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| import streamlit as st | |
| import altair as alt | |
| from constants import delay_category | |
| import gdown | |
| import pandas as pd | |
| from pydantic.v1 import BaseSettings | |
| from streamlit_pandas_profiling import st_profile_report | |
| from streamlit_extras.switch_page_button import switch_page | |
| st.set_page_config(page_title='Flight Never Delay', page_icon = '✈️', layout = 'centered', initial_sidebar_state = 'expanded') | |
| """ | |
| @st.cache(suppress_st_warning=True) | |
| def get_data(filename): | |
| url = "https://drive.google.com/uc?id=1-4OXefZDioyrobHyhtBfAFMNMem_XmLp" | |
| output = filename | |
| gdown.download(url, output, quiet=False) | |
| """ | |
| def profiler(df): | |
| pr = df.profile_report() | |
| st_profile_report(pr) | |
| def intro(): | |
| st.header("Project Flight Never Delay ✈️") | |
| st.markdown("As frequent travelers, our team members often experience flight delays and cancellations.\ | |
| However, there is no good way for us to be informed on whether a flight will be delayed or cancelled in advance.") | |
| st.markdown("To address our problem, we built a flight delay and cancellation prediction model using previous \ | |
| flight delay data from the [Bureau of Transportation Statistics - On-Time : Marketing Carrier On-Time Performance dataset](https://www.transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FGK&QO_fu146_anzr=b0-gvzr). \ | |
| We further augmented our dataset with fine-grained geographic information by mapping airports to US state \ | |
| names and coordinates (latitude and longitude) from a [Kaggle dataset](https://www.kaggle.com/datasets/usdot/flight-delays?select=airports.csv).") | |
| st.markdown("After data cleaning, we have a dataset of 1,141,693 flight delays from 2021 covering information such as flight time, \ | |
| flight carrier, flight origin and destinations, flight delay times and reasons, flight cancellations and reasons, and geographical information. You may view \ | |
| the full dataset at the bottom of the page.") | |
| # st.write("Cancel rate: 9.38 %") | |
| # st.write("Average delay time: 66.63 minutes") | |
| # df = pd.DataFrame( | |
| # {"reasons of delay": list(delay_category.keys()), "value": list(delay_category.values())} | |
| # ) | |
| # pie_chart = alt.Chart(df).mark_arc().encode( | |
| # theta=alt.Theta(field="value", type="quantitative"), | |
| # color=alt.Color(field="reasons of delay", type="nominal"), | |
| # ) | |
| # st.altair_chart(pie_chart) | |
| if __name__ == '__main__': | |
| # st.set_page_config(layout="wide") | |
| #st.set_page_config(page_title='Flight Never Delay', page_icon = '✈️', layout = 'centered', initial_sidebar_state = 'expanded') | |
| intro() | |
| st.write("Click on one of the following buttons to continue.") | |
| if st.button("Visualize correlations in the data! 📊"): | |
| switch_page("Visualization") | |
| if st.button("Predict my flight! 🧠"): | |
| switch_page("Prediction") | |
| st.markdown("""---""") | |
| filename = "data-coordinates.csv" | |
| #get_data(filename) | |
| df = pd.read_csv(filename) | |
| with st.expander("View full dataset"): | |
| st.subheader("Dataset") | |
| st.dataframe(df) | |
| # if st.checkbox("Generate data profile (this takes several minutes as the dataset is large)"): | |
| # profiler(df) | |
| # nav = st.sidebar.radio("Navigation", | |
| # ("Introduction", "Visualization", "Prediction")) | |
| # if nav == "Introduction": | |
| # intro() | |
| # elif nav == "Visualization": | |
| # vis() | |
| # else: | |
| # pred() | |
| # profiler(filename) | |