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from __future__ import annotations |
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import os |
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import pathlib |
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import tarfile |
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import deepdanbooru as dd |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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import tensorflow as tf |
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DESCRIPTION = "# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)" |
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def load_sample_image_paths() -> list[pathlib.Path]: |
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image_dir = pathlib.Path("images") |
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if not image_dir.exists(): |
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path = huggingface_hub.hf_hub_download("public-data/sample-images-TADNE", "images.tar.gz", repo_type="dataset") |
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with tarfile.open(path) as f: |
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f.extractall() |
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return sorted(image_dir.glob("*")) |
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def load_model() -> tf.keras.Model: |
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path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "model-resnet_custom_v3.h5") |
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model = tf.keras.models.load_model(path) |
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return model |
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def load_labels() -> list[str]: |
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path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "tags.txt") |
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with open(path) as f: |
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labels = [line.strip() for line in f.readlines()] |
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return labels |
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model = load_model() |
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labels = load_labels() |
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def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]: |
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_, height, width, _ = model.input_shape |
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image = np.asarray(image) |
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image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True) |
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image = image.numpy() |
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image = dd.image.transform_and_pad_image(image, width, height) |
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image = image / 255.0 |
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probs = model.predict(image[None, ...])[0] |
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probs = probs.astype(float) |
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indices = np.argsort(probs)[::-1] |
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result_all = dict() |
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result_threshold = dict() |
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for index in indices: |
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label = labels[index] |
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prob = probs[index] |
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result_all[label] = prob |
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if prob < score_threshold: |
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break |
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result_threshold[label] = prob |
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result_text = ", ".join(result_all.keys()) |
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return result_threshold, result_all, result_text |
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image_paths = load_sample_image_paths() |
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examples = [[path.as_posix(), 0.5] for path in image_paths] |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label="Input", type="pil") |
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score_threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, step=0.05, value=0.5) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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with gr.Tabs(): |
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with gr.Tab(label="Output"): |
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result = gr.Label(label="Output", show_label=False) |
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with gr.Tab(label="JSON"): |
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result_json = gr.JSON(label="JSON output", show_label=False) |
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with gr.Tab(label="Text"): |
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result_text = gr.Text(label="Text output", show_label=False, lines=5) |
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gr.Examples( |
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examples=examples, |
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inputs=[image, score_threshold], |
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outputs=[result, result_json, result_text], |
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fn=predict, |
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cache_examples=os.getenv("CACHE_EXAMPLES") == "1", |
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) |
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run_button.click( |
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fn=predict, |
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inputs=[image, score_threshold], |
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outputs=[result, result_json, result_text], |
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api_name="predict", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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