app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow_addons as tfa
import os
import numpy as np


# labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
HEIGHT,WIDTH=224,224

model=load_model('Models/best_model1.h5')

# def classify_image(inp):
#   np.random.seed(143)
#   inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
#   inp = tf.keras.applications.nasnet.preprocess_input(inp) 
#   prediction = model.predict(inp)
#   ###label = dict((v,k) for k,v in labels.items())
#   predicted_class_indices=np.argmax(prediction,axis=1)
#   result = {}
#   for i in range(len(predicted_class_indices)):
#       if predicted_class_indices[i] < NUM_CLASSES:
#           result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
#   return result 

def classify_image(inp):
    np.random.seed(143)
    labels = {'Cat': 0, 'Dog': 1}
    NUM_CLASSES = 2
    #inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
    #inp = tf.keras.applications.nasnet.preprocess_input(inp)
    prediction = model.predict(inp)
    predicted_class_indices = np.argmax(prediction, axis=1)

    label_order = ["Cat","Dog"]

    result = {label: float(f"{prediction[0][labels[label]]:.6f}") for label in label_order}

    return result

    
image = gr.Image(height=HEIGHT,width=WIDTH,label='Input')
label = gr.Label(num_top_classes=2)

gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Smart Pet Classifier').launch(debug=False)