import streamlit as st

import pandas as pd

import dataframes as df

import analytics as a

from model_test import predict_winner

st.title("F1 Analytics")

st.write("## Driver Stats")

driver = st.selectbox("Driver",

options=([name for name in (df.drivers_df())['Full Name']]))

st.write("### Driver Standing Per Season")

d_standings = a.driver_standing_over_time(driver)

st.line_chart(d_standings, x='year', y='position', x_label='Year', y_label='Position')

st.write("### Driver Wins Per Season")

st.line_chart(a.driver_wins_over_time(driver))

st.write("## Constructor Stats")

constructor = st.selectbox("Constructor",

options=([name for name in df.constructors_df['name']]))

st.write("### Constructor Standing Per Season")

c_standings = a.constructor_standings_over_time(constructor)

st.line_chart(c_standings, x='year', y='position', x_label='Year', y_label='Position')

st.write("### Constructor Results by Circuit")

circuit = st.selectbox("Circuit", options=([name for name in df.circuit_df['name']]))

results_per_year = a.constructor_results_over_time_per_circuit(constructor, circuit)

st.line_chart(results_per_year, x='year', y='points')

st.write("### Constructor Results Over Season")

year = st.text_input("Year (1950-2020):", "2020")

st.bar_chart(a.constructor_results_across_circuits_over_year(constructor, year), x='name_circuit', y="points", x_label="circuit name")

st.write("## Head to Head")

driver1 = st.selectbox("Driver 1",

options=([name for name in (df.drivers_df())['Full Name']]))

driver2 = st.selectbox("Driver 2",

options=([name for name in (df.drivers_df())['Full Name']]))

driver1id = df.drivers_df()[df.drivers_df()['Full Name'] == driver1]['driverId'].values[0]

driver2id = df.drivers_df()[df.drivers_df()['Full Name'] == driver2]['driverId'].values[0]

results, feature_diff = predict_winner(driver1id, driver2id)

st.write("Our model predicts that:")

st.write(driver1 if results == driver1id else driver2)

st.write("will win against")

st.write(driver2 if results == driver1id else driver1)

st.write("### Feature Difference")

st.write(pd.DataFrame([feature_diff], columns=['podiums','wins','championships','pole_positions','average_position_diff','fastest_laps']))

st.write("### Model Accuracy")

st.write("Our model has an accuracy of 65%")

st.write("## All Races")

# Separate Race Information

st.write("## Race Stats")

sort_order = st.radio("Order:",("Ascending", "Descending"))

ascending = True if sort_order == "Ascending" else False

race = st.selectbox("Race",

options=([race for race in (df.races_df(ascending=ascending))['Race Identifier']]))

st.write("### Race Results")

try:

race_results_df = a.race_results(race)

st.table(race_results_df)

except Exception as e:

st.write(str(e))

st.write("#### Fastest Lap Time")

try:

best_lap_time = a.lap_times(race)

st.table(best_lap_time)

except Exception as e:

st.write(str(e))

st.write("#### Fastest Pit Stop (seconds)")

st.write("This data includes drive-through penalties")

try:

best_pit_stop = a.best_pit_stop_per_race(race)

st.table(best_pit_stop)

except Exception as e:

st.write(str(e))