| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| import cv2 | |
| model = tf.keras.models.load_model("mymodel/mymodel") | |
| def predict(img): | |
| if img is None: | |
| return | |
| z = tf.keras.preprocessing.image.img_to_array(img) | |
| kernel = np.ones((5,5),np.uint8) | |
| z = cv2.dilate(z ,kernel,iterations = 1) | |
| z = np.expand_dims(z, axis=0) | |
| y = model.predict(z) | |
| ysoft = tf.nn.softmax(y) | |
| ymax = np.argmax(ysoft) | |
| return int(ymax) | |
| sp = gr.Sketchpad(tool="sketch", shape=(140,100), image_mode="L", label='arabic numeral', invert_colors=False).style(height=200, width=280) | |
| gr.Label() | |
| gr.Interface(fn=predict, | |
| inputs=sp, | |
| outputs="label", | |
| live=True, | |
| examples=[ | |
| ["8.png"], | |
| ["4.png"], | |
| ["9.png"], | |
| ["2.png"]]).launch() |