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| """ | |
| @author: Tan Quang Duong | |
| """ | |
| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from datasets import load_dataset | |
| from PIL import Image | |
| # setting logos in the page | |
| app_logo = Image.open("./figs/AI-driven-Solutions.png") | |
| # set page config | |
| st.set_page_config(page_title="Review Sentiment Analysis", page_icon="🚀", layout="wide") | |
| st.sidebar.image(app_logo, use_column_width=True) | |
| st.sidebar.markdown( | |
| "<h1 style='text-align: center; color: grey;'> Quang Duong </h1>", | |
| unsafe_allow_html=True, | |
| ) | |
| # model name | |
| model_name = "tanquangduong/distilbert-imdb" | |
| # Load tokenizer, model and imdb dataset from hugging face hub and add them to st.session_state | |
| if "tokenizer" not in st.session_state: | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| st.session_state["tokenizer"] = tokenizer | |
| if "model" not in st.session_state: | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| st.session_state["model"] = model | |
| if "df_imdb_test" not in st.session_state: | |
| imdb = load_dataset("imdb") | |
| df_test = pd.DataFrame(imdb["test"]) | |
| df_test = df_test.sample(frac=1) | |
| st.session_state["df_imdb_test"] = df_test | |
| st.write("# Welcome to LLM-based sentiment analysis app!👋") | |
| # st.sidebar.success("Select a demo above.") | |
| st.markdown( | |
| """ | |
| # Objective | |
| This app leverages LLM to perform **:green[sentiment analysis]** for **:green[user reviews]**. Some potential use-cases are as bellow, but not limitted to: | |
| - User reviews for drug efficiency on drug/medicin forums | |
| - User reviews for mobile applications on app stores, e.g. Google Play, App Store | |
| - User reviews for food quality on food delivery app | |
| - User reviews for product quality on e-commerce websites | |
| - etc. | |
| """ | |
| ) | |