Create app.py

app.py

@@ -0,0 +1,175 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import html
+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
+import piexif
+import piexif.helper
+
+TITLE = 'Alchemy'
+
+TOKEN = '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'
+MODEL_REPO = 'PICOF/deepdanbooru'
+MODEL_FILENAME = 'model-resnet_custom_v3.h5'
+LABEL_FILENAME = 'tags.txt'
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--score-slider-step', type=float, default=0.05)
+    parser.add_argument('--score-threshold', type=float, default=0.5)
+    parser.add_argument('--theme', type=str, default='dark-grass')
+#    parser.add_argument('--live', action='store_true')
+    parser.add_argument('--share', action='store_true')
+#    parser.add_argument('--port', type=int)
+#    parser.add_argument('--disable-queue',
+#                        dest='enable_queue',
+#                        action='store_false')
+    parser.add_argument('--allow-flagging', type=str, default='never')
+    return parser.parse_args()
+
+#def load_sample_image_paths() -> list[pathlib.Path]:
+#    image_dir = pathlib.Path('images')
+#    if not image_dir.exists():
+#        dataset_repo = 'hysts/sample-images-TADNE'
+#        path = huggingface_hub.hf_hub_download(dataset_repo,
+#                                               'images.tar.gz',
+#                                               repo_type='dataset',
+#                                               use_auth_token=TOKEN)
+#        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(MODEL_REPO,
+                                           MODEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    model = tf.keras.models.load_model(path)
+    return model
+
+
+def load_labels() -> list[str]:
+    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+                                           LABEL_FILENAME,
+                                           use_auth_token=TOKEN)
+    with open(path) as f:
+        labels = [line.strip() for line in f.readlines()]
+    return labels
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+def predict(image: PIL.Image.Image, score_threshold: float,
+            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
+    rawimage = image
+    _, 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)
+    res = dict()
+    for prob, label in zip(probs.tolist(), labels):
+        if prob < score_threshold:
+            continue
+        res[label] = prob
+    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
+    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
+    
+    items = rawimage.info
+    geninfo = ''
+    
+    if "exif" in rawimage.info:
+        exif = piexif.load(rawimage.info["exif"])
+        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
+        try:
+            exif_comment = piexif.helper.UserComment.load(exif_comment)
+        except ValueError:
+            exif_comment = exif_comment.decode('utf8', errors="ignore")
+    
+        items['exif comment'] = exif_comment
+        geninfo = exif_comment
+    
+        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                      'loop', 'background', 'timestamp', 'duration']:
+            items.pop(field, None)
+    
+    geninfo = items.get('parameters', geninfo)
+    
+    info = f"""
+<p><h4>PNG Info</h4></p>    
+"""
+    for key, text in items.items():
+        info += f"""
+<div>
+{str(key)}:[{str(text)}]
+</div>
+""".strip()+"\n"
+    
+    if len(info) == 0:
+        message = "Nothing found in the image."
+        info = f"<div><p>{message}<p></div>"
+    
+    return (info,a,res)
+
+
+def main():
+    args = parse_args()
+    model = load_model()
+    labels = load_labels()
+
+    func = functools.partial(predict, model=model, labels=labels)
+    func = functools.update_wrapper(func, predict)
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Image(type='pil', label='Input'),
+            gr.inputs.Slider(0,
+                             1,
+                             step=args.score_slider_step,
+                             default=args.score_threshold,
+                             label='Score Threshold'),
+        ],
+        [
+            gr.outputs.HTML(),
+            gr.outputs.Textbox(label='DeepDanbooru Output (string)'), 
+            gr.outputs.Label(label='DeepDanbooru Output (label)')
+        ],
+        examples=[
+        ['eula.png',0.5],
+        ['keqing.png',0.5]
+        ],
+        title=TITLE,
+        description='''
+Just play for fun...
+        ''',
+        theme=args.theme,
+        allow_flagging=args.allow_flagging,
+    ).launch(
+        enable_queue=True,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()