Initial commit

app.py

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+import gradio as gr
+import huggingface_hub
+from PIL import Image
+from pathlib import Path
+import onnxruntime as rt
+import numpy as np
+import csv
+
+
+MODEL_REPO = 'toynya/Z3D-E621-Convnext'
+THRESHOLD = 0.5
+DESCRIPTION = """
+This is a demo of https://huggingface.co/toynya/Z3D-E621-Convnext
+I am not affiliated with the model author in anyway, this is just a useful tool requested by a user.
+"""
+
+
+def prepare_image(image: Image.Image, target_size: int):
+	# Pad image to square
+	image_shape = image.size
+	max_dim = max(image_shape)
+	pad_left = (max_dim - image_shape[0]) // 2
+	pad_top = (max_dim - image_shape[1]) // 2
+
+	padded_image = Image.new("RGB", (max_dim, max_dim), (255, 255, 255))
+	padded_image.paste(image, (pad_left, pad_top))
+
+	# Resize
+	if max_dim != target_size:
+		padded_image = padded_image.resize((target_size, target_size), Image.BICUBIC)
+	
+	# Convert to numpy array
+	image_array = np.asarray(padded_image, dtype=np.float32) / 255.0
+	return np.expand_dims(image_array, axis=0)
+
+
+def predict(image: Image.Image):
+	image_array = prepare_image(image, 448)
+
+	image_array = prepare_image(image, 448)
+	input_name = 'input_1:0'
+	output_name = 'predictions_sigmoid'
+
+	result = session.run([output_name], {input_name: image_array})
+	result = result[0][0]
+
+	scores = {tags[i]: result[i] for i in range(len(result))}
+	predicted_tags = [tag for tag, score in scores.items() if score > THRESHOLD]
+	tag_string = ', '.join(predicted_tags)
+
+	return tag_string, scores
+
+
+print("Downloading model...")
+path = huggingface_hub.snapshot_download(MODEL_REPO)
+print("Loading model...")
+session = rt.InferenceSession(path / 'model.onnx', providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+
+with open(path / 'tags-selected.csv', mode='r', encoding='utf-8') as file:
+	csv_reader = csv.DictReader(file)
+	tags = [row['name'].strip() for row in csv_reader]
+
+print("Starting server...")
+
+gradio_app = gr.Interface(
+	predict,
+	inputs=gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'),
+	outputs=[
+		gr.Textbox(label="Tag String"),
+		gr.Label(label="Tag Predictions", num_top_classes=100),
+	],
+	title="JoyTag",
+	description=DESCRIPTION,
+	allow_flagging="never",
+)
+
+
+if __name__ == '__main__':
+	gradio_app.launch()

requirements.txt

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+numpy==1.26.3