Spaces:
Runtime error
Runtime error
File size: 3,530 Bytes
0970948 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
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)
"""
@st.cache(suppress_st_warning=True)
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)
|