Create app.py
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app.py
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import gradio as gr
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import io
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from PIL import Image
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import torch
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from clip_interrogator import Config, Interrogator
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config = Config()
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config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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config.blip_offload = False if torch.cuda.is_available() else True
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config.chunk_size = 2048
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config.flavor_intermediate_count = 512
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config.blip_num_beams = 64
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ci = Interrogator(config)
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def inference(input_images, mode, best_max_flavors):
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prompt_results = []
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for image_bytes in input_images:
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image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
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if mode == 'best':
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prompt_result = ci.interrogate(image, max_flavors=int(best_max_flavors))
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elif mode == 'classic':
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prompt_result = ci.interrogate_classic(image)
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else:
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prompt_result = ci.interrogate_fast(image)
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prompt_results.append(prompt_result)
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return "\n\n".join(prompt_results)
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title = """
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<div style="text-align: center; max-width: 500px; margin: 0 auto;">
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<h1 style="font-weight: 600; margin-bottom: 7px;">
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CLIP Interrogator 2.1
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</h1>
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<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
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Want to figure out what a good prompt might be to create new images like an existing one?
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<br />The CLIP Interrogator is here to get you answers!
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<br />This version is specialized for producing nice prompts for use with Stable Diffusion 2.0 using the ViT-H-14 OpenCLIP model!
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</p>
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</div>
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"""
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article = """
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<div style="text-align: center; max-width: 500px; margin: 0 auto;font-size: 94%;">
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<p>
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Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/open-clip/clip_interrogator.ipynb">Google Colab</a>
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</p>
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<p>
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Has this been helpful to you? Follow Pharma on twitter
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<a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a>
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and check out more tools at his
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<a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
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</p>
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</div>
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"""
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css = '''
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#col-container {width: 80%; margin-left: auto; margin-right: auto;}
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a {text-decoration-line: underline; font-weight: 600;}
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'''
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with gr.Blocks(css=css) as block:
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with gr.Column(elem_id="col-container"):
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gr.HTML(title)
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input_image = gr.Files(label="Inputs", file_count="multiple", type='binary', elem_id='inputs')
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with gr.Row():
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mode_input = gr.Radio(['best', 'classic', 'fast'], label='Select mode', value='best')
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flavor_input = gr.Slider(minimum=2, maximum=24, step=2, value=4, label='Best mode max flavors')
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submit_btn = gr.Button("Submit")
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output_text = gr.Textbox(label="Output Prompts", lines=10, elem_id="output_text")
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with gr.Group(elem_id="share-btn-container"):
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gr.HTML(article)
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submit_btn.click(fn=inference, inputs=[input_image, mode_input, flavor_input], outputs=[output_text], api_name="clipi2")
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block.queue().launch()
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