Update app.py

app.py

@@ -7,9 +7,65 @@ from tensorflow.keras.preprocessing.image import load_img, img_to_array # type:
 from tensorflow.keras.applications.convnext import preprocess_input # type: ignore
 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', sep=';')
+taxo_df['species'] = taxo_df['species'].str.replace('_', ' ')
+
+# Extract unique class names from the 'species' column
+class_names = sorted(taxo_df['species'].unique())
+
+# Function to map predicted class index to class name
+def get_class_name(predicted_class):
+    return class_names[predicted_class]
+
+# Function to load and preprocess the image
+def load_and_preprocess_image(image, target_size=(224, 224)):
+    # Resize the image (assuming image is a PIL image)
+    img_array = img_to_array(image.resize(target_size))
+    # Expand the dimensions of the array to match model input
+    img_array = np.expand_dims(img_array, axis=0)
+    # Preprocess using the appropriate function (for example, ResNet50)
+    img_array = preprocess_input(img_array)
+    return img_array
+
+# Function to make predictions
+def make_prediction(image):
+    # Preprocess the image
+    img_array = load_and_preprocess_image(image)
+    # Make a prediction
+    prediction = model.predict(img_array)
+    
+    # Get the top 5 predictions
+    top_indices = np.argsort(prediction[0])[-5:][::-1]  # Get indices of top 5 classes
+    
+    # Get predicted class and common name for the top prediction
+    predicted_class_index = np.argmax(prediction)
+    predicted_class_name = get_class_name(predicted_class_index)
+    predicted_common_name = taxo_df[taxo_df['species'] == predicted_class_name]['common_name'].values[0]  # Get common name
+    confidence = prediction[0][predicted_class_index] * 100  # Confidence of the predicted class
+    
+    # Create output text with HTML formatting
+    output_text = f"<h1 style='font-weight: bold;'><span style='font-style: italic;'>{predicted_class_name}</span> ({predicted_common_name})</h1>"  # Large bold for predicted class, italic for class name
+    output_text += "<h4 style='font-weight: bold; font-size: 1.2em;'>Top 5 Predictions:</h4>"  # Bold and larger font for predictions
+    
+    for i in top_indices:
+        class_name = get_class_name(i)
+        common_name = taxo_df[taxo_df['species'] == class_name]['common_name'].values[0]  # Get common name from CSV
+        confidence_percentage = prediction[0][i] * 100
+        
+        # Format the output with space between class name and common name
+        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
 interface = gr.Interface(
-    fn=print('HIIIII'),           # Function to be called for predictions
+    fn=make_prediction,           # Function to be called for predictions
     inputs=gr.Image(type="pil"),  # Input type: Image (PIL format)
     outputs="html",               # Output type: HTML for formatting
     title="Amazon arboreal species classification",
@@ -17,4 +73,4 @@ interface = gr.Interface(
 )
 
 # Launch the Gradio interface
-interface.launch()
+interface.launch()