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| import streamlit as st | |
| from model import predictor | |
| from streamlit.scriptrunner import add_script_run_ctx | |
| import audiobot | |
| import chatbot | |
| import os | |
| import threading | |
| def runInThread(): | |
| print('Initialize model in thread') | |
| st.session_state['predictor'] = predictor.Predictor() | |
| print('Model is initialized') | |
| def run(): | |
| st.set_page_config( | |
| page_title='Welcome to Visual Question Answering - Bot', | |
| page_icon=':robot:', | |
| layout='wide' | |
| ) | |
| os.environ['TOKENIZERS_PARALLELISM'] = 'false' | |
| if 'thread' not in st.session_state: | |
| st.session_state.thread = threading.Thread(target=runInThread) | |
| add_script_run_ctx(st.session_state.thread) | |
| st.session_state.thread.start() | |
| st.sidebar.title('VQA Bot') | |
| st.sidebar.write(''' | |
| VQA Bot addresses the challenge of visual question answering with the chat and voice assistance. | |
| Here, we merged ViLT(Vision-and-Language Transformer) model fine-tuned on VQAv2 into T5-small (Text-to-Text Transfer Transformer). | |
| We pretrained and finetuned our model on Language transformer to get the desired result. | |
| Please use the radio buttons below to navigate. | |
| ''') | |
| selected_page = st.sidebar.radio('Go to', ('VQA Chatbot', 'VQA Audiobot')) | |
| if selected_page == 'VQA Chatbot': | |
| chatbot.show() | |
| elif selected_page == 'VQA Audiobot': | |
| audiobot.show() | |
| st.caption("Created by Madhuri Sakhare - [Github](https://github.com/msak1612/vqa_chatbot) [Linkedin](https://www.linkedin.com/in/madhuri-sakhare/)") | |
| run() | |