Create app.py

app.py

@@ -0,0 +1,122 @@
+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)