new file: app.py

app.py

@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+import onnxruntime as rt
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from dataclasses import dataclass
+
+tagger_model_path = hf_hub_download(
+    repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx")
+
+tagger_model = rt.InferenceSession(
+    tagger_model_path, providers=['CPUExecutionProvider'])
+tagger_model_meta = tagger_model.get_modelmeta().custom_metadata_map
+tagger_tags = eval(tagger_model_meta['tags'])
+
+
+@dataclass
+class Tag:
+    lable: str
+    prob: float
+
+
+def tagger_predict(image, score_threshold):
+    image = np.array(image)
+    # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    s = 512
+    h, w = image.shape[:-1]
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    ph, pw = s - h, s - w
+    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
+    image = cv2.copyMakeBorder(
+        image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE)
+    image = image.astype(np.float32) / 255
+    image = image[np.newaxis, :]
+    probs = tagger_model.run(None, {"input_1": image})[0][0]
+    probs = probs.astype(np.float32)
+    res = []
+    for prob, label in zip(probs.tolist(), tagger_tags):
+        if prob < score_threshold:
+            continue
+        res.append(Tag(label, prob))
+    sorted_res = sorted(res, key=lambda Tag: Tag.prob, reverse=True)
+    output_string = ""
+    output_string_without_prob = ""
+    for iter in sorted_res:
+        output_string += iter.lable + f" : {iter.prob:.2f}\n"
+        output_string_without_prob += iter.lable + "\n"
+    output_string = output_string[:-1]
+    output_string_without_prob = output_string_without_prob[:-1]
+    return (output_string, output_string_without_prob)
+
+
+def gradio_wrapper(image, score_threshold):
+    return tagger_predict(image, score_threshold)
+
+
+inputs = gr.inputs.Image()
+slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.5)
+outputs = gr.outputs.Textbox()
+outputs_list = gr.outputs.Textbox()
+
+iface = gr.Interface(fn=gradio_wrapper,
+                     inputs=[inputs, slider],
+                     outputs=[outputs, outputs_list])
+
+iface.launch()