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"

map_sampleid_name = {
                    'dress' : '00fe223d-9d1f-4bd3-a556-7ece9d28e6fb.jpeg',
                    'earrings': '0b3862ae-f89e-419c-bc1e-57418abd4180.jpeg',
                    'sweater': '0c21ba7b-ceb6-4136-94a4-1d4394499986.jpeg',
                    'sunglasses': '0e44ec10-e53b-473a-a77f-ac8828bb5e01.jpeg',
                    'shoe': '4cd37d6d-e7ea-4c6e-aab2-af700e480bc1.jpeg',
                    'hat': '69aeb517-c66c-47b8-af7d-bdf1fde57ed0.jpeg',
                    'heels':'447abc42-6ac7-4458-a514-bdcd570b1cd1.jpeg',
                    'socks': 'd188836c-b734-4031-98e5-423d5ff1239d.jpeg',
                    'tee': 'e2d8637a-5478-429d-a2a8-3d5859dbc64d.jpeg',
                    'bracelet': 'e78518ac-0f54-4483-a233-fad6511f0b86.jpeg'
                    }
#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
    st.text("Select image:")
    option = st.selectbox('From sample', ('None', 'dress', 'earrings', 'sweater', 'sunglasses', 'shoe', 'hat', 'heels', 'socks', 'tee', 'bracelet'), index = 0)
    st.text("OR")
    file_name = st.file_uploader("Upload an image")
  
    image = None
    if file_name is not None:     

        image = Image.open(file_name)

    elif option is not 'None': 

        file_name = os.path.join(sample_img_path, map_sampleid_name[option])
        image = Image.open(file_name)

    if image is not None:

        image_col, caption_text = st.columns(2)
        image_col.header("Image")
        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()