import numpy as np
from tensorflow.keras.preprocessing import image
import gradio as gr
import numpy as np
from tensorflow.keras.preprocessing import image
import gradio as gr
import keras

model = keras.models.load_model('x_ray.keras')

# Define the function for image classification
def classify_image(img):
    # Set the input image dimensions
    img_width, img_height = 150, 150

    # Resize the image to match the model's input shape
    img = img.resize((img_width, img_height))

    # Convert the image to a numpy array
    img = np.array(img)
    img = img.astype('float32') / 255.0
    img = np.expand_dims(img, axis=0)

    # Get the prediction
    prediction = model.predict(img)

    return "NOT fractured" if prediction > 0.5 else "fractured"

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.inputs.Image(type="pil", label="Upload an X-ray image"),
    outputs="text",
    title="Bone Fracture Classification",
    description="Upload an X-ray image, and this model will classify it as fractured or not.",
)

# Start the Gradio interface
iface.launch()