| import torch |
| from transformers import AutoProcessor,AutoModelForCausalLM |
| import gradio as gr |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| processor=AutoProcessor.from_pretrained("alibidaran/General_image_captioning") |
| model=AutoModelForCausalLM.from_pretrained("alibidaran/General_image_captioning").to(device) |
| def generate_caption(image,length): |
| encoded=processor(images=image, return_tensors="pt").to(device) |
| pixels=encoded['pixel_values'].to(device) |
| with torch.no_grad(): |
| generated_ids=model.generate(pixel_values=pixels,max_length=length) |
| generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| return generated_caption |
| demo=gr.Interface( |
| fn=generate_caption, |
| inputs=[ |
| gr.Image(type='pil',flagging_options=["blurry", "incorrect", "other"]), |
| gr.Slider(10,50,value=10) |
| ], |
| outputs= 'label', |
| examples=["sample.jpg","sample1.jpg","sample2.jpg","sample3.jpg","sample4.jpg"], |
| theme=gr.themes.Soft(primary_hue='purple',secondary_hue=gr.themes.colors.gray) |
| ) |
| demo.launch(show_error=True) |
