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

# In[82]:


import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
import tensorflow_hub as hub
import sklearn
import random
from glob import glob
import matplotlib.pyplot as plt
import requests


# In[83]:


print("TF version:", tf.__version__)
print("Hub version:", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")


# In[94]:



inception_net = tf.keras.applications.EfficientNetB7()


# In[100]:


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


# In[107]:


import gradio as gr
title = "Classifier"
Description = "Model,used :- Efficient Net B7,fine tuned on dataset 'https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals'"

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

             inputs=gr.Image(shape=(600, 600)),
             outputs=gr.Label(num_top_classes=3),
             examples=["data/animals/animals/antelope/0a37838e99.jpg", "data/animals/animals/starfish/0a63e965c2.jpg"]).launch(share=True)