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()