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

# Load your model
model = tf.keras.models.load_model("animal_classifier.h5")

def predict_image(img):
    # Preprocess
    img = img.resize((128, 128))
    img_array = np.array(img) / 255.0
    img_batch = np.expand_dims(img_array, axis=0)
    
    # Predict
    pred = model.predict(img_batch, verbose=0)[0][0]
    label = "dog" if pred > 0.5 else "cat"
    confidence = float(pred) if pred > 0.5 else float(1 - pred)
    return {label: confidence}

# Gradio interface
demo = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=2),
    examples=[],
    title="🐱 vs 🐶 Cat or Dog Classifier",
    description="Trained on only 100 images! Upload a photo to see the prediction."
)

demo.launch()