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""" |
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Adapted from https://huggingface.co/spaces/stabilityai/stable-diffusion |
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""" |
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from tensorflow import keras |
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import time |
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import gradio as gr |
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import keras_cv |
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from constants import css, img_height, img_width, num_images_to_gen |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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from huggingface_hub import from_pretrained_keras |
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PLACEHOLDER_TOKEN="<my-cat-token>" |
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MODEL_CKPT = "chansung/my-kitty@v1673362345" |
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MODEL = from_pretrained_keras(MODEL_CKPT) |
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model = keras_cv.models.StableDiffusion( |
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img_width=img_width, img_height=img_height, jit_compile=True |
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) |
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model._text_encoder = MODEL |
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model._text_encoder.compile(jit_compile=True) |
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model.tokenizer.add_tokens(PLACEHOLDER_TOKEN) |
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_ = model.text_to_image("Teddy bear", batch_size=num_images_to_gen) |
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def generate_image_fn(prompt: str, unconditional_guidance_scale: int) -> list: |
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start_time = time.time() |
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images = model.text_to_image( |
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prompt, |
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batch_size=num_images_to_gen, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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) |
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end_time = time.time() |
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print(f"Time taken: {end_time - start_time} seconds.") |
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return [image for image in images] |
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description = "This Space demonstrates a fine-tuned Stable Diffusion model." |
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article = "This Space is generated automatically from a TFX pipeline. If you are interested in, please check out the [original repository](https://github.com/deep-diver/textual-inversion-sd)." |
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demoInterface = gr.Interface( |
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generate_image_fn, |
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inputs=[ |
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gr.Textbox( |
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label="Enter your prompt", |
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max_lines=1, |
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), |
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gr.Slider(value=40, minimum=8, maximum=50, step=1), |
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], |
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outputs=gr.Gallery().style(grid=[2], height="auto"), |
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title="Generate custom images with finetuned embeddings of Stable Diffusion", |
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description=description, |
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article=article, |
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examples=[[f"an oil painting of {PLACEHOLDER_TOKEN}", 8], [f"gandalf the gray as a {PLACEHOLDER_TOKEN}", 8]], |
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allow_flagging=False, |
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) |
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def avaliable_providers(): |
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providers = [] |
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headers = { |
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"Content-Type": "application/json", |
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} |
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endpoint_url = "https://api.endpoints.huggingface.cloud/provider" |
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response = requests.get(endpoint_url, headers=headers) |
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for provider in response.json()['items']: |
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if provider['status'] == 'available': |
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providers.append(provider['vendor']) |
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return providers |
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def update_regions(provider): |
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avalialbe_regions = [] |
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headers = { |
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"Content-Type": "application/json", |
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} |
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endpoint_url = f"https://api.endpoints.huggingface.cloud/provider/{provider}/region" |
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response = requests.get(endpoint_url, headers=headers) |
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for region in response.json()['items']: |
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if region['status'] == 'available': |
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avalialbe_regions.append(f"{region['region']}/{region['label']}") |
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return gr.Dropdown.update( |
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choices=avalialbe_regions, |
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value=avalialbe_regions[0] if len(avalialbe_regions) > 0 else None |
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) |
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def update_compute_options(provider, region): |
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region = region.split("/")[0] |
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avalialbe_compute_options = [] |
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headers = { |
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"Content-Type": "application/json", |
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} |
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endpoint_url = f"https://api.endpoints.huggingface.cloud/provider/{provider}/region/{region}/compute" |
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print(endpoint_url) |
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response = requests.get(endpoint_url, headers=headers) |
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for compute in response.json()['items']: |
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if compute['status'] == 'available': |
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accelerator = compute['accelerator'] |
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numAccelerators = compute['numAccelerators'] |
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memoryGb = compute['memoryGb'].replace("Gi", "GB") |
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architecture = compute['architecture'] |
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type = f"{numAccelerators}vCPU {memoryGb} · {architecture}" if accelerator == "cpu" else f"{numAccelerators}x {architecture}" |
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avalialbe_compute_options.append( |
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f"{compute['accelerator'].upper()} [{compute['instanceSize']}] · {type}" |
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) |
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return gr.Dropdown.update( |
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choices=avalialbe_compute_options, |
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value=avalialbe_compute_options[0] if len(avalialbe_compute_options) > 0 else None |
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) |
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with gr.Blocks() as hf_endpoint: |
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providers = avaliable_providers() |
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head_sha = "v1673362345_SHA" |
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gr.Markdown( |
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""" |
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## Deploy Stable Diffusion on 🤗 Endpoint |
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--- |
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""") |
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gr.Markdown(""" |
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#### Your 🤗 Access Token |
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""") |
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hf_token_input = gr.Textbox( |
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show_label=False |
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) |
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gr.Markdown(""" |
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#### Decide the Endpoint name |
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""") |
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endpoint_name_input = gr.Textbox( |
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show_label=False |
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) |
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with gr.Row(): |
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gr.Markdown(""" |
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#### Cloud Provider |
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""") |
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gr.Markdown(""" |
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#### Cloud Region |
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""") |
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with gr.Row(): |
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provider_selector = gr.Dropdown( |
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choices=providers, |
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interactive=True, |
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show_label=False, |
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) |
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region_selector = gr.Dropdown( |
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[], |
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value="", |
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interactive=True, |
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show_label=False, |
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) |
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provider_selector.change(update_regions, inputs=provider_selector, outputs=region_selector) |
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with gr.Row(): |
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gr.Markdown(""" |
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#### Target Model |
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""") |
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gr.Markdown(""" |
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#### Target Model Version(branch) |
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""") |
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with gr.Row(): |
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repository_selector = gr.Textbox( |
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value="chansung/my-kitty", |
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interactive=False, |
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show_label=False, |
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) |
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repository_selector = gr.Textbox( |
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value=f"v1673362345/{head_sha[:7]}", |
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interactive=False, |
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show_label=False, |
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) |
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with gr.Row(): |
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gr.Markdown(""" |
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#### Task |
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""") |
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gr.Markdown(""" |
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#### Framework |
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""") |
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with gr.Row(): |
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task_selector = gr.Textbox( |
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value="Custom", |
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interactive=False, |
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show_label=False, |
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) |
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framework_selector = gr.Textbox( |
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value="TensorFlow", |
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interactive=False, |
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show_label=False, |
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) |
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gr.Markdown(""" |
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#### Select Compute Instance Type |
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""") |
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compute_selector = gr.Dropdown( |
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[], |
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value="", |
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interactive=True, |
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show_label=False, |
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) |
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region_selector.change(update_compute_options, inputs=[provider_selector, region_selector], outputs=compute_selector) |
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with gr.Row(): |
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gr.Markdown(""" |
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#### Min Number of Nodes |
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""") |
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gr.Markdown(""" |
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#### Max Number of Nodes |
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""") |
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gr.Markdown(""" |
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#### Security Level |
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""") |
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with gr.Row(): |
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min_node_selector = gr.Number( |
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value=1, |
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interactive=True, |
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show_label=False, |
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) |
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max_node_selector = gr.Number( |
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value=1, |
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interactive=True, |
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show_label=False, |
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) |
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security_selector = gr.Radio( |
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choices=["Protected", "Public", "Private"], |
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value="Public", |
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interactive=True, |
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show_label=False, |
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) |
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submit_button = gr.Button( |
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value="Submit", |
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) |
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status_txt = gr.Textbox( |
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value="any status update will be displayed here", |
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interactive=False |
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) |
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gr.TabbedInterface( |
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[demoInterface, hf_endpoint], ["Try-out", " Deploy on 🤗 Endpoint"] |
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).launch(enable_queue=True) |
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