Delete app.py

app.py

@@ -1,122 +0,0 @@
-import numpy as np
-import pandas as pd
-import streamlit as st
-import matplotlib.pyplot as plt
-
-from zebris_extractor import extract_zebris_csv
-
-st.set_page_config(page_title="Zebris — Profil & Seuils", layout="wide")
-
-st.title("Zebris — Profil biomécanique & seuils individualisés")
-
-with st.sidebar:
-    uploaded_files = st.file_uploader(
-        "Importer CSV Zebris",
-        type=["csv"],
-        accept_multiple_files=True,
-    )
-
-    volume_horaire = st.number_input(
-        "Volume horaire / semaine",
-        min_value=0.5,
-        max_value=40.0,
-        value=5.0,
-        step=0.5,
-    )
-
-if not uploaded_files:
-    st.stop()
-
-
-def avg(a, b):
-    if pd.isna(a) and pd.isna(b):
-        return np.nan
-    if pd.isna(a):
-        return float(b)
-    if pd.isna(b):
-        return float(a)
-    return (float(a) + float(b)) / 2
-
-
-def asym(a, b):
-    m = avg(a, b)
-    if pd.isna(m) or m == 0:
-        return np.nan
-    return abs(a - b) / m * 100
-
-
-def compute_metrics(row, poids):
-    poids_n = poids * 9.81
-
-    force = avg(row["Force talon G (N)"], row["Force talon D (N)"])
-    pression = avg(row["Pression talon G (N/cm²)"], row["Pression talon D (N/cm²)"])
-
-    asym_talon = asym(row["Force talon G (N)"], row["Force talon D (N)"])
-
-    return {
-        "force": force,
-        "pression": pression,
-        "asym": asym_talon,
-    }
-
-
-def compute_thresholds(poids, volume):
-    poids_n = poids * 9.81
-
-    if volume <= 3:
-        f_low, f_high = 0.25, 0.40
-        p_low, p_high = 4, 8
-    elif volume <= 6:
-        f_low, f_high = 0.22, 0.37
-        p_low, p_high = 4, 7.5
-    else:
-        f_low, f_high = 0.20, 0.35
-        p_low, p_high = 4, 7
-
-    return {
-        "force_low": f_low * poids_n,
-        "force_high": f_high * poids_n,
-        "pression_low": p_low,
-        "pression_high": p_high,
-    }
-
-
-dfs = []
-for f in uploaded_files:
-    df, _ = extract_zebris_csv(f)
-    dfs.append(df)
-
-df = pd.concat(dfs)
-
-athlete = st.selectbox("Athlète", df["Nom"].unique())
-df = df[df["Nom"] == athlete]
-
-row = df.iloc[0]
-
-poids = st.number_input("Poids", value=70.0)
-
-metrics = compute_metrics(row, poids)
-thresholds = compute_thresholds(poids, volume_horaire)
-
-st.subheader("Seuils")
-
-impact_df = pd.DataFrame({
-    "Variable": [
-        "Force talon",
-        "Pression talon",
-    ],
-    "Zone basse": [
-        f"< {thresholds['force_low']:.1f} N",
-        f"< {thresholds['pression_low']:.1f} N/cm²",
-    ],
-    "Zone normale": [
-        f"{thresholds['force_low']:.1f} à {thresholds['force_high']:.1f} N",
-        f"{thresholds['pression_low']:.1f} à {thresholds['pression_high']:.1f} N/cm²",
-    ],
-    "Zone élevée": [
-        f"> {thresholds['force_high']:.1f} N",
-        f"> {thresholds['pression_high']:.1f} N/cm²",
-    ],
-})
-
-st.dataframe(impact_df, use_container_width=True)