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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import pathlib | |
| import tarfile | |
| import deepdanbooru as dd | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import PIL.Image | |
| import tensorflow as tf | |
| DESCRIPTION = "# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)" | |
| def load_sample_image_paths() -> list[pathlib.Path]: | |
| image_dir = pathlib.Path("images") | |
| if not image_dir.exists(): | |
| path = huggingface_hub.hf_hub_download("public-data/sample-images-TADNE", "images.tar.gz", repo_type="dataset") | |
| with tarfile.open(path) as f: | |
| f.extractall() | |
| return sorted(image_dir.glob("*")) | |
| 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) -> tuple[dict[str, float], dict[str, float], str]: | |
| _, 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 / 255.0 | |
| probs = model.predict(image[None, ...])[0] | |
| probs = probs.astype(float) | |
| indices = np.argsort(probs)[::-1] | |
| result_all = dict() | |
| result_threshold = dict() | |
| for index in indices: | |
| label = labels[index] | |
| prob = probs[index] | |
| result_all[label] = prob | |
| if prob < score_threshold: | |
| break | |
| result_threshold[label] = prob | |
| result_text = ", ".join(result_all.keys()) | |
| return result_threshold, result_all, result_text | |
| image_paths = load_sample_image_paths() | |
| examples = [[path.as_posix(), 0.5] for path in image_paths] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| 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") | |
| with gr.Column(): | |
| with gr.Tabs(): | |
| with gr.Tab(label="Output"): | |
| result = gr.Label(label="Output", show_label=False) | |
| with gr.Tab(label="JSON"): | |
| result_json = gr.JSON(label="JSON output", show_label=False) | |
| with gr.Tab(label="Text"): | |
| result_text = gr.Text(label="Text output", show_label=False, lines=5) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[image, score_threshold], | |
| outputs=[result, result_json, result_text], | |
| fn=predict, | |
| cache_examples=os.getenv("CACHE_EXAMPLES") == "1", | |
| ) | |
| run_button.click( | |
| fn=predict, | |
| inputs=[image, score_threshold], | |
| outputs=[result, result_json, result_text], | |
| api_name="predict", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |