import numpy as np
import tensorflow as tf
import gradio as gr
from huggingface_hub import from_pretrained_keras
import cv2

img_size = 28
model = from_pretrained_keras("keras-io/keras-reptile")

def read_image(image):
    image = tf.convert_to_tensor(image)
    image = cv2.resize()
    image = image / 127.5 - 1
    return image

def infer(model, image_tensor):
    predictions = model.predict(np.expand_dims((image_tensor), axis=0))
    predictions = np.squeeze(predictions)
    predictions = np.argmax(predictions, axis=0)
    return predictions
def display_result(input_image):
    image_tensor = read_image(input_image)
    prediction_label = infer(model=model, image_tensor=image_tensor)
    return prediction_label

input = gr.inputs.Image()
examples = [["/content/drive/MyDrive/boot.jpg"], ["/content/drive/MyDrive/sneaker.jpg"]] 
title = "Few shot learning"
description = "Upload an image or select from examples to classify fashion items."


gr.Interface(display_result, input, outputs="text", examples=examples, allow_flagging=False, analytics_enabled=False,
 title=title, description=description).launch(enable_queue=True)
gr.launch()