src/streamlit_app.py

import streamlit as st
import numpy as np
import pandas as pd

# ── Constants ──────────────────────────────────────────────────────────────

LABEL_MAP = {
    0: "WALKING",
    1: "WALKING_UPSTAIRS",
    2: "WALKING_DOWNSTAIRS",
    3: "SITTING",
    4: "STANDING",
    5: "LAYING",
}

EXPLANATIONS = {
    "LAYING": "Minimal movement detected across all axes with low acceleration magnitude — consistent with a stationary horizontal posture.",
    "SITTING": "Low dynamic acceleration with a stable gravity component suggests a stationary upright posture with little body movement.",
    "STANDING": "Similar to sitting but with slight postural micro-movements. This class is often the hardest to distinguish from sitting.",
    "WALKING": "Rhythmic periodic acceleration with peaks on the vertical axis — consistent with level walking at normal cadence.",
    "WALKING_DOWNSTAIRS": "Downward gravitational shift with higher impact peaks characteristic of descending a staircase.",
    "WALKING_UPSTAIRS": "Elevated vertical acceleration effort with upward body displacement — consistent with climbing stairs.",
}

# ── Model loader ────────────────────────────────────────────────────────────

@st.cache_resource
def load_model():
    try:
        import tensorflow as tf
        from model_def import FeedForwardNetwork
        model = tf.keras.models.load_model(
            "model.keras",
            custom_objects={"FeedForwardNetwork": FeedForwardNetwork},
        )
        return model, "ready"
    except Exception as e:
        return None, f"error: {e}"

# ── Page config ─────────────────────────────────────────────────────────────

st.set_page_config(
    page_title="Human Activity Recognition",
    page_icon="🏃",
    layout="centered"
)

st.title("Human Activity Recognition")
st.markdown(
    "Deep learning classifier trained on 561 smartphone sensor features "
    "from the [UCI HAR dataset](https://www.kaggle.com/datasets/uciml/human-activity-recognition-with-smartphones). "
    "Classifies six daily activities from accelerometer and gyroscope readings."
)

# ── Sidebar ──────────────────────────────────────────────────────────────────

with st.sidebar:
    st.header("About")
    st.markdown("""
    **Dataset:** UCI Human Activity Recognition
    **Subjects:** 30 volunteers aged 19–48
    **Sensor:** Samsung Galaxy S II (waist-mounted)
    **Sampling rate:** 50Hz
    **Features:** 561 time + frequency domain features
    **Classes:** 6 activities of daily living
    """)
    st.markdown("---")
    st.markdown("**Model performance on test set**")
    st.metric("Architecture", "FFN 512→256→128")
    st.metric("Status", "FFN live · CNN pending")
    st.markdown("---")
    st.caption("DAT606 Group Assignment · Pan-Atlantic University")

# ── Model status ─────────────────────────────────────────────────────────────

model, model_status = load_model()

if model_status != "ready":
    st.warning(f"Model not loaded — {model_status}")

# ── Tabs ─────────────────────────────────────────────────────────────────────

tab1, tab2 = st.tabs(["Select a Sample", "Upload Phyphox CSV"])

# ── Tab 1: Sample selector ───────────────────────────────────────────────────

with tab1:
    st.subheader("Select a pre-loaded test sample")
    st.caption(
        "Each sample is one 2.56-second window of sensor data "
        "from a test subject the model has never seen during training."
    )

    try:
        samples_df = pd.read_csv("data/samples.csv")
        feature_cols = [
            c for c in samples_df.columns
            if c not in ["Activity", "subject"]
        ]

        sample_labels = [
            f"Sample {i+1}  —  {row['Activity']}"
            for i, (_, row) in enumerate(samples_df.iterrows())
        ]

        selected = st.selectbox("Choose a sample:", sample_labels)
        selected_idx = sample_labels.index(selected)
        selected_row = samples_df.iloc[selected_idx]
        true_label = selected_row["Activity"]
        feature_vector = selected_row[feature_cols].values.astype(np.float32)

        col1, col2 = st.columns(2)
        with col1:
            st.metric("True Activity", true_label)
        with col2:
            st.metric("Feature count", len(feature_vector))

        if st.button("Classify this sample", type="primary"):
            if model_status == "no_model":
                st.error("Model not loaded — cannot predict yet.")
            else:
                arr = feature_vector.reshape(1, -1)
                probs = model.predict(arr, verbose=0)[0]
                pred_idx = int(np.argmax(probs))
                pred_label = LABEL_MAP[pred_idx]
                confidence = float(probs[pred_idx]) * 100
                correct = pred_label == true_label

                st.markdown("---")
                st.subheader("Result")

                if correct:
                    st.success(
                        f"**{pred_label}** · {confidence:.1f}% confidence · ✓ Correct"
                    )
                else:
                    st.error(
                        f"**{pred_label}** · {confidence:.1f}% confidence · "
                        f"✗ Incorrect (true: {true_label})"
                    )

                st.markdown(f"_{EXPLANATIONS[pred_label]}_")
                st.markdown("**Confidence across all classes**")

                chart_data = pd.DataFrame({
                    "Confidence (%)": [
                        float(probs[i]) * 100 for i in range(6)
                    ]
                }, index=[LABEL_MAP[i] for i in range(6)])

                st.bar_chart(chart_data)

    except FileNotFoundError:
        st.error("Sample data file not found. Add `data/samples.csv` to the repo.")

# ── Tab 2: Phyphox upload (placeholder) ─────────────────────────────────────

with tab2:
    st.subheader("Upload Phyphox sensor recording")
    st.markdown("""
    **How to record your own data:**
    1. Install [Phyphox](https://phyphox.org/) on your phone
    2. Open the **Acceleration (without g)** and **Gyroscope** experiments
    3. Record at least 3 seconds of a single activity
    4. Export as CSV and upload below
    """)

    uploaded_file = st.file_uploader(
        "Upload Phyphox CSV export",
        type=["csv"],
        help="Export from Phyphox as CSV — must contain accelerometer and gyroscope columns"
    )

    if uploaded_file is not None:
        st.info(
            "Phyphox pipeline coming soon. "
            "Feature extraction from raw sensor readings "
            "(filtering → jerk → FFT → 561 features) is under development."
        )
        try:
            preview = pd.read_csv(uploaded_file)
            st.markdown("**File preview:**")
            st.dataframe(preview.head(10))
            st.caption(
                f"{len(preview)} rows · {len(preview.columns)} columns detected"
            )
        except Exception as e:
            st.error(f"Could not read file: {e}")