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| import string | |
| import gradio as gr | |
| import requests | |
| import torch | |
| from transformers import BlipForQuestionAnswering, BlipProcessor | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") | |
| model_vqa = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(device) | |
| def inference_chat(input_image,input_text): | |
| inputs = processor(images=input_image, text=input_text,return_tensors="pt") | |
| inputs["max_length"] = 20 | |
| inputs["num_beams"] = 5 | |
| out = model_vqa.generate(**inputs) | |
| return processor.batch_decode(out, skip_special_tokens=True)[0] | |
| with gr.Blocks( | |
| css=""" | |
| .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px} | |
| #component-21 > div.wrap.svelte-w6rprc {height: 600px;} | |
| """ | |
| ) as iface: | |
| state = gr.State([]) | |
| #caption_output = None | |
| #gr.Markdown(title) | |
| #gr.Markdown(description) | |
| #gr.Markdown(article) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image_input = gr.Image(type="pil") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| caption_output = None | |
| chat_input = gr.Textbox(lines=1, label="VQA Input") | |
| with gr.Row(): | |
| clear_button = gr.Button(value="Clear", interactive=True) | |
| submit_button = gr.Button( | |
| value="Submit", interactive=True, variant="primary" | |
| ) | |
| caption_output = gr.Textbox(lines=1, label="VQA Output") | |
| submit_button.click( | |
| inference_chat, | |
| [ | |
| image_input, | |
| chat_input, | |
| ], | |
| [caption_output], | |
| ) | |
| clear_button.click( | |
| lambda: ("", [], []), | |
| [], | |
| [chat_input, state], | |
| queue=False, | |
| ) | |
| chat_input.submit( | |
| inference_chat, | |
| [ | |
| image_input, | |
| chat_input, | |
| ], | |
| [ caption_output], | |
| ) | |
| image_input.change( | |
| lambda: ("", "", []), | |
| [], | |
| [ caption_output, state], | |
| queue=False, | |
| ) | |
| # examples = gr.Examples( | |
| # examples=examples, | |
| # inputs=[image_input, chat_input], | |
| # ) | |
| iface.queue(concurrency_count=1, api_open=False, max_size=10) | |
| iface.launch(enable_queue=True) |