(img_array)
     return img_array
 
-# Function to make predictions when taxonomic level is specified
-def make_prediction_with_taxonomic_level(image, taxonomic_level):
+# Function to make predictions
+def make_prediction(image, taxonomic_level):
     # Preprocess the image
     img_array = load_and_preprocess_image(image)
     
@@ -78,12 +83,13 @@ def make_prediction_with_taxonomic_level(image, taxonomic_level):
         
         if taxonomic_level == "species":
             # Display common names only at species level and make it italic
-            common_name = taxo_df[taxo_level == class_name]['common_name'].values[0]
+            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>" \
@@ -91,79 +97,33 @@ def make_prediction_with_taxonomic_level(image, taxonomic_level):
     
     return output_text
 
-# Function to make predictions with automatic taxonomic resolution
-def make_prediction_auto(image):
-    # 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)
-    
-    # Start with species-level predictions
-    taxonomic_level = 'species'
-    aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_level, class_names)
-    
-    # Check confidence and move to higher taxonomic levels if necessary
-    predicted_class_index = np.argmax(aggregated_predictions)
-    confidence = aggregated_predictions[0][predicted_class_index]
-    
-    while confidence < 0.80 and taxonomic_levels.index(taxonomic_level) < len(taxonomic_levels) - 1:
-        # Move to the next higher taxonomic level
-        taxonomic_level = taxonomic_levels[taxonomic_levels.index(taxonomic_level) + 1]
-        aggregated_predictions, aggregated_class_labels = aggregate_predictions(prediction, taxonomic_level, class_names)
-        predicted_class_index = np.argmax(aggregated_predictions)
-        confidence = aggregated_predictions[0][predicted_class_index]
-
-    predicted_class_name = aggregated_class_labels[predicted_class_index]
-    
-    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>"
-
-    # Return the final prediction text
-    return output_text
-
-# Gradio function to handle the flag logic
-def make_prediction(image, choose_resolution, taxonomic_level):
-    if choose_resolution == "Yes, I want to specify the taxonomic level":
-        return make_prediction_with_taxonomic_level(image, taxonomic_level)
-    else:
-        return make_prediction_auto(image)
-
-# Function to dynamically disable/enable the taxonomic level dropdown
-def update_taxonomic_level_interface(choose_resolution):
-    if choose_resolution == "No, I will let the model decide":
-        return gr.Dropdown.update(interactive=False)
-    else:
-        return gr.Dropdown.update(interactive=True)
+# Define a function to update the welcome message based on the logged-in user
+def update_message(request: gr.Request):
+    return f"Welcome to the demo, Dr. {request.username}!"
 
 # Define the Gradio interface
-with gr.Blocks() as interface:
-    with gr.Row():
-        image_input = gr.Image(type="pil")
-        choose_resolution = gr.Radio(
-            choices=["Yes, I want to specify the taxonomic level", "No, I will let the model decide"], 
-            label="Do you want to choose the taxonomic resolution for predictions?", value="No, I will let the model decide"
-        )
-        taxonomic_level = gr.Dropdown(
-            choices=taxonomic_levels, label="Taxonomic level", value="species", interactive=True
-        )
-    
-    result_output = gr.HTML()
-
-    # Add a button to submit the image for prediction
-    predict_button = gr.Button("Predict")
-
-    # Bind the dynamic dropdown control
-    choose_resolution.change(fn=update_taxonomic_level_interface, inputs=choose_resolution, outputs=taxonomic_level)
-
-    # Bind the button click to the prediction function
-    predict_button.click(fn=make_prediction, inputs=[image_input, choose_resolution, taxonomic_level], outputs=result_output)
-
-# Launch the Gradio interface
-interface.launch()
+with gr.Blocks() as demo:
+    # Add a Markdown component for displaying the welcome message
+    welcome_message = gr.Markdown()
+    
+    # Load the update_message function to display the welcome message
+    demo.load(update_message, None, welcome_message)
+
+    # Define the main interface for predictions
+    interface = gr.Interface(
+        fn=make_prediction,            # Function to be called for predictions
+        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",
+        description="Upload an image and select the taxonomic level to classify the species."
+    )
+
+    # Add the prediction interface to the main demo
+    interface.render()
+
+# Launch the Gradio interface with authentication for the specified users
+demo.launch(auth=[
+    ("Luca Santini", "lucasantini"), 
+    ("Ana Benítez López", "anabenítezlópez")
+])