app.py

import gradio as gr
import cv2
import requests
import gdown
import tensorflow as tf
from tensorflow import keras
#from custom_model import ImageClassifier
import numpy as np
#from tensorflow.keras import models
#from keras.layers import Dense, Activation, Flatten,Dropout, Conv2D, BatchNormalization, MaxPooling2D

from keras.models import load_model



 

path  = [['car_bike.jpg'], ['human.jpg'], ['chair.jpg']]

        
url = 'https://drive.google.com/file/d/1PCb6MTqelw7Tk0iCxo5Ef70zYwEpsI4Y/view?usp=sharing'
output_path = 'classlabel.txt'
gdown.download(url, output_path, quiet=False,fuzzy=True)

with open(output_path,'r') as file:
    LABELS = [x.strip() for x in file.readlines()]


num_classes = 12
IMG_SIZE = 124

def _normalize_img(img):
    img = tf.cast(img, tf.float32)/255.   # All images will be rescaled by 1./255
    img = tf.image.resize(img,  (IMG_SIZE, IMG_SIZE), method= 'bilinear')
    return (img)



model = load_model("model.h5")


def predict_fn(img):
     img = img.convert('RGB')
     img_data = _normalize_img(img)
     x = np.array(img_data)
     x = np.expand_dims(x, axis=0)
     temp = model.predict(x)
     
     idx = np.argsort(np.squeeze(temp))[::-1]
     top3_value = np.asarray([temp[0][i] for i in idx[0:3]])
     top3_idx = idx[0:3]
     
     return {LABELS[i]:str(v) for i,v in zip(top3_idx,top3_value)}
     


gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label', examples=path,).launch()