app.py

import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px

st.set_page_config(page_title="Interactive 12 Pubs of Christmas", layout="wide")

# ------------------------------------
# Parameters & Constants
# ------------------------------------
# Julia's data
julia_weight_lbs = 120
julia_weight_kg = julia_weight_lbs * 0.45359237
julia_weight_g = julia_weight_kg * 1000
julia_r = 0.55

# Sean's data
sean_weight_kg = 86.0
sean_weight_g = sean_weight_kg * 1000
sean_r = 0.68

beta = 0.015  # g/dL/h elimination rate
g_per_unit = 8

# Drink options
# We'll define a few drink types with their volumes and ABVs. 
# For simplicity, we assume:
# - 1 UK pint = 568 ml
# - Half beer = Half pint = 284 ml
# - Shot = 30 ml
# - ABV is given in percentage.
# We'll approximate each drink's ethanol mass:
# Ethanol mass (g) = volume (ml) * density_of_ethanol(0.789 g/ml) * (ABV/100)
# For simplicity, we use a units-based approach:
# 1 unit (UK) = 10ml pure ethanol = 8g ethanol
# Units = (Volume in ml * ABV%) / 1000 (approx UK definition: 1 unit = 10ml pure ethanol)
# So ethanol in grams = Units * 8.

drink_options = {
    "Guinness (Pint)": {"volume_ml": 568, "abv": 4.2},
    "Heineken (Pint)": {"volume_ml": 568, "abv": 5.0},
    "Stella Artois (Pint)": {"volume_ml": 568, "abv": 4.8},
    "Half Beer (Generic)": {"volume_ml": 284, "abv": 4.0},
    "Vodka Shot": {"volume_ml": 30, "abv": 40.0},
    "Whiskey Shot": {"volume_ml": 30, "abv": 40.0},
    "Cider (Pint)": {"volume_ml": 568, "abv": 4.5},
    "Red Wine (175ml)": {"volume_ml": 175, "abv": 12.0}
}

# A function to calculate ethanol grams from a given drink
def alcohol_in_grams(volume_ml, abv_percent):
    # 1 unit (UK) ~ (volume_ml * abv) / 1000 * (8g/unit)
    units = (volume_ml * abv_percent) / 1000
    return units * 8  # grams of ethanol

def bac_calculation(total_alcohol_g, W, r, t):
    # Widmark formula approximation:
    # BAC = (A/(W*r))*100 - beta*(t-1) if t>1
    bac = (total_alcohol_g / (W * r)) * 100
    if t > 1:
        bac = bac - beta*(t-1)
    return max(bac,0)

st.title("12 Pubs of Christmas - Interactive Drink Selection")

st.write("""
Select the drinks you (Julia) and Sean will consume each hour over a 6-hour period.
We'll estimate your BAC levels and show you comparisons.

**Note:** These are rough estimates and not medically accurate. Actual BAC varies by individual.
""")

hours = 6

col_drinks = st.columns(hours)
selected_drinks = []
st.subheader("Select Drinks for Each Hour")
for i in range(hours):
    with col_drinks[i]:
        drink_choice = st.selectbox(
            f"Hour {i+1} Drink",
            list(drink_options.keys()),
            key=f"drink_hour_{i}"
        )
        selected_drinks.append(drink_choice)

# Calculate total alcohol consumed per hour
alcohol_by_hour = []
for i, d in enumerate(selected_drinks):
    vol = drink_options[d]["volume_ml"]
    abv = drink_options[d]["abv"]
    grams = alcohol_in_grams(vol, abv)
    alcohol_by_hour.append(grams)

# Cumulative alcohol and BAC calculations
time_points = list(range(hours+1))  # from 0 to 6
julia_bac = []
sean_bac = []
julia_alc_over_time = []
sean_alc_over_time = []

for t in time_points:
    # total alcohol consumed up to hour t
    total_alc = sum(alcohol_by_hour[:t])
    # Julia BAC
    julia_bac_val = bac_calculation(total_alc, julia_weight_g, julia_r, t)
    julia_bac.append(julia_bac_val)
    # Sean BAC
    sean_bac_val = bac_calculation(total_alc, sean_weight_g, sean_r, t)
    sean_bac.append(sean_bac_val)

df = pd.DataFrame({
    "Hour": time_points,
    "Julia_BAC": julia_bac,
    "Sean_BAC": sean_bac,
    "Total_Alcohol_Consumed_g": [sum(alcohol_by_hour[:t]) for t in time_points]
})

# Calculate difference
df["BAC_Difference"] = df["Julia_BAC"] - df["Sean_BAC"]

# Plot 1: BAC over time
st.subheader("BAC over Time")
fig_bac = go.Figure()
fig_bac.add_trace(go.Scatter(x=df["Hour"], y=df["Julia_BAC"], mode='lines+markers', name="Julia BAC", line=dict(color='firebrick', width=3)))
fig_bac.add_trace(go.Scatter(x=df["Hour"], y=df["Sean_BAC"], mode='lines+markers', name="Sean BAC", line=dict(color='royalblue', width=3)))
fig_bac.update_layout(title="Estimated BAC vs. Time (Hours)", xaxis_title="Hours", yaxis_title="BAC (%)", template="plotly_white")
st.plotly_chart(fig_bac, use_container_width=True)

# Plot 2: Alcohol consumed vs BAC (Dual-Axis)
st.subheader("Alcohol Consumed and BAC")
fig_bar_line = go.Figure()

# Bar for total alcohol consumed at each hour
fig_bar_line.add_trace(go.Bar(x=df["Hour"], y=df["Total_Alcohol_Consumed_g"], name="Total Alcohol (g)", marker_color='goldenrod', yaxis='y1'))
# Julia BAC line
fig_bar_line.add_trace(go.Scatter(x=df["Hour"], y=df["Julia_BAC"], name="Julia BAC", mode='lines+markers', line=dict(color='firebrick', width=3), yaxis='y2'))
# Sean BAC line
fig_bar_line.add_trace(go.Scatter(x=df["Hour"], y=df["Sean_BAC"], name="Sean BAC", mode='lines+markers', line=dict(color='royalblue', width=3), yaxis='y2'))

fig_bar_line.update_layout(
    title="Total Alcohol (g) vs BAC (%) Over Time",
    xaxis=dict(title="Hour"),
    yaxis=dict(title="Total Alcohol (g)", side='left', range=[0, df["Total_Alcohol_Consumed_g"].max()*1.1]),
    yaxis2=dict(title="BAC (%)", side='right', overlaying='y', range=[0, df[["Julia_BAC","Sean_BAC"]].max().max()*1.1]),
    template="plotly_white"
)
st.plotly_chart(fig_bar_line, use_container_width=True)

# Plot 3: Difference in BAC
st.subheader("Difference in BAC (Julia - Sean)")
fig_diff = go.Figure(go.Bar(
    x=df["Hour"], 
    y=df["BAC_Difference"], 
    text=[f"{d:.3f}%" for d in df["BAC_Difference"]],
    textposition='outside',
    marker_color='indianred'
))
fig_diff.update_layout(
    title="Difference in BAC Between Julia and Sean Over Time",
    xaxis_title="Hour",
    yaxis_title="Difference in BAC (%)",
    template="plotly_white"
)
st.plotly_chart(fig_diff, use_container_width=True)

# Plot 4: Pie/Donut Chart of Alcohol Proportions if needed
# For a bit of fun, let's compare the total alcohol consumed to their body weight as a fraction
st.subheader("Alcohol as a Percentage of Body Weight")
# 1g = 0.001 kg
julia_pct_body_weight = (df["Total_Alcohol_Consumed_g"].iloc[-1] / julia_weight_g)*100
sean_pct_body_weight = (df["Total_Alcohol_Consumed_g"].iloc[-1] / sean_weight_g)*100

donut_data = pd.DataFrame({
    "Person": ["Julia", "Sean"],
    "Alcohol_as_pct_body_weight": [julia_pct_body_weight, sean_pct_body_weight]
})

fig_donut = px.pie(donut_data, values='Alcohol_as_pct_body_weight', names='Person', hole=0.5,
                   color='Person', color_discrete_map={"Julia":"firebrick","Sean":"royalblue"})
fig_donut.update_layout(title="Alcohol Consumed as % of Body Weight", template="plotly_white")
st.plotly_chart(fig_donut, use_container_width=True)

st.write("""
**Disclaimer:**  
These BAC values are rough estimates. Actual impairment depends on many factors including metabolism, 
recent food intake, and individual tolerance. This tool is for illustrative purposes only.
""")