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| import numpy as np | |
| import gradio as gr | |
| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
| from tensorflow.keras.models import load_model | |
| model = load_model('saved_model.keras') | |
| def predict_number(image): | |
| image_rgb = image["composite"][..., :3] # delete alpha | |
| image_grayScale= np.mean(image_rgb, axis=-1).astype(np.uint8) | |
| image_grayScale = image_grayScale.reshape(1, 280, 280, 1) | |
| preds = model.predict(image_grayScale)[0] | |
| return {str(i): float(preds[i]) for i in range(10)} | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| <center> | |
| <h1>Digit Classification</h1> | |
| </center> | |
| ## How to use: | |
| 1. **Draw a single digit** (0-9) in the center of the black canvas. Digit should be as large as possible. | |
| 2. **Click 'Recognize'** to see the prediction and the model's confidence levels. | |
| 3. **Click 'Clear canvas'** to clear the canvas. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| sketch_pad = gr.Sketchpad( | |
| label="Draw here (White on Black)", | |
| layers=False, | |
| canvas_size=(280, 280), | |
| brush=gr.Brush(colors=["#FFFFFF"], default_color="#FFFFFF", default_size=10), | |
| value={ | |
| "background": np.zeros((280, 280, 3), dtype=np.uint8), | |
| "layers": [], | |
| "composite": None | |
| }, | |
| type="numpy" | |
| ) | |
| with gr.Column(): | |
| output = gr.Label(num_top_classes=3, label="Prediction") | |
| btn = gr.Button("Recognize", variant="primary") | |
| clear_btn = gr.Button("Clear canvas", variant="stop") | |
| clear_btn.click(lambda: {"background": np.zeros((280, 280, 3), dtype=np.uint8), "layers": [], "composite": None}, | |
| outputs=sketch_pad) | |
| btn.click(predict_number, inputs=sketch_pad, outputs=output) | |
| gr.Markdown(""" | |
| ## GitHub Repository | |
| If you want to see the training process, data augmentation, and how the model was built, check out the **[main_analysis.ipynb](https://github.com/Arcus72/Image-Digit-Classification)** file in my GitHub repository.""") | |
| demo.launch(theme=gr.themes.Soft()) |