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| import os | |
| import torch | |
| import gradio as gr | |
| from inference import CityClassifierMultiModelPipeline, get_model_path | |
| TOKEN = os.environ.get("HFS_TOKEN") | |
| HFREPO = "City96/AnimeClassifiers" | |
| MODELS = [ | |
| "CCAnime-ChromaticAberration-v1.16", | |
| "CCAnime-Compression-v1.5", | |
| ] | |
| article = """\ | |
| # About | |
| These are classifiers meant to work with anime images. | |
| For more information, you can check out the [Huggingface Hub](https://huggingface.co/city96/AnimeClassifiers) or [GitHub page](https://github.com/city96/CityClassifiers). | |
| """ | |
| info_default="""\ | |
| Include default class (unknown/negative) in output results. | |
| """ | |
| info_tiling = """\ | |
| Divide the image into parts and run classifier on each part separately. | |
| Greatly improves accuracy but slows down inference. | |
| """ | |
| info_tiling_combine = """\ | |
| How to combine the confidence scores of the different tiles. | |
| Mean averages confidence over all tiles. Median takes the value in the middle. | |
| Max/min take the score from the tile with the highest/lowest confidence respectively, but can results in multiple labels having very high/very low confidence scores. | |
| """ | |
| pipeline_args = {} | |
| if torch.cuda.is_available(): | |
| pipeline_args.update({ | |
| "device" : "cuda", | |
| "clip_dtype" : torch.float16, | |
| }) | |
| pipeline = CityClassifierMultiModelPipeline( | |
| model_paths = [get_model_path(x, HFREPO, TOKEN) for x in MODELS], | |
| config_paths = [get_model_path(x, HFREPO, TOKEN, extension="config.json") for x in MODELS], | |
| **pipeline_args, | |
| ) | |
| gr.Interface( | |
| fn = pipeline, | |
| title = "Anime Classifiers - demo", | |
| article = article, | |
| inputs = [ | |
| gr.Image(label="Input image", type="pil"), | |
| gr.Checkbox(label="Include default", value=True, info=info_default), | |
| gr.Checkbox(label="Tiling", value=True, info=info_tiling), | |
| gr.Dropdown( | |
| label = "Tiling combine strategy", | |
| choices = ["mean", "median", "max", "min"], | |
| value = "mean", | |
| type = "value", | |
| info = info_tiling_combine, | |
| ) | |
| ], | |
| outputs = [gr.Label(label=x) for x in MODELS], | |
| examples = "./examples" if os.path.isdir("./examples") else None, | |
| allow_flagging = "never", | |
| analytics_enabled = False, | |
| ).launch() | |