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| import gradio as gr | |
| import cv2 | |
| import requests | |
| import gdown | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| import numpy as np | |
| from keras.models import load_model | |
| path = [['0229_01.jpg'], ['0385_01.jpg'], ['0067_01.jpg']] | |
| URL = 'https://drive.google.com/file/d/1TpQmvq2R8nHy9CQaVb0jtMMuRQA0Cw0L/view?usp=sharing' | |
| output_path = 'label.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 = 4000 | |
| IMG_SIZE = 224 | |
| 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("final_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.Image(type='pil'), outputs='label', examples=path,).launch() | |