32 lines
927 B
Python
32 lines
927 B
Python
from transformers import ViltProcessor, ViltForQuestionAnswering
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import requests
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from PIL import Image
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import gradio as gr
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import torch
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processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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def vqa(image, question):
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inp = Image.fromarray(image.astype('uint8'), 'RGB')
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inputs = processor(inp, question, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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idx = logits.argmax(-1).item()
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return model.config.id2label[idx]
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demo = gr.Interface(fn=vqa,
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inputs=['image', 'text'],
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outputs='text',
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title = "vqa",
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examples = [['soccer.jpg', 'how many people in the picture?']])
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7023)
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