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import json |
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import random |
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random.seed(999) |
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import torch |
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from torchvision.transforms import transforms |
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import gradio as gr |
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from datetime import datetime |
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|
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model = torch.load('model.pth', map_location=torch.device('cpu')) |
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model.eval() |
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transform = transforms.Compose([ |
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transforms.Resize((384, 384)), |
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transforms.ToTensor(), |
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transforms.Normalize( |
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mean=[ |
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0.5, |
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0.5, |
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0.5, |
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], std=[ |
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0.5, |
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0.5, |
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0.5, |
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]) |
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]) |
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with open("tags_9940.json", "r") as file: |
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allowed_tags = json.load(file) |
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allowed_tags = sorted(allowed_tags) |
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allowed_tags.append("explicit") |
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allowed_tags.append("questionable") |
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allowed_tags.append("safe") |
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def create_tags(image, threshold): |
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img = image.convert('RGB') |
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tensor = transform(img).unsqueeze(0) |
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with torch.no_grad(): |
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logits = model(tensor) |
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probabilities = torch.nn.functional.sigmoid(logits[0]) |
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indices = torch.where(probabilities > threshold)[0] |
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values = probabilities[indices] |
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temp = [] |
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tag_score = dict() |
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for i in range(indices.size(0)): |
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temp.append([allowed_tags[indices[i]], values[i].item()]) |
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tag_score[allowed_tags[indices[i]]] = values[i].item() |
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temp = [t[0] for t in temp] |
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text_no_impl = " ".join(temp) |
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current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") |
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print(f"{current_datetime}: finished.") |
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return text_no_impl, tag_score |
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|
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demo = gr.Interface( |
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create_tags, |
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inputs=[gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'), gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.30, label="Threshold")], |
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outputs=[ |
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gr.Textbox(label="Tag String"), |
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gr.Label(label="Tag Predictions", num_top_classes=200), |
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], |
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allow_flagging="never", |
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) |
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demo.launch() |
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