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Runtime error
| import gradio as gr | |
| from PIL import Image | |
| import requests | |
| import torch | |
| from transformers import BlipForConditionalGeneration, BlipProcessor, GenerationConfig | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| _MODEL_PATH = 'IDEA-CCNL/Taiyi-BLIP-750M-Chinese' | |
| processor = BlipProcessor.from_pretrained(_MODEL_PATH) | |
| model = BlipForConditionalGeneration.from_pretrained(_MODEL_PATH).half().eval().to(device) | |
| def inference(raw_image, model_n, question, strategy): | |
| if model_n == 'Image Captioning': | |
| image = processor(raw_image).to(device, torch.float16) | |
| with torch.no_grad(): | |
| if strategy == "Beam search": | |
| config = GenerationConfig( | |
| do_sample=False, | |
| num_beams=3, | |
| max_length=20, | |
| min_length=5, | |
| ) | |
| captions = model.generate(image, generation_config=config) | |
| else: | |
| config = GenerationConfig( | |
| do_sample=True, | |
| top_p=0.9, | |
| max_length=20, | |
| min_length=5, | |
| ) | |
| captions = model.generate(image, generation_config=config) | |
| caption = processor.decode(captions[0], skip_special_tokens=True) | |
| caption = caption.replace(' ', '') | |
| return 'caption: '+caption | |
| inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=['Image Captioning'], type="value", default="Image Captioning", label="Task"), gr.inputs.Textbox( | |
| lines=2, label="Question"), gr.inputs.Radio(choices=['Beam search', 'Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")] | |
| outputs = gr.outputs.Textbox(label="Output") | |
| title = "BLIP" | |
| description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://github.com/IDEA-CCNL/Fengshenbang-LM' target='_blank'>Github Repo</a></p>" | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[ | |
| ['starrynight.jpeg', "Image Captioning", "None", "Nucleus sampling"]]).launch(enable_queue=True) | |