app.py

import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load Data
@st.cache_data
def load_data():
    return pd.read_csv("inbody_with_clusters.csv", parse_dates=["Test Date"])

df = load_data()

st.title("📊 InBody 270 Dashboard")
st.markdown("Explore trends, clusters, and body composition metrics.")

# Sidebar Filters
with st.sidebar:
    st.header("🔎 Filter")
    id_options = df['ID'].unique()
    selected_ids = st.multiselect("Select Patient ID(s)", id_options, default=list(id_options[:5]))

    gender = st.radio("Gender", options=["All", "M", "F"])
    bmi_range = st.slider("BMI Range", float(df["27. BMI (Body Mass Index)"].min()), 
                          float(df["27. BMI (Body Mass Index)"].max()), (18.5, 35.0))

# Apply Filters
filtered = df[df["ID"].isin(selected_ids)]
if gender != "All":
    filtered = filtered[filtered["3. Gender"] == gender]
filtered = filtered[(filtered["27. BMI (Body Mass Index)"] >= bmi_range[0]) & 
                    (filtered["27. BMI (Body Mass Index)"] <= bmi_range[1])]

# Line Chart: Weight/BMI/BFM over time
st.subheader("📈 Trends Over Time")
metric = st.selectbox("Select a Metric to Plot", ['6. Weight', '27. BMI (Body Mass Index)', '18. BFM (Body Fat Mass)'])

fig, ax = plt.subplots(figsize=(10, 5))
sns.lineplot(data=filtered, x="Test Date", y=metric, hue="ID", marker="o", ax=ax)
st.pyplot(fig)

# Cluster Scatterplot
st.subheader("🧬 Body Type Clusters")
fig2, ax2 = plt.subplots()
sns.scatterplot(data=filtered, x="18. BFM (Body Fat Mass)", y="24. SMM (Skeletal Muscle Mass)", 
                hue="Body Type Cluster", palette="Set1", ax=ax2)
st.pyplot(fig2)

# Summary Table
st.subheader("📋 Latest Measurements (Per Patient)")
latest = filtered.sort_values("Test Date").groupby("ID").tail(1)
st.dataframe(latest[["ID", "Test Date", "6. Weight", "27. BMI (Body Mass Index)", 
                     "18. BFM (Body Fat Mass)", "24. SMM (Skeletal Muscle Mass)",
                     "58. BMR (Basal Metabolic Rate)", "Body Type Cluster"]])

# Download option
st.download_button("📥 Download Filtered Data", latest.to_csv(index=False), file_name="filtered_inbody.csv")