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import gradio as gr | |
import tensorflow as tf | |
import numpy as np | |
from PIL import Image | |
model = tf.keras.models.load_model('my_model.h5') | |
labels = ["No rotated","Rotared to left","Upside down","Rotared to right"] | |
sample_images = [["0.jpeg"],["90.jpeg"],["180.jpeg"],["270.jpeg"]] | |
def classify_image(inp): | |
inp = tf.keras.preprocessing.image.load_img(inp.name, target_size=(90,90)) | |
img_array = tf.keras.preprocessing.image.img_to_array(inp) | |
a_file = open("gradio.txt", "w") | |
for row in img_array: | |
np.savetxt(a_file, row) | |
a_file.close() | |
img_array = tf.expand_dims(inp, 0) # Create a batch | |
prediction = model.predict(img_array) | |
score = tf.nn.softmax(prediction[0]) | |
return {labels[np.argmax(score)]:1 * np.max(score) for i in range(4)} | |
image = gr.inputs.Image(type="file", invert_colors=False) | |
label = gr.outputs.Label(num_top_classes=1) | |
gr.Interface(fn=classify_image, inputs=image, examples = sample_images, outputs=label, allow_flagging=None, title="Scan rotation app", theme="dark").launch() |