from PIL import Image import requests import gradio as gr from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel import torch from label import predict,recursion_change_bn,load_labels,hook_feature,returnCAM,returnTF,load_model git_processor = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps") git_model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps") blip_processor = AutoProcessor.from_pretrained("jaimin/Imagecap") blip_model = BlipForConditionalGeneration.from_pretrained("jaimin/Imagecap") device = "cuda" if torch.cuda.is_available() else "cpu" git_model.to(device) blip_model.to(device) def generate_caption(processor, model, image, use_float_16=False): inputs = processor(images=image, return_tensors="pt").to(device) if use_float_16: inputs = inputs.to(torch.float16) generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] return generated_caption def generate_captions(image): #img = Image.open(image) caption_git = generate_caption(git_processor, git_model, image) caption_blip = generate_caption(blip_processor, blip_model, image) env, scene = predict(image) return env,scene,caption_git_large_textcaps, caption_blip_large outputs = [gr.outputs.Textbox(label="Environment"), gr.outputs.Textbox(label="Objects detected"), gr.outputs.Textbox(label="Caption generated by GIT"), gr.outputs.Textbox(label="Caption generated by BLIP")] title = "Image Cap with Scene" description = " Image caption with scene" interface = gr.Interface(fn=generate_captions, inputs=gr.inputs.Image(type="pil"), outputs=outputs, title=title, description=description, enable_queue=True) interface.launch(debug=True)