Upload app.py

app.py

@@ -0,0 +1,77 @@
+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)