import numpy as np
import gradio as gr
from keras.models import load_model
from keras.preprocessing.image import load_img, img_to_array

# Load the pre-trained model
model = load_model('best_model.h5')

# Define class names for Boy or Girl classification
class_names = ['Boy', 'Girl']

# Function to preprocess the image and make predictions
def classify_image(image):
    # Load and preprocess the image
    img = load_img(image, target_size=(150, 150))  # Ensure this matches the input size of your model
    img_array = img_to_array(img) / 255.0  # Normalize pixel values
    img_array = np.expand_dims(img_array, axis=0)  # Reshape for the model input (1, 150, 150, 3)

    # Log the shape of the image array for debugging
    print(f"Image shape for prediction: {img_array.shape}")

    # Get prediction
    prediction = model.predict(img_array)

    # Determine the predicted class based on the model output
    predicted_class_index = np.argmax(prediction[0])  # Get index of the highest prediction score
    predicted_class = class_names[predicted_class_index]  # Map index to class name
    return predicted_class

# Gradio interface
iface = gr.Interface(fn=classify_image,
                     inputs=gr.Image(type="filepath"),
                     outputs=gr.Textbox(),
                     title="Boy or Girl Classifier",
                     description="Upload an image to classify it as either a Boy or a Girl.")

# Launch the app
iface.launch()