import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image

model_path = "xception_aerial.keras"
model = tf.keras.models.load_model(model_path)
 
# Define the core prediction function
def predict_aerial(image):
    # Preprocess image
    print(type(image))
    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
    image = image.resize((150, 150))  # Resize the image to 150x150
    image = np.array(image)
    image = np.expand_dims(image, axis=0)  # Expand dimensions to match the model input shape
    
    # Predict
    prediction = model.predict(image)
    
    # Print the shape of the prediction to debug
    print(f"Prediction shape: {prediction.shape}")
    
    # Assuming the output is already softmax probabilities
    probabilities = prediction[0]
    
    # Print the probabilities array to debug
    print(f"Probabilities: {probabilities}")
    
    # Assuming your model was trained with these class names
    class_names = ['agriculture', 'airport', 'beach', 'city', 'forest']  # Replace 'another_pokemon' with your third class name
    
    # Create a dictionary of class probabilities
    result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
    
    return result
 
 
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
    fn=predict_aerial,
    inputs=input_image, 
    outputs=gr.Label(),
    examples=["aerial_examples/agriculture1.jpg", "aerial_examples/agriculture2.jpg", "aerial_examples/agriculture3.jpg", "aerial_examples/airport1.jpg", "aerial_examples/airport2.jpg", "aerial_examples/airport3.jpg", "aerial_examples/beach1.jpg", "aerial_examples/beach2.jpg", "aerial_examples/beach3.jpg", "aerial_examples/forest1.jpg", "aerial_examples/forest2.jpg", "aerial_examples/forest3.jpg",
              "aerial_examples/city1.jpg", "aerial_examples/city2.jpg", "aerial_examples/city3.jpg"],   
    description="A simple mlp classification model for image classification using the mnist dataset.")
iface.launch()