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#!/usr/bin/env python3 |
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import argparse |
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import gradio as gr |
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import open_clip |
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from clip_interrogator import Interrogator, Config |
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parser = argparse.ArgumentParser() |
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parser.add_argument('-s', '--share', action='store_true', help='Create a public link') |
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args = parser.parse_args() |
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ci = Interrogator(Config(cache_path="cache", clip_model_path="cache")) |
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def inference(image, mode, clip_model_name, blip_max_length, blip_num_beams): |
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if clip_model_name != ci.config.clip_model_name: |
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ci.config.clip_model_name = clip_model_name |
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ci.load_clip_model() |
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ci.config.blip_max_length = int(blip_max_length) |
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ci.config.blip_num_beams = int(blip_num_beams) |
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image = image.convert('RGB') |
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if mode == 'best': |
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return ci.interrogate(image) |
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elif mode == 'classic': |
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return ci.interrogate_classic(image) |
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else: |
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return ci.interrogate_fast(image) |
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models = ['/'.join(x) for x in open_clip.list_pretrained()] |
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inputs = [ |
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gr.inputs.Image(type='pil'), |
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gr.Radio(['best', 'classic', 'fast'], label='Mode', value='best'), |
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gr.Dropdown(models, value='ViT-L-14/openai', label='CLIP Model'), |
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gr.Number(value=32, label='Caption Max Length'), |
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gr.Number(value=64, label='Caption Num Beams'), |
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] |
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outputs = [ |
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gr.outputs.Textbox(label="Output"), |
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] |
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io = gr.Interface( |
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inference, |
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inputs, |
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outputs, |
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title="🕵️♂️ CLIP Interrogator 🕵️♂️", |
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allow_flagging=False, |
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) |
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io.launch(share=args.share) |
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