import gradio as gr
import cv2
import numpy as np
from PIL import Image
from keras.preprocessing import image
import tensorflow as tf
#predict function
model = tf.keras.models.load_model(r"model.keras") 

def predict(img):
    print("hi")

    print("hi")
    Retina_classes = ['DR', 'No_DR']
    img = np.resize(img,(224,224,3))
    img = np.expand_dims(img, axis=0)
    prediction=model.predict(img)[0]
    print(prediction)
    return {Retina_classes[i]: float(prediction[i]) for i in range(2)}
#Just four line implementation
image = gr.Image(label="Upload Here")
label = gr.Label(num_top_classes=2)
gr.Interface(fn=predict, inputs="image", outputs=label).launch()