🚀 - Release working version of auto-tag

.gitignore

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+# Model
+model-resnet_custom_v3.h5
+
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README.md

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+# Auto-Tag for Anime Images
+Original code by [Epsp0](https://github.com/Epsp0) in [auto-tag-anime](https://github.com/Epsp0/auto-tag-anime)\
+Model provided by [KichangKim](https://github.com/KichangKim) in [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)
+
+## Requrements
+- Python 3.10
+
+## Instructions
+1. **Download the model**: Download from [Google Drive](https://drive.google.com/file/d/1qffwjF-BHV6MkPVliLO1jZwMQatri06v) or [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) and place it in the `./model` folder.
+2. **Install dependencies**: Run the following command to install required packages:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Usage
+```bash
+python3 auto-tag.py "/path/to/directory/"
+```
+
+## Important Disclaimer
+
+**Do not modify `tags.txt`**: This file contains all possible tags used by the model and must remain unchanged to ensure the model functions correctly.
+
+**Modify only `metadata.txt`**: If you want to control which tags should be included in the JSON output, edit the `metadata.txt` file. Add or remove tags from this file to customize the tags that will be saved to the JSON.
+
+## Simple Version
+
+For users who prefer a simpler version where character tags are mixed in with other tags and cannot be customized, please switch to the `simple-tag` branch.\
+This version includes all tags by default and does not allow modifying the tags list.
+
+To switch to the `simple-tag` branch, run:
+```bash
+git checkout simple-tag
+```
+In this version, you will get a single set of tags for each image without the option to exclude characters or specify custom tags.

auto-tag.py

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+import os
+import sys
+import time
+from tags import load_to_memory, save_to_json
+from model import DeepDanbooruModel
+
+class AddAnimeTags:
+    def __init__(self):
+        self.model = DeepDanbooruModel()
+        self.directory = None
+
+    def navigate_directory(self, path: str):
+        start_time = time.time()
+
+        if os.path.isdir(path):
+            self.directory = path
+            for root, _, files in os.walk(path):
+                for filename in files:
+                    file_path = os.path.join(root, filename)
+                    self.classify_and_add_tags(file_path)
+            self.save_tags()
+        else:
+            self.classify_and_add_tags(path)
+            self.directory = os.path.dirname(path)
+            self.save_tags()
+
+        total_time = time.time() - start_time
+        print(f"Total operation time: {total_time:.2f} seconds")
+
+    def classify_and_add_tags(self, path: str):
+        image_start_time = time.time()
+
+        status, tags, characters = self.model.classify_image(path)
+        if status == 'success':
+            load_to_memory(path, tags, characters)
+            image_time = time.time() - image_start_time
+            num_tags = len(tags)
+            num_characters = len(characters)
+            print(f"[Success] [{image_time:.2f} seconds] [{num_tags} tags, {num_characters} characters added] [{path}]")
+        else:
+            image_time = time.time() - image_start_time
+            print(f"[Failed] [{image_time:.2f} seconds] [No tags] [{path}]")
+
+    def save_tags(self):
+        if self.directory:
+            json_file_path = os.path.join(self.directory, 'tags.json')
+            save_to_json(json_file_path)
+            print(f"Tags saved to {json_file_path}")
+
+def parse_args():
+    if len(sys.argv) < 2:
+        print("Usage: python auto-tag-anime.py \"<path>\"")
+        sys.exit()
+
+    if not os.path.exists(sys.argv[1]):
+        print('Path does not exist')
+        sys.exit()
+
+if __name__ == "__main__":
+    parse_args()
+    add_anime_tags = AddAnimeTags()
+    add_anime_tags.navigate_directory(sys.argv[1])

model.py

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+import os
+import sys
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+
+class DeepDanbooruModel:
+    THRESHOLD = 0.75 # Increase this to achieve more accurate results or decrease it for less accurate results.
+    METADATA_PATH = "./tags/metadata.txt" # Modify the tags you prefer to display exclusively.
+    CHARACTERS_PATH = "./tags/characters.txt" # Modify the characters you prefer to display exclusively.
+
+    # Do not change the paths or settings below here.
+    MODEL_PATH = "./model/model-resnet_custom_v3.h5"
+    TAGS_PATH = "./model/tags.txt"
+    IMAGE_SIZE = (512, 512)
+
+    def __init__(self):
+        self.model = self.load_model()
+        self.tags = self.load_tags()
+        self.characters = self.load_characters()
+        self.metadata_tags = self.load_metadata_tags()
+
+    def load_model(self) -> tf.keras.Model:
+        print('Loading model...')
+        if not os.path.exists(self.MODEL_PATH):
+            self.model_not_found_error(self.MODEL_PATH)
+
+        try:
+            model = tf.keras.models.load_model(self.MODEL_PATH, compile=False)
+            print('Model loaded successfully.')
+        except Exception as e:
+            print(f'Failed to load the model. Error: {e}')
+            sys.exit()
+
+        return model
+
+    def load_tags(self) -> np.ndarray:
+        if not os.path.exists(self.TAGS_PATH):
+            self.model_not_found_error(self.TAGS_PATH)
+
+        try:
+            with open(self.TAGS_PATH, 'r') as tags_stream:
+                tags = np.array([tag.strip() for tag in tags_stream if tag.strip()])
+            print(f'Tags loaded successfully.')
+        except Exception as e:
+            print(f'Failed to load tags. Error: {e}')
+            sys.exit()
+
+        return tags
+
+    def load_characters(self) -> set:
+        if not os.path.exists(self.CHARACTERS_PATH):
+            self.model_not_found_error(self.CHARACTERS_PATH)
+
+        try:
+            with open(self.CHARACTERS_PATH, 'r') as characters_stream:
+                characters = {character.strip() for character in characters_stream if character.strip()}
+            print(f'Characters loaded successfully. Number of characters: {len(characters)}')
+        except Exception as e:
+            print(f'Failed to load characters. Error: {e}')
+            sys.exit()
+
+        return characters
+
+    def load_metadata_tags(self) -> set:
+        if not os.path.exists(self.METADATA_PATH):
+            self.model_not_found_error(self.METADATA_PATH)
+
+        try:
+            with open(self.METADATA_PATH, 'r') as metadata_stream:
+                metadata_tags = {tag.strip() for tag in metadata_stream if tag.strip()}
+            print(f'Metadata tags loaded successfully. Number of metadata tags: {len(metadata_tags)}')
+        except Exception as e:
+            print(f'Failed to load metadata tags. Error: {e}')
+            sys.exit()
+
+        return metadata_tags
+
+    @staticmethod
+    def model_not_found_error(path: str):
+        print(f'File not found at {path}')
+        print('Please download the required file from https://github.com/KichangKim/DeepDanbooru')
+        sys.exit()
+
+    def classify_image(self, image_path: str) -> tuple[str, list[str], list[str]]:
+        try:
+            image = self.load_image(image_path)
+        except IOError:
+            return 'fail', [], []
+
+        results = self.model.predict(np.array([image]))
+
+        if results.shape[1] != self.tags.shape[0]:
+            print("Mismatch between model output and number of tags!")
+            return 'fail', [], []
+
+        result_tags = self.get_result_tags(results.reshape(-1))
+
+        tags = [tag for tag in result_tags.keys() if tag in self.metadata_tags]
+        characters = [tag for tag in result_tags.keys() if tag in self.characters]
+
+        return 'success', tags, characters
+
+    def load_image(self, image_path: str) -> np.ndarray:
+        image = Image.open(image_path).convert('RGB').resize(self.IMAGE_SIZE)
+        return np.array(image) / 255.0
+
+    def get_result_tags(self, results: np.ndarray) -> dict[str, float]:
+        return {self.tags[i]: results[i] for i in range(len(self.tags)) if results[i] > self.THRESHOLD}