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from fastai.vision.all import * |
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import gradio as gr |
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import torch |
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from diffusers import FluxPipeline |
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from huggingface_hub import login |
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login() |
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) |
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prompt = "A cat holding a sign that says hello world" |
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image = pipe( |
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prompt, |
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height=1024, |
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width=1024, |
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guidance_scale=3.5, |
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num_inference_steps=50, |
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max_sequence_length=512, |
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generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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image.save("flux-dev.png") |
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learn = load_learner('export.pkl') |
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categories = ('balsamroot', 'bladderpod', 'blazing star', 'bristlecone pine flowers', 'brittlebrush') |
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def classify_image(img): |
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pred, idx, probs = learn.predict(img) |
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return dict(zip(categories, map(float, probs))) |
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image=gr.Image(height = 192, width = 192) |
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label = gr.Label() |
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examples = ['https://www.deserthorizonnursery.com/wp-content/uploads/2024/03/Brittlebush-Encelia-Farinosa-desert-horizon-nursery.jpg','https://cdn.mos.cms.futurecdn.net/VJE7gSuQ9KWbkqEsWgX5zS.jpg'] |
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intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) |
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intf.launch(inline=False) |
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