47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
from PIL import Image
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import gradio as gr
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from transformers import ViTFeatureExtractor, ViTForImageClassification
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import torch
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# Init model, transforms
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model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
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transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
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def predict(im):
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labels = {0:"0-2", 1: "3-9" , 2: "10-19", 3: "20-29", 4: "30-39", 5: "40-49", 6: "50-59", 7:"60-69",8:"more than 70"}
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# Transform our image and pass it through the model
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inputs = transforms(im, return_tensors='pt')
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output = model(**inputs)
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# Predicted Class probabilities
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proba = output.logits.softmax(1)
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# Predicted Classes
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preds = proba.argmax(1)
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values, indices = torch.topk(proba, k=5)
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return {labels[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
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inputs = [
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gr.inputs.Image(type="pil", label="Input Image")
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]
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title = "ViT-Age-Classification"
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description = "ViT-Age-Classification is used to categorize an individual's age using images"
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article = " <a href='https://huggingface.co/nateraw/vit-age-classifier'>ViT Age Classification Model Repo on Hugging Face Model Hub</a>"
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examples = ["stock_baby.webp","stock_teen.webp","stock_guy.jpg","stock_old_woman.jpg"]
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gr.Interface(
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predict,
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inputs,
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outputs = 'label',
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title=title,
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description=description,
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article=article,
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examples=examples,
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theme="huggingface",
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).launch(debug=True, enable_queue=True) |