app.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import tensorflow as tf

import sklearn
import random
import matplotlib.pyplot as plt
import requests


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inception_net = tf.keras.applications.EfficientNetB7()


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import requests

response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def classify_image(inp):
  inp = inp.reshape((-1, 600, 600, 3))
  inp = tf.keras.applications.efficientnet_v2.preprocess_input(inp)
  prediction = inception_net.predict(inp).flatten()
  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences


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import gradio as gr
title = "Simple Image Classifier"
Description = "A image classifier demo , using pretrained Efficient Net B7 and fine tuned on Animal Images dataset found on Kaggle ,tools used Tensorflow , PIL,numpy , sklearn"

gr.Interface(fn=classify_image,
             title = title,
             description = Description,

             inputs=gr.Image(shape=(600, 600)),
             outputs=gr.Label(num_top_classes=3),
            ).launch()