app.py

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

# Load the TFLite model
interpreter = tf.lite.Interpreter(model_path=r"model.tflite")
interpreter.allocate_tensors()

# Get input and output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

CLASS_NAMES = ['Abyssinian',
 'Bengal',
 'Birman',
 'Bombay',
 'British_Shorthair',
 'Egyptian_Mau',
 'Maine_Coon',
 'Persian',
 'Ragdoll',
 'Russian_Blue',
 'Siamese',
 'Sphynx',
 'american_bulldog',
 'american_pit_bull_terrier',
 'basset_hound',
 'beagle',
 'boxer',
 'chihuahua',
 'english_cocker_spaniel',
 'english_setter',
 'german_shorthaired',
 'great_pyrenees',
 'havanese',
 'japanese_chin',
 'keeshond',
 'leonberger',
 'miniature_pinscher',
 'newfoundland',
 'pomeranian',
 'pug',
 'saint_bernard',
 'samoyed',
 'scottish_terrier',
 'shiba_inu',
 'staffordshire_bull_terrier',
 'wheaten_terrier',
 'yorkshire_terrier']

IMAGE_SIZE = 256

def predict_image(image: Image.Image):
    image = image.convert("RGB")
    image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
    image_np = np.array(image) / 255.0
    img_batch = np.expand_dims(image_np, 0).astype(np.float32)

    interpreter.set_tensor(input_details[0]['index'], img_batch)
    interpreter.invoke()
    output = interpreter.get_tensor(output_details[0]['index'])[0]
    predicted_class = CLASS_NAMES[np.argmax(output)]
    confidence = float(np.max(output)) * 100
    return predicted_class, f"{confidence:.1f}%"

# Gradio interface
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Label(label="Predicted Class"), gr.Label(label="Confidence")],
    title="Cat & Dog Breed Classifier",
    description="Upload an image of a cat or dog to identify its breed."
)

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