32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
from transformers import AutoProcessor, AutoModelForCausalLM
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from huggingface_hub import hf_hub_download
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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 = AutoProcessor.from_pretrained("microsoft/git-base-vqav2")
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model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vqav2")
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def vqa(image, question):
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inp = Image.fromarray(image.astype('uint8'), 'RGB')
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pixel_values = processor(images=inp, return_tensors="pt").pixel_values
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input_ids = processor(text=question, add_special_tokens=False).input_ids
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input_ids = [processor.tokenizer.cls_token_id] + input_ids
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input_ids = torch.tensor(input_ids).unsqueeze(0)
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generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
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return processor.batch_decode(generated_ids, skip_special_tokens=True)
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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 = 7024)
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