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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import deepdanbooru as dd | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import PIL.Image | |
| import tensorflow as tf | |
| def load_model() -> tf.keras.Model: | |
| path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru', | |
| 'model-resnet_custom_v3.h5') | |
| model = tf.keras.models.load_model(path) | |
| return model | |
| def load_labels() -> list[str]: | |
| path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru', | |
| 'tags.txt') | |
| with open(path) as f: | |
| labels = [line.strip() for line in f.readlines()] | |
| return labels | |
| model = load_model() | |
| labels = load_labels() | |
| def predict(image: PIL.Image.Image, score_threshold: float): | |
| _, height, width, _ = model.input_shape | |
| image = np.asarray(image) | |
| image = tf.image.resize(image, | |
| size=(height, width), | |
| method=tf.image.ResizeMethod.AREA, | |
| preserve_aspect_ratio=True) | |
| image = image.numpy() | |
| image = dd.image.transform_and_pad_image(image, width, height) | |
| image = image / 255. | |
| probs = model.predict(image[None, ...])[0] | |
| probs = probs.astype(float) | |
| indices = np.argsort(probs)[::-1] | |
| result_all = dict() | |
| result_threshold = dict() | |
| result_html = '' | |
| for index in indices: | |
| label = labels[index] | |
| prob = probs[index] | |
| result_all[label] = prob | |
| if prob < score_threshold: | |
| break | |
| result_html = result_html + '<p class="m5dd_list use"><span>' + str(label) + '</span><span>' + str(round(prob, 3)) + '</span></p>' | |
| result_threshold[label] = prob | |
| result_text = ', '.join(result_threshold.keys()) | |
| result_text = '<div id="m5dd_result">' + str(result_text) + '</div>' | |
| result_html = '<div>' + str(result_html) + '</div>' | |
| return result_html, result_text | |
| js = """ | |
| async () => { | |
| document.addEventListener('click', function(event) { | |
| let tagItem = event.target.closest('.m5dd_list') | |
| let resultArea = event.target.closest('#m5dd_result') | |
| if (tagItem){ | |
| if (tagItem.classList.contains('use')){ | |
| tagItem.classList.remove('use') | |
| }else{ | |
| tagItem.classList.add('use') | |
| } | |
| document.getElementById('m5dd_result').innerText = | |
| Array.from(document.querySelectorAll('.m5dd_list.use>span:nth-child(1)')) | |
| .map(v=>v.innerText) | |
| .join(', ') | |
| }else if (resultArea){ | |
| const selection = window.getSelection() | |
| selection.removeAllRanges() | |
| const range = document.createRange() | |
| range.selectNodeContents(resultArea) | |
| selection.addRange(range) | |
| }else{ | |
| return | |
| } | |
| }) | |
| } | |
| """ | |
| with gr.Blocks(css="style.css") as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image = gr.Image(label='Input', type='pil') | |
| score_threshold = gr.Slider(label='Score threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.5) | |
| run_button = gr.Button('Run') | |
| result_text = gr.HTML(value="<div></div>") | |
| with gr.Column(scale=3): | |
| result_html = gr.HTML(value="<div></div>") | |
| run_button.click( | |
| fn=predict, | |
| inputs=[image, score_threshold], | |
| outputs=[result_html, result_text], | |
| api_name='predict', | |
| ) | |
| demo.load(None,None,None,_js=js) | |
| demo.queue().launch() | |