Spaces:
Sleeping
Sleeping
File size: 3,349 Bytes
fb59cb8 e9c5f95 fb59cb8 b8ce03a fb59cb8 e9c5f95 b8ce03a e9c5f95 b8ce03a e9c5f95 b8ce03a fb59cb8 e9c5f95 b8ce03a e9c5f95 fb59cb8 e9c5f95 b8ce03a e9c5f95 01b28b7 b8ce03a fb59cb8 b8ce03a fb59cb8 b8ce03a fb59cb8 bbe49e5 fb59cb8 bbe49e5 b8ce03a bbe49e5 fb59cb8 01b28b7 b8ce03a 01b28b7 b8ce03a 01b28b7 bbe49e5 b8ce03a 01b28b7 bbe49e5 b8ce03a bbe49e5 b8ce03a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
#!/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()
|