X-Analysis/teeth-xray-app

1import tensorflow as tf2import gradio as gr3from keras.layers import TFSMLayer4import numpy as np5from PIL import Image6 7# Load the TensorFlow SavedModel using Keras 3 syntax8model = TFSMLayer(".", call_endpoint="serving_default")9 10# Preprocessing function11def preprocess_image(image):12    image = image.resize((256, 256))  # Resize to match model input13    image = np.array(image) / 255.0   # Normalize14    image = np.expand_dims(image, axis=0)  # Add batch dimension15    return image.astype(np.float32)16 17# Inference function18def predict(image):19    input_tensor = preprocess_image(image)20    prediction = model(input_tensor)21    prediction = tf.squeeze(prediction, axis=0).numpy()  # Remove batch dim22    mask = (prediction > 0.5).astype(np.uint8) * 255      # Binarize mask23    return Image.fromarray(mask)24 25# Gradio interface26interface = gr.Interface(27    fn=predict,28    inputs=gr.Image(type="pil"),29    outputs=gr.Image(type="pil"),30    title="Teeth Segmentation",31    description="Upload a panoramic X-ray image to segment the teeth using a U-Net model."32)33 34interface.launch()