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Antoine245
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c631412
Update app.py
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app.py
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import gradio as gr
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import requests
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import torch
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from transformers import AlignProcessor, AlignModel
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processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
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model = AlignModel.from_pretrained("kakaobrain/align-base")
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pipe = pipeline(model="kakaobrain/align-base")
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def image_classifier(image):
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outputs = pipe(image)
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results = {}
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for result in outputs:
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results[result['label']] = result['score']
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return results
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title = "Is it a dog"
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description = """
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"""
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import torch
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import gradio as gr
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from transformers import AlignProcessor, AlignModel
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device = "cuda" if torch.cuda.is_available() else "cpu"
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processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
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model = AlignModel.from_pretrained("kakaobrain/align-base").to(device)
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model.eval()
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def predict(image, labels):
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labels = labels.split(', ')
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inputs = processor(images=image, text=labels, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image
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probs = logits_per_image.softmax(dim=1).cpu().numpy()
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return {k: float(v) for k, v in zip(labels, probs[0])}
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description = """
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<div class="container" style="display:flex;">
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<div class="image">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/132_vit_align/align.png" alt="ALIGN performance" />
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</div>
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<div class="text">
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<p>Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/align">ALIGN</a>,
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as introduced in <a href="https://arxiv.org/abs/2102.05918"></a><i>"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision"</i>. ALIGN features a dual-encoder architecture with EfficientNet and BERT as its text and vision encoders, and learns to align visual and text representations with contrastive learning.
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Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe.
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\n\nALIGN is not open-sourced and the `kakaobrain/align-base` model used for this demo is based on the Kakao Brain implementation that follows the original paper. The model is trained on the open source [COYO](https://github.com/kakaobrain/coyo-dataset) dataset by the Kakao Brain team. To perform zero-shot image classification with ALIGN, upload an image and enter your candidate labels as free-form text separated by a comma followed by a space.</p>
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</div>
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</div>
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"""
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gr.Interface(
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fn=predict,
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inputs=[
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gr.inputs.Image(label="Image to classify", type="pil"),
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gr.inputs.Textbox(lines=1, label="Comma separated candidate labels", placeholder="Enter labels separated by ', '",)
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],
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theme="grass",
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outputs="label",
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examples=[
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["assets/cartoon.jpeg", "dinosaur, drawing, forest",],
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["assets/painting.jpeg", "watercolor painting, oil painting, boats",],
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],
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title="Zero-Shot Image Classification with ALIGN",
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description=description
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).launch()
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