52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
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import torch
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import requests
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from PIL import Image
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from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
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import gradio as gr
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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loc = "ydshieh/vit-gpt2-coco-en"
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feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
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tokenizer = AutoTokenizer.from_pretrained(loc)
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model = VisionEncoderDecoderModel.from_pretrained(loc)
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model.eval()
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def predict(image):
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pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
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with torch.no_grad():
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output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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total_caption = ""
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for pred in preds:
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total_caption = total_caption + pred.strip()
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total_caption = total_caption + "\r\n"
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return total_caption
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demo = gr.Interface(fn=predict,
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inputs='image',
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outputs='text',
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title = "image2text",
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theme = theme,
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examples = ['soccer.jpg'])
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if __name__ == "__main__":
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demo.queue(concurrency_count=1).launch(server_name = "0.0.0.0")
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