app.py

import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
from keras import layers

# Load your trained Xception model
model = tf.keras.models.load_model("xception-head")

# Define the labels for your classification
class_labels = ['fresh', 'early decay', 'advanced decay','skeletonized']  # Replace with your actual class names

def classify_image(img):
    # Preprocess the image to fit the model input shape
    img = img.resize((299, 299))  # Xception takes 299x299 input size
    img = np.array(img) / 255.0   # Normalize the image
    img = np.expand_dims(img, axis=0)

    # Make prediction
    predictions = model.predict(img)
    predicted_class = np.argmax(predictions, axis=1)[0]
    confidence = np.max(predictions)
    return {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}, confidence

# Example images (local paths or URLs)
example_images = [
    'examples/fresh.jpg',  # Replace with actual local file paths or URLs
    'examples/early_decay.jpg',
    'examples/advanced_decay.jpg',
    'examples/skeletonized.jpg'
]

# Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(num_top_classes=len(class_labels)), gr.Number()],
    live=True,
    examples=example_images
)

if __name__ == "__main__":
    demo.launch()