import numpy as np
import torch
from PIL import Image
from transformers import ViltConfig, ViltProcessor, ViltForQuestionAnswering
from transformers import BlipProcessor, BlipForQuestionAnswering
import cv2
import streamlit as st

st.title("Live demo of multimodal vqa")

config = ViltConfig.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("Minqin/carets_vqa_finetuned")

orig_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

blip_processor = BlipProcessor.from_pretrained('Salesforce/blip-vqa-base')
blip_model = BlipForQuestionAnswering.from_pretrained('Salesforce/blip-vqa-base')

uploaded_file = st.file_uploader("Please upload one image", type=["jpg", "png", "bmp", "jpeg"])

question = st.text_input("Type here one question on the image")
if uploaded_file is not None:
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    opencv_img = cv2.imdecode(file_bytes, 1)
    image_cv2 = cv2.cvtColor(opencv_img, cv2.COLOR_BGR2RGB)
    st.image(image_cv2, channels="RGB")

    img = Image.fromarray(image_cv2)

    encoding = processor(images=img, text=question, return_tensors="pt")

    outputs = model(**encoding)
    logits = outputs.logits
    idx = logits.argmax(-1).item()
    pred = model.config.id2label[idx]

    orig_outputs = orig_model(**encoding)
    orig_logits = orig_outputs.logits
    idx = orig_logits.argmax(-1).item()
    orig_pred = orig_model.config.id2label[idx]

    ## BLIP
    pixel_values = blip_processor(images=img, return_tensors="pt").pixel_values
    blip_ques = blip_processor.tokenizer.cls_token + question
    batch_input_ids = blip_processor(text=blip_ques, add_special_tokens=False).input_ids
    batch_input_ids = torch.tensor(batch_input_ids).unsqueeze(0)

    generate_ids = blip_model.generate(pixel_values=pixel_values, input_ids=batch_input_ids, max_length=50)
    blip_output = blip_processor.batch_decode(generate_ids, skip_special_tokens=True)

    st.text(f"Answer of ViLT: {orig_pred}")
    st.text(f"Answer after fine-tuning: {pred}")
    st.text(f"Answer of BLIP: {blip_output[0]}")