File size: 1,760 Bytes
21ac435 d6e285e 21ac435 d6e285e 21ac435 d6e285e 21ac435 d6e285e 21ac435 d6e285e 21ac435 d6e285e 21ac435 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
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() |