Update app.py

app.py

@@ -1,5 +1,18 @@
 import gradio as gr
 
+import argparse
+import os
+
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import onnxruntime as rt
+import pandas as pd
+from PIL import Image
+
+
+
+
 # Daftar model dan ControlNet
 models = ["Model A", "Model B", "Model C"]
 vae = ["VAE A", "VAE B", "VAE C"]
@@ -22,6 +35,7 @@ def controlnet_process(image, controlnet_type, model):
     return f"Proses gambar dengan ControlNet '{controlnet_type}' dan model '{model}'"
 
 #wd tagger
+
 # Dataset v3 series of models:
 SWINV2_MODEL_DSV3_REPO = "SmilingWolf/wd-swinv2-tagger-v3"
 CONV_MODEL_DSV3_REPO = "SmilingWolf/wd-convnext-tagger-v3"
@@ -36,7 +50,181 @@ CONV_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
 CONV2_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
 VIT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"
 
-dropdown_list = [
+# Files to download from the repos
+MODEL_FILENAME = "model.onnx"
+LABEL_FILENAME = "selected_tags.csv"
+
+# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/a9eacb1eff904552d3012babfa28b57e1d3e295c/tagger/ui.py#L368
+kaomojis = [ "0_0", "(o)_(o)", "+_+", "+_-", "._.", "<o>_<o>", "<|>_<|>", "=_=", ">_<", "3_3", "6_9", ">_o", "@_@", "^_^", "o_o", "u_u", "x_x", "|_|", "||_||", ] 
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--score-slider-step", type=float, default=0.05)
+    parser.add_argument("--score-general-threshold", type=float, default=0.35)
+    parser.add_argument("--score-character-threshold", type=float, default=0.85)
+    parser.add_argument("--share", action="store_true")
+    return parser.parse_args()
+
+
+def load_labels(dataframe) -> list[str]:
+    name_series = dataframe["name"]
+    name_series = name_series.map(
+        lambda x: x.replace("_", " ") if x not in kaomojis else x
+    )
+    tag_names = name_series.tolist()
+
+    rating_indexes = list(np.where(dataframe["category"] == 9)[0])
+    general_indexes = list(np.where(dataframe["category"] == 0)[0])
+    character_indexes = list(np.where(dataframe["category"] == 4)[0])
+    return tag_names, rating_indexes, general_indexes, character_indexes
+
+
+def mcut_threshold(probs):
+    """
+    Maximum Cut Thresholding (MCut)
+    Largeron, C., Moulin, C., & Gery, M. (2012). MCut: A Thresholding Strategy
+     for Multi-label Classification. In 11th International Symposium, IDA 2012
+     (pp. 172-183).
+    """
+    sorted_probs = probs[probs.argsort()[::-1]]
+    difs = sorted_probs[:-1] - sorted_probs[1:]
+    t = difs.argmax()
+    thresh = (sorted_probs[t] + sorted_probs[t + 1]) / 2
+    return thresh
+
+
+class Predictor:
+    def __init__(self):
+        self.model_target_size = None
+        self.last_loaded_repo = None
+
+    def download_model(self, model_repo):
+        csv_path = huggingface_hub.hf_hub_download(
+            model_repo,
+            LABEL_FILENAME,
+        )
+        model_path = huggingface_hub.hf_hub_download(
+            model_repo,
+            MODEL_FILENAME,
+        )
+        return csv_path, model_path
+        
+def load_model(self, model_repo):
+        if model_repo == self.last_loaded_repo:
+            return
+
+        csv_path, model_path = self.download_model(model_repo)
+
+        tags_df = pd.read_csv(csv_path)
+        sep_tags = load_labels(tags_df)
+
+        self.tag_names = sep_tags[0]
+        self.rating_indexes = sep_tags[1]
+        self.general_indexes = sep_tags[2]
+        self.character_indexes = sep_tags[3]
+
+        model = rt.InferenceSession(model_path)
+        _, height, width, _ = model.get_inputs()[0].shape
+        self.model_target_size = height
+
+        self.last_loaded_repo = model_repo
+        self.model = model
+
+    def prepare_image(self, image):
+        target_size = self.model_target_size
+
+        canvas = Image.new("RGBA", image.size, (255, 255, 255))
+        canvas.alpha_composite(image)
+        image = canvas.convert("RGB")
+
+        # 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)
+
+        # Convert PIL-native RGB to BGR
+        image_array = image_array[:, :, ::-1]
+
+        return np.expand_dims(image_array, axis=0)
+
+        
+    def predict(
+        self,
+        image,
+        model_repo,
+        general_thresh,
+        general_mcut_enabled,
+        character_thresh,
+        character_mcut_enabled,
+    ):
+        self.load_model(model_repo)
+
+        image = self.prepare_image(image)
+
+        input_name = self.model.get_inputs()[0].name
+        label_name = self.model.get_outputs()[0].name
+        preds = self.model.run([label_name], {input_name: image})[0]
+
+        labels = list(zip(self.tag_names, preds[0].astype(float)))
+
+        # First 4 labels are actually ratings: pick one with argmax
+        ratings_names = [labels[i] for i in self.rating_indexes]
+        rating = dict(ratings_names)
+
+        # Then we have general tags: pick any where prediction confidence > threshold
+        general_names = [labels[i] for i in self.general_indexes]
+
+        if general_mcut_enabled:
+            general_probs = np.array([x[1] for x in general_names])
+            general_thresh = mcut_threshold(general_probs)
+
+        general_res = [x for x in general_names if x[1] > general_thresh]
+        general_res = dict(general_res)
+
+        # Everything else is characters: pick any where prediction confidence > threshold
+        character_names = [labels[i] for i in self.character_indexes]
+
+        if character_mcut_enabled:
+            character_probs = np.array([x[1] for x in character_names])
+            character_thresh = mcut_threshold(character_probs)
+            character_thresh = max(0.15, character_thresh)
+
+        character_res = [x for x in character_names if x[1] > character_thresh]
+        character_res = dict(character_res)
+
+        sorted_general_strings = sorted(
+            general_res.items(),
+            key=lambda x: x[1],
+            reverse=True,
+        )
+        sorted_general_strings = [x[0] for x in sorted_general_strings]
+        sorted_general_strings = (
+            ", ".join(sorted_general_strings).replace("(", "\(").replace(")", "\)")
+        )
+
+        return sorted_general_strings, rating, character_res, general_res
+
+
+def main():
+    args = parse_args()
+
+    predictor = Predictor()
+
+    dropdown_list = [
         SWINV2_MODEL_DSV3_REPO,
         CONV_MODEL_DSV3_REPO,
         VIT_MODEL_DSV3_REPO,
@@ -47,7 +235,7 @@ dropdown_list = [
         CONV_MODEL_DSV2_REPO,
         CONV2_MODEL_DSV2_REPO,
         VIT_MODEL_DSV2_REPO,
-]
+    ]
 
 with gr.Blocks(css= "style.css") as app:
     # Dropdown untuk memilih model di luar tab dengan lebar kecil