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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 PIL import Image | |
| from keras.models import load_model | |
| url = 'https://drive.google.com/file/d/1ueGC-MBuVip9jkfLJdfeH-RBy6fjDZ5j/view?usp=sharing' | |
| output_path = 'classes.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 = 200 | |
| IMG_SIZE = 128 | |
| model = load_model('model.h5') | |
| # Preprocess image | |
| def preprocess(image): | |
| image = image.convert('RGB') # To RGB | |
| image = image.resize((IMG_SIZE, IMG_SIZE)) # Resize the image to 128 x 128 | |
| image = np.array(image) | |
| image = tf.keras.applications.mobilenet_v2.preprocess_input(image) # Rescale the pixel to [-1,1] for MobileNetV2 | |
| return image | |
| def predict(image): | |
| image = preprocess(image) | |
| image = np.expand_dims(image, axis=0) # Add batch dimension | |
| prediction = model.predict(image) # Predict the image using the trained model | |
| # Get the top 3 predictions | |
| idx = np.argsort(prediction[0])[::-1][:3] | |
| top3_value = np.asarray([prediction[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)} | |
| # Create Gradio interface | |
| input_image = gr.inputs.Image(type='pil') | |
| output_text = 'label' | |
| app = gr.Interface(fn=predict, inputs=input_image, outputs=output_text) | |
| app.launch() |