import gradio as gr
import tensorflow as tf
import numpy as np

num_classes = 200
IMG_HEIGHT = 256
IMG_WIDTH = 256

with open("classlabel.txt", 'r') as file:
    CLASS_LABEL = [x.strip() for x in file.readlines()]

def normalize_image(img):
    img = tf.cast(img, tf.float32)/255.
    img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH), method='bilinear')
    return img


def predict_fn(img):
    img = img.convert('RGB')
    img_data = normalize_image(img)
    x = np.array(img_data)
    x = np.expand_dims(x, axis=0)
    temp = model.predict(x)
    
    class_index = np.argmax(temp)
    return CLASS_LABEL[class_index]


model = tf.keras.models.load_model("model019.h5")

interface = gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label')
interface.launch()