import pandas as pd
import streamlit as st

st.set_page_config(page_title="Seuils individualisés - Zebris", layout="wide")

st.title("Générateur de seuils individualisés")
st.caption("Poids + volume horaire d'entraînement → seuils personnalisés de référence")

st.subheader("Entrées athlète")

c1, c2 = st.columns(2)
with c1:
    nom = st.text_input("Nom de l'athlète", value="Lucas Martin")
    poids_kg = st.number_input("Poids (kg)", min_value=30.0, max_value=150.0, value=72.0, step=0.1)

with c2:
    sport = st.text_input("Sport", value="Course sur route")
    volume_horaire = st.number_input("Volume horaire / semaine", min_value=0.5, max_value=30.0, value=5.0, step=0.5)

poids_n = poids_kg * 9.81

# Définition de la catégorie de charge
if volume_horaire <= 3:
    charge = "faible"
elif volume_horaire <= 6:
    charge = "modérée"
else:
    charge = "élevée"

st.info(f"Catégorie de charge retenue : **{charge}**")

# Seuils selon la charge
if charge == "faible":
    force_bw_low = 0.75
    force_bw_high = 1.00
    pression_low = 20
    pression_high = 26
    cadence_low = 160
    cadence_high = 172
    contact_low = 70
    contact_high = 74
    flight_low = 26
    flight_high = 30
    asym_low = 6
    asym_high = 10
    rotation_low = 6
    rotation_high = 10

elif charge == "modérée":
    force_bw_low = 0.70
    force_bw_high = 0.95
    pression_low = 20
    pression_high = 25
    cadence_low = 165
    cadence_high = 176
    contact_low = 69
    contact_high = 73
    flight_low = 27
    flight_high = 31
    asym_low = 5
    asym_high = 9
    rotation_low = 5
    rotation_high = 9

else:
    force_bw_low = 0.65
    force_bw_high = 0.90
    pression_low = 20
    pression_high = 24
    cadence_low = 168
    cadence_high = 180
    contact_low = 68
    contact_high = 72
    flight_low = 28
    flight_high = 32
    asym_low = 4
    asym_high = 8
    rotation_low = 4
    rotation_high = 8

# Conversion en Newton
force_n_low = force_bw_low * poids_n
force_n_high = force_bw_high * poids_n

st.subheader("Résumé athlète")
r1, r2, r3 = st.columns(3)
with r1:
    st.metric("Poids", f"{poids_kg:.1f} kg")
with r2:
    st.metric("Poids en Newton", f"{poids_n:.1f} N")
with r3:
    st.metric("Charge", charge)

st.subheader("Seuils individualisés")

impact_df = pd.DataFrame({
    "Variable": [
        "Force talon normalisée",
        "Force talon convertie",
        "Pression talon",
    ],
    "Zone basse / faible": [
        f"< {force_bw_low:.2f} BW",
        f"< {force_n_low:.1f} N",
        f"< {pression_low} N/cm²",
    ],
    "Zone attendue": [
        f"{force_bw_low:.2f} à {force_bw_high:.2f} BW",
        f"{force_n_low:.1f} à {force_n_high:.1f} N",
        f"{pression_low} à {pression_high} N/cm²",
    ],
    "Zone haute / élevée": [
        f"> {force_bw_high:.2f} BW",
        f"> {force_n_high:.1f} N",
        f"> {pression_high} N/cm²",
    ],
})

dynamique_df = pd.DataFrame({
    "Variable": [
        "Cadence",
        "Temps de contact",
        "Temps de vol",
    ],
    "Zone basse / faible": [
        f"< {cadence_low} pas/min",
        f"< {contact_low} %",
        f"< {flight_low} %",
    ],
    "Zone attendue": [
        f"{cadence_low} à {cadence_high} pas/min",
        f"{contact_low} à {contact_high} %",
        f"{flight_low} à {flight_high} %",
    ],
    "Zone haute / élevée": [
        f"> {cadence_high} pas/min",
        f"> {contact_high} %",
        f"> {flight_high} %",
    ],
})

symetrie_df = pd.DataFrame({
    "Variable": [
        "Asymétrie force talon",
        "Asymétrie force avant-pied",
        "Asymétrie COP",
        "Différence rotation G/D",
    ],
    "Zone faible": [
        f"< {asym_low} %",
        f"< {asym_low} %",
        f"< {asym_low} %",
        f"< {rotation_low}°",
    ],
    "Zone modérée": [
        f"{asym_low} à {asym_high} %",
        f"{asym_low} à {asym_high} %",
        f"{asym_low} à {asym_high} %",
        f"{rotation_low} à {rotation_high}°",
    ],
    "Zone marquée": [
        f"> {asym_high} %",
        f"> {asym_high} %",
        f"> {asym_high} %",
        f"> {rotation_high}°",
    ],
})

tab1, tab2, tab3 = st.tabs(["Impact", "Dynamique", "Symétrie"])

with tab1:
    st.dataframe(impact_df, hide_index=True, use_container_width=True)

with tab2:
    st.dataframe(dynamique_df, hide_index=True, use_container_width=True)

with tab3:
    st.dataframe(symetrie_df, hide_index=True, use_container_width=True)

st.subheader("Lecture méthodologique")
st.write(
    "Ces seuils sont individualisés à partir du poids et du volume horaire d’entraînement hebdomadaire. "
    "Ils constituent une base de référence personnelle de l’athlète avant toute analyse ou recommandation."
)