import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
import matplotlib.pyplot as plt
import requests


def predict_img(img):
    image = np.array(img) / 255.0
    # new_model = tf.keras.models.load_model("https://huggingface.co/khushpreet/eyedisease/blob/main/64x3-CNN.model/saved_model.pb")
    proxies = {"http": "https://huggingface.co/spaces/khushpreet/diseasedetection/tree/main/64x3-CNN.model"}
    new_model = AutoModelForSequenceClassification.from_pretrained(proxies)
    predict=new_model.predict(np.array([image]))
    per=np.argmax(predict,axis=1)
    
    if per==1:
        return "No Diabetic"
    else:
        return "Diabetic"

image = gr.inputs.Image(shape=(224,224))

gr.Interface(fn=predict_img, inputs=image, outputs="text",interpretation='default').launch(debug='false')