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| from transformers import ( | |
| Blip2VisionConfig, | |
| Blip2QFormerConfig, | |
| OPTConfig, | |
| Blip2Config, | |
| Blip2ForConditionalGeneration, | |
| Blip2VisionModel, | |
| Blip2Processor, | |
| AutoProcessor | |
| ) | |
| from PIL import Image | |
| import requests | |
| import torch | |
| import gradio as gr | |
| config = Blip2Config() | |
| model = Blip2ForConditionalGeneration(config) | |
| config = model.config | |
| vis_config = Blip2VisionConfig() | |
| model = Blip2VisionModel(vis_config) | |
| config_2 = model.config | |
| processor = AutoProcessor.from_pretrained('Salesforce/blip-image-captioning-large') | |
| model = Blip2ForConditionalGeneration.from_pretrained('Salesforce/blip-image-captioning-large') | |
| def captioning(image): | |
| inputs = processor(images=image, return_tensors='pt') | |
| generated_ids = model.generate(**inputs) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| return image, generated_text | |
| demo = gr.Interface( | |
| captioning, | |
| inputs=gr.Image(type="pil"), | |
| outputs = ['image', 'text'] | |
| ) | |
| demo.launch() |