Replace streamlit app file

src/streamlit_app.py

@@ -0,0 +1,104 @@
+import streamlit as st
+import pandas as pd, joblib, json
+
+st.set_page_config(page_title="VO₂ Max & Training Readiness", page_icon="🏃", layout="centered")
+st.title("🏃 VO₂ Max & Training Readiness (Synthetic, Demo)")
+st.caption("CPU-only • Synthetic data • Not medical advice.")
+
+MODEL_PATH = "model/vo2_predictor.joblib"
+DATA_PATH = "assets/vo2max_synthetic.csv"
+
+@st.cache_resource
+def load_model():
+    return joblib.load(MODEL_PATH)
+
+@st.cache_data
+def load_sample():
+    try:
+        df = pd.read_csv(DATA_PATH)
+        return df
+    except Exception:
+        return pd.DataFrame()
+
+pipe = load_model()
+df = load_sample()
+
+with st.expander("Sample data (first 50 rows)"):
+    if not df.empty:
+        st.dataframe(df.head(50), use_container_width=True)
+    else:
+        st.info("Sample CSV not found.")
+
+st.subheader("Enter runner metrics")
+cols = st.columns(2)
+
+sex = cols[0].selectbox("Sex (0=female, 1=male)", [0,1], index=1)
+age = cols[1].slider("Age", 18, 70, 35)
+height_cm = cols[0].slider("Height (cm)", 150, 200, 172)
+weight_kg = cols[1].slider("Weight (kg)", 45, 120, 74)
+
+resting_hr = cols[0].slider("Resting HR (bpm)", 40, 100, 60)
+max_hr = cols[1].slider("Max HR (bpm)", 150, 205, 185)
+avg_hr_during_run = cols[0].slider("Avg HR During Run (bpm)", 95, 190, 150)
+hr_recovery_1min = cols[1].slider("HR Recovery 1 min (bpm drop)", 10, 55, 30)
+
+distance_km = cols[0].slider("Distance of last run (km)", 1, 30, 5)
+duration_min = cols[1].slider("Duration of last run (min)", 10, 180, 30)
+pace_min_per_km = max(3.5, min(12.0, duration_min / max(distance_km, 0.5)))
+avg_speed_kmh = 60.0 / pace_min_per_km
+elevation_gain_m = cols[0].slider("Elevation Gain (m)", 0, 600, 50)
+
+training_hours_week = cols[1].slider("Training Hours / Week", 0, 12, 4)
+avg_intensity = cols[0].slider("Avg Intensity (1–10)", 1, 10, 6)
+rest_days = cols[1].slider("Rest Days (last 7d)", 0, 4, 1)
+
+sleep_hours_last_night = cols[0].slider("Sleep Hours Last Night", 3, 10, 7)
+avg_sleep_hours_week = cols[1].slider("Avg Sleep Hours / Week", 4, 9, 7)
+sleep_quality_score = cols[0].slider("Sleep Quality (0–100)", 25, 95, 72)
+resting_hr_delta = resting_hr - 60
+
+temperature_C = cols[1].slider("Temperature (°C)", 0, 35, 18)
+humidity_pct = cols[0].slider("Humidity (%)", 15, 95, 55)
+altitude_m = cols[1].slider("Altitude (m)", 0, 2200, 200)
+
+hr_ratio = avg_hr_during_run / max_hr if max_hr>0 else 0
+training_load_index = distance_km * (avg_hr_during_run/100.0) / (duration_min/60.0 + 0.1)
+speed_per_kg = avg_speed_kmh / (weight_kg/70.0)
+
+features = {
+ "sex":sex,"age":age,"height_cm":height_cm,"weight_kg":weight_kg,
+ "bmi": weight_kg/((height_cm/100.0)**2),
+ "resting_hr":resting_hr,"max_hr":max_hr,"avg_hr_during_run":avg_hr_during_run,
+ "hr_recovery_1min":hr_recovery_1min,
+ "distance_km":distance_km,"duration_min":duration_min,
+ "pace_min_per_km":pace_min_per_km,"avg_speed_kmh":avg_speed_kmh,
+ "elevation_gain_m":elevation_gain_m,
+ "training_hours_week":training_hours_week,"avg_intensity":avg_intensity,"rest_days":rest_days,
+ "sleep_hours_last_night":sleep_hours_last_night,"avg_sleep_hours_week":avg_sleep_hours_week,
+ "sleep_quality_score":sleep_quality_score,"resting_hr_delta":resting_hr_delta,
+ "temperature_C":temperature_C,"humidity_pct":humidity_pct,"altitude_m":altitude_m,
+ "hr_ratio":hr_ratio,"training_load_index":training_load_index,"speed_per_kg":speed_per_kg
+}
+
+def coaching_tip(vo2, sleep_quality, training_hours, hrr):
+    tips = []
+    if vo2 < 38: tips.append("Prioritize easy aerobic runs (Zone 2) 3–4x/week.")
+    elif vo2 < 48: tips.append("Add 1 weekly interval (e.g., 4×4min hard, full recovery).")
+    else: tips.append("Maintain with polarized training; 80% easy, 20% hard.")
+
+    if sleep_quality < 60: tips.append("Boost sleep hygiene to 7–8h; reduce late caffeine/screens.")
+    if training_hours > 8: tips.append("High load detected — deload 10–20% this week.")
+    if hrr < 20: tips.append("Low HRR — keep intensities sub-threshold for 2–3 days.")
+    return " ".join(tips) or "Keep up the good work!"
+
+if st.button("Predict VO₂ max"):
+    x = pd.DataFrame([features])
+    pipe = load_model()
+    vo2 = float(pipe.predict(x)[0])
+    tip = coaching_tip(vo2, sleep_quality_score, training_hours_week, hr_recovery_1min)
+
+    st.metric("Estimated VO₂ max (ml/kg/min)", f"{vo2:.1f}")
+    st.success(tip)
+
+st.divider()
+st.caption("Limitations: Synthetic data; not a medical device; demo use only.")