from transformers import Blip2ForConditionalGeneration from transformers import Blip2Processor from peft import PeftModel import streamlit as st from PIL import Image #import torch import os 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 sample_img_path = "./sample_images/" #init_model_required = True def init_model(): #if init_model_required: #Preprocess input processor = Blip2Processor.from_pretrained(preprocess_ckp) #Model #Inferance on GPU device. Will give error in CPU system, as "load_in_8bit" is an setting of bitsandbytes library and only works for GPU #model = Blip2ForConditionalGeneration.from_pretrained(base_model_ckp, load_in_8bit = True, device_map = "auto") #Inferance on CPU device model = Blip2ForConditionalGeneration.from_pretrained(base_model_ckp) model = PeftModel.from_pretrained(model, peft_model_ckp) #init_model_required = False return processor, model def main(): st.title("Fashion Image Caption using BLIP2") #processor, model = init_model() #Select few sample images for the catagory of cloths option = st.selectbox('Select from sample an images', ('None','cap', 'tee', 'dress'), index = 0) st.text("OR") file_name = st.file_uploader("Upload an image") st.text(option) """ if file_name is None and option is not None: file_name = os.path.join(sample_img_path, option) 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 #Inferance on GPU. When used this on GPU will get errors like: "slow_conv2d_cpu" not implemented for 'Half'" , " Input type (float) and bias type (struct c10::Half)" #inputs = processor(images = image, return_tensors = "pt").to('cuda', torch.float16) #Inferance on CPU inputs = processor(images = image, return_tensors = "pt") 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()