app.py

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

# Load the model
model = tf.keras.models.load_model("car_damage_model .h5")


# Prediction function
def predict_damage(img):
    img = img.resize((224, 224))
    img = np.array(img) / 255.0
    img = np.expand_dims(img, axis=0)
    pred = model.predict(img)[0][0]
    label = "Whole Car" if pred > 0.5 else "Damaged Car"
    return f"{label} ({pred:.4f})"

# Gradio interface
interface = gr.Interface(
    fn=predict_damage,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(),
    title="Car Damage Classifier",
    description="Upload a car image and this model will tell if it's damaged or not."
)

interface.launch()