| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow.keras as keras | |
| from tensorflow.keras.models import load_model | |
| # load model | |
| model = load_model('vehicle_cnn.h5') | |
| classnames = ['traintaro', 'cartaro', 'bicycletaro'] | |
| def predict_image(img): | |
| img_4d=img.reshape(-1,224, 224,3) | |
| prediction=model.predict(img_4d)[0] | |
| return {classnames[i]: float(prediction[i]) for i in range(3)} | |
| image = gr.inputs.Image(shape=(224, 224)) | |
| label = gr.outputs.Label(num_top_classes=3) | |
| gr.Interface(fn=predict_image, inputs=image, title="Garbage Classifier V3", | |
| description="ThiFaces using Gradio.",outputs=label,enable_queue=True,interpretation='default').launch() |