app.py

#!/usr/bin/env python3
import gradio as gr
import os
from clip_interrogator import Config, Interrogator
from huggingface_hub import hf_hub_download
from share_btn import community_icon_html, loading_icon_html, share_js

MODELS = ['ViT-L (best for Stable Diffusion 1.*)', 'ViT-H (best for Stable Diffusion 2.*)']

# download preprocessed files
PREPROCESS_FILES = [
    'ViT-H-14_laion2b_s32b_b79k_artists.pkl',
    'ViT-H-14_laion2b_s32b_b79k_flavors.pkl',
    'ViT-H-14_laion2b_s32b_b79k_mediums.pkl',
    'ViT-H-14_laion2b_s32b_b79k_movements.pkl',
    'ViT-H-14_laion2b_s32b_b79k_trendings.pkl',
    'ViT-L-14_openai_artists.pkl',
    'ViT-L-14_openai_flavors.pkl',
    'ViT-L-14_openai_mediums.pkl',
    'ViT-L-14_openai_movements.pkl',
    'ViT-L-14_openai_trendings.pkl',
]
print("Download preprocessed cache files...")
for file in PREPROCESS_FILES:
    path = hf_hub_download(repo_id="pharma/ci-preprocess", filename=file, cache_dir="cache")
    cache_path = os.path.dirname(path)


# load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14
config = Config(cache_path=cache_path, clip_model_path="cache", clip_model_name="ViT-L-14/openai")
ci_vitl = Interrogator(config)
ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")

# load ViT-H https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
config.blip_model = ci_vitl.blip_model
config.clip_model_name = "ViT-H-14/laion2b_s32b_b79k"
ci_vith = Interrogator(config)
ci_vith.clip_model = ci_vith.clip_model.to("cpu")


def image_analysis(image, clip_model_name):
    # move selected model to GPU and other model to CPU
    if clip_model_name == MODELS[0]:
        ci_vith.clip_model = ci_vith.clip_model.to("cpu")
        ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device)
        ci = ci_vitl
    else:
        ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")
        ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device)
        ci = ci_vith

    image = image.convert('RGB')
    image_features = ci.image_to_features(image)

    top_mediums = ci.mediums.rank(image_features, 5)
    top_artists = ci.artists.rank(image_features, 5)
    top_movements = ci.movements.rank(image_features, 5)
    top_trendings = ci.trendings.rank(image_features, 5)
    top_flavors = ci.flavors.rank(image_features, 5)

    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
    
    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks


def image_to_prompt(image, clip_model_name, mode):
    # move selected model to GPU and other model to CPU
    if clip_model_name == MODELS[0]:
        ci_vith.clip_model = ci_vith.clip_model.to("cpu")
        ci_vitl.clip_model = ci_vitl.clip_model.to(ci_vitl.device)
        ci = ci_vitl
    else:
        ci_vitl.clip_model = ci_vitl.clip_model.to("cpu")
        ci_vith.clip_model = ci_vith.clip_model.to(ci_vith.device)
        ci = ci_vith

    ci.config.blip_num_beams = 64
    ci.config.chunk_size = 2048
    ci.config.flavor_intermediate_count = 2048 if clip_model_name == MODELS[0] else 1024

    image = image.convert('RGB')
    if mode == 'best':
        prompt = ci.interrogate(image)
    elif mode == 'classic':
        prompt = ci.interrogate_classic(image)
    elif mode == 'fast':
        prompt = ci.interrogate_fast(image)
    elif mode == 'negative':
        prompt = ci.interrogate_negative(image)

    return prompt, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)


TITLE = """
    <div style="text-align: center; max-width: 650px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px;">
            CLIP Interrogator
        </h1>
        </div>
        <p style="margin-bottom: 10px; font-size: 94%">
        Want to figure out what a good prompt might be to create new images like an existing one?<br>The CLIP Interrogator is here to get you answers!
        </p>
        <p>You can skip the queue by duplicating this space and upgrading to gpu in settings: <a style='display:inline-block' href='https://huggingface.co/spaces/pharma/CLIP-Interrogator?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>
    </div>
"""

ARTICLE = """
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
    <p>
    Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a> 
    and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a> 
    from pixabay.com
    </p>

    <p>
    Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb">Google Colab</a>
    </p>

    <p>
    Has this been helpful to you? Follow me on twitter 
    <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a><br>
    and check out more tools at my
    <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
    </p>
</div>
"""

CSS = """
    #col-container {margin-left: auto; margin-right: auto;}
    a {text-decoration-line: underline; font-weight: 600;}
    .animate-spin {
        animation: spin 1s linear infinite;
    }
    @keyframes spin {
        from { transform: rotate(0deg); }
        to { transform: rotate(360deg); }
    }
    #share-btn-container {
        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
    }
    #share-btn {
        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
    }
    #share-btn * {
        all: unset;
    }
    #share-btn-container div:nth-child(-n+2){
        width: auto !important;
        min-height: 0px !important;
    }
    #share-btn-container .wrap {
        display: none !important;
    }
"""

def analyze_tab():
    with gr.Column():
        with gr.Row():
            image = gr.Image(type='pil', label="Image")
            model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
        with gr.Row():
            medium = gr.Label(label="Medium", num_top_classes=5)
            artist = gr.Label(label="Artist", num_top_classes=5)        
            movement = gr.Label(label="Movement", num_top_classes=5)
            trending = gr.Label(label="Trending", num_top_classes=5)
            flavor = gr.Label(label="Flavor", num_top_classes=5)
    button = gr.Button("Analyze", api_name="image-analysis")
    button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor])

with gr.Blocks(css=CSS) as block:
    with gr.Column(elem_id="col-container"):
        gr.HTML(TITLE)

        with gr.Tab("Prompt"):
            with gr.Row():
                input_image = gr.Image(type='pil', elem_id="input-img")
                with gr.Column():
                    input_model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
                    input_mode = gr.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Mode')
            submit_btn = gr.Button("Submit", api_name="image-to-prompt")
            output_text = gr.Textbox(label="Output", elem_id="output-txt")

            with gr.Group(elem_id="share-btn-container"):
                community_icon = gr.HTML(community_icon_html, visible=False)
                loading_icon = gr.HTML(loading_icon_html, visible=False)
                share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)

            examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
            ex = gr.Examples(
                examples=examples, 
                fn=image_to_prompt, 
                inputs=[input_image, input_model, input_mode], 
                outputs=[output_text, share_button, community_icon, loading_icon], 
                cache_examples=True, 
                run_on_click=True
            )
            ex.dataset.headers = [""]

        with gr.Tab("Analyze"):
            analyze_tab()
        
        gr.HTML(ARTICLE)

    submit_btn.click(
        fn=image_to_prompt, 
        inputs=[input_image, input_model, input_mode], 
        outputs=[output_text, share_button, community_icon, loading_icon]
    )
    share_button.click(None, [], [], _js=share_js)

block.queue(max_size=64).launch(show_api=False)