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| import time | |
| import streamlit as st | |
| from streamlit_lottie import st_lottie | |
| from util import load_lottie, stream_data, welcome_message, introduction_message | |
| from prediction_model import prediction_model_pipeline | |
| from cluster_model import cluster_model_pipeline | |
| from regression_model import regression_model_pipeline | |
| from visualization import data_visualization | |
| from src.util import read_file_from_streamlit | |
| st.set_page_config(page_title="Streamline Analyst", page_icon=":rocket:", layout="wide") | |
| # TITLE SECTION | |
| with st.container(): | |
| st.subheader("Hello there 👋") | |
| st.title("Welcome to Streamline Analyst!") | |
| if 'initialized' not in st.session_state: | |
| st.session_state.initialized = True | |
| if st.session_state.initialized: | |
| st.session_state.welcome_message = welcome_message() | |
| st.write(stream_data(st.session_state.welcome_message)) | |
| time.sleep(0.5) | |
| st.write("[Github > ](https://github.com/Wilson-ZheLin/Streamline-Analyst)") | |
| st.session_state.initialized = False | |
| else: | |
| st.write(st.session_state.welcome_message) | |
| st.write("[Github > ](https://github.com/Wilson-ZheLin/Streamline-Analyst)") | |
| # INTRO SECTION | |
| with st.container(): | |
| st.divider() | |
| if 'lottie' not in st.session_state: | |
| st.session_state.lottie_url1, st.session_state.lottie_url2 = load_lottie() | |
| st.session_state.lottie = True | |
| left_column_r1, right_column_r1 = st.columns([6, 4]) | |
| with left_column_r1: | |
| st.header("What can Streamline Analyst do?") | |
| st.write(introduction_message()[0]) | |
| with right_column_r1: | |
| if st.session_state.lottie: | |
| st_lottie(st.session_state.lottie_url1, height=280, key="animation1") | |
| left_column_r2, _, right_column_r2 = st.columns([6, 1, 5]) | |
| with left_column_r2: | |
| if st.session_state.lottie: | |
| st_lottie(st.session_state.lottie_url2, height=200, key="animation2") | |
| with right_column_r2: | |
| st.header("Simple to Use") | |
| st.write(introduction_message()[1]) | |
| # MAIN SECTION | |
| with st.container(): | |
| st.divider() | |
| st.header("Let's Get Started") | |
| left_column, right_column = st.columns([6, 4]) | |
| with left_column: | |
| API_KEY = st.text_input( | |
| "Your API Key won't be stored or shared!", | |
| placeholder="Enter your API key here...", | |
| ) | |
| st.write("👆Your OpenAI API key:") | |
| uploaded_file = st.file_uploader("Choose a data file. Your data won't be stored as well!", accept_multiple_files=False, type=['csv', 'json', 'xls', 'xlsx']) | |
| if uploaded_file: | |
| if uploaded_file.getvalue(): | |
| uploaded_file.seek(0) | |
| st.session_state.DF_uploaded = read_file_from_streamlit(uploaded_file) | |
| st.session_state.is_file_empty = False | |
| else: | |
| st.session_state.is_file_empty = True | |
| with right_column: | |
| SELECTED_MODEL = st.selectbox( | |
| 'Which OpenAI model do you want to use?', | |
| ('GPT-4-Turbo', 'GPT-3.5-Turbo')) | |
| MODE = st.selectbox( | |
| 'Select proper data analysis mode', | |
| ('Predictive Classification', 'Clustering Model', 'Regression Model', 'Data Visualization')) | |
| st.write(f'Model selected: :green[{SELECTED_MODEL}]') | |
| st.write(f'Data analysis mode: :green[{MODE}]') | |
| # Proceed Button | |
| is_proceed_enabled = uploaded_file is not None and API_KEY != "" or uploaded_file is not None and MODE == "Data Visualization" | |
| # Initialize the 'button_clicked' state | |
| if 'button_clicked' not in st.session_state: | |
| st.session_state.button_clicked = False | |
| if st.button('Start Analysis', disabled=(not is_proceed_enabled) or st.session_state.button_clicked, type="primary"): | |
| st.session_state.button_clicked = True | |
| if "is_file_empty" in st.session_state and st.session_state.is_file_empty: | |
| st.caption('Your data file is empty!') | |
| # Start Analysis | |
| if st.session_state.button_clicked: | |
| GPT_MODEL = 4 if SELECTED_MODEL == 'GPT-4-Turbo' else 3.5 | |
| with st.container(): | |
| if "DF_uploaded" not in st.session_state: | |
| st.error("File is empty!") | |
| else: | |
| if MODE == 'Predictive Classification': | |
| prediction_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL) | |
| elif MODE == 'Clustering Model': | |
| cluster_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL) | |
| elif MODE == 'Regression Model': | |
| regression_model_pipeline(st.session_state.DF_uploaded, API_KEY, GPT_MODEL) | |
| elif MODE == 'Data Visualization': | |
| data_visualization(st.session_state.DF_uploaded) |