app.py

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

model = tf.keras.applications.DenseNet121(input_shape=(224, 224, 3))

response=requests.get('https://raw.githubusercontent.com/gradio-app/mobilenet-example/master/labels.txt')
labels=response.text.split('\n')


def make_pred(image):
    image=tf.image.resize(image,(224,224))
    image=tf.keras.applications.densenet.preprocess_input(image)
    image=tf.expand_dims(image,0)
    pred = model.predict(image).reshape(-1)
    
    conf= {}
    for i in range(len(labels[:-1])):
        conf[labels[i]]=float(pred[i])

    

    return conf

demo = gr.Interface(fn=make_pred,inputs=[gr.inputs.Image()],
                    outputs=[gr.outputs.Label(num_top_classes=5)])
demo.launch()