from transformers import Blip2ForConditionalGeneration from transformers import Blip2Processor from peft import PeftModel import streamlit as st from PIL import Image import torch preprocess_ckp = "Salesforce/blip2-opt-2.7b" #Checkpoint path used for perprocess image base_model_ckp = "/model/blip2-opt-2.7b-fp16-sharded" #Base model checkpoint path peft_model_ckp = "/model/blip2_peft" #PEFT model checkpoint path #init_model_required = True processor = None model = None def init_model(): #if init_model_required: #Preprocess input processor = Blip2Processor.from_pretrained(preprocess_ckp) #Model model = Blip2ForConditionalGeneration.from_pretrained(base_model_ckp, load_in_8bit = True, device_map = "auto") model = PeftModel.from_pretrained(model, peft_model_ckp) #init_model_required = False def main(): st.title("Fashion Image Caption using BLIP2") init_model() file_name = st.file_uploader("Upload image") if file_name is not None: image_col, caption_text = st.columns(2) image_col.header("Image") image = Image.open(file_name) image_col.image(image, use_column_width = True) #Preprocess the image inputs = processor(images = image, return_tensors = "pt").to('cuda', torch.float16) pixel_values = inputs.pixel_values #Predict the caption for the imahe generated_ids = model.generate(pixel_values = pixel_values, max_length = 25) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] #Output the predict text caption_text.header("Generated Caption") caption_text.text(generated_caption) if __name__ == "__main__": main()