src/app.py

import streamlit as st
import numpy as np
import pandas as pd
import torch
import json

from PIL import Image
from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModel
from infer import InfenceTest

## Read file all of class
with open("dataclass/inverse_labels.json", "r") as js:
     inverse_labels = json.load(js)


#  Inititalize model
model_name = "vikenkd/vqa-llm1"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_name)
model = model.to(device)

# Load tokenize
visual_feature_extractor_name = "google/vit-base-patch16-224-in21k"
textual_feature_extractor_name = "bert-base-uncased"

## Text
tokenizer = AutoTokenizer.from_pretrained(textual_feature_extractor_name)
text_encoder = AutoModel.from_pretrained(textual_feature_extractor_name)
for p in text_encoder.parameters():
    p.requires_grad = False

## Image processor
image_processor = AutoFeatureExtractor.from_pretrained(visual_feature_extractor_name)
image_encoder = AutoModel.from_pretrained(visual_feature_extractor_name)

for p in image_encoder.parameters():
    p.requires_grad = False


image_encoder = image_encoder.to(device)
text_encoder = text_encoder.to(device)

## Initialize class
infer_encoding = InfenceTest(image_encoder, 
                             text_encoder, 
                             tokenizer, 
                             image_processor,
                             device)

# Custom Website App for deploying that model

st.title("📝 Visual Question Answering Project", )

width_head = """
    <style>
        [data-testid="stApp"] {
                background: url(https://img.freepik.com/free-photo/vivid-blurred-colorful-background_58702-2655.jpg);
                background-attachment: fixed;
                background-repeat: no-repeat;
                background-size: cover;
        }
        [data-testid="StyledLinkIconContainer" span] {
            white-space : nowrap;
            background: -webkit-linear-gradient(#eee, #333);
            -webkit-background-clip: text;
            # -webkit-text-fill-color: transparent;
        }   
        .st-emotion-cache-zt5igj span {
            white-space: nowrap;
            background: linear-gradient(172deg, #c42525cf, #2130ae);
            -webkit-background-clip: text;
            background-clip: text;
            -webkit-text-fill-color: transparent;
        }

        [data-testid="element-container"]{
                text-align: -webkit-center;

        }
        [data-testid="baseButton-secondaryFormSubmit"]{
            padding: 12px 40px;
        }
    </style>
"""
st.markdown(width_head, unsafe_allow_html=True)
# Custom CSS to create a border
border_css = """
<style>
    .custom-border {
        border: 10px solid #4CAF50;  /* Green border */
        padding: 10px;
        border-radius: 5px;
    }
</style>
"""

st.markdown('<div class="custom-border">', unsafe_allow_html=True)
# st.write("This is inside a bordered box")
st1, st2 = st.columns(2)
image = None
if image == None:
    uploaded_file = st.file_uploader("Upload an article", type=("png", "jpg"))
    # Check if a file has been uploaded
    if uploaded_file is not None:
        # Open the image using PIL
        image = Image.open(uploaded_file)
        
question: str = ""
submitted = None
if image != None:
        with st.form("my_form"):
            col1, col2 = st.columns(2)
            with col1:
                question = st.text_area("Enter text:", placeholder="What is your question?")
            with col2:
                st.image(image, caption="Uploaded Image.", use_column_width=True)
            if not image or  not question:
                if not image:
                     st.info("Please upload your image.")
                elif not question:
                     st.info("Please add your question.")
            
            submitted = st.form_submit_button("Submit")     

st.markdown('</div>', unsafe_allow_html=True)

inverse_labels = None

if submitted:
    encoding_status = infer_encoding.encoding(question = question, image = image)
    answer = infer_encoding.infer(model= model, 
                         inputs_require= encoding_status, 
                         top_k= 10)
    st.write("### Answer")
    st.write(inverse_labels[answer["answer"]])
    st.write(" - With answer's probability is ")
    st.write(answer["probs"])