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Group3DActRecog
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actRecog

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

# Paths anchored to the repo root regardless of working directory
_SRC_DIR = os.path.dirname(os.path.abspath(__file__))
_REPO_ROOT = os.path.dirname(_SRC_DIR)
_SAMPLES_PATH = os.path.join(_REPO_ROOT, "data", "samples.csv")
_NORM_PATH    = os.path.join(_REPO_ROOT, "data", "norm_params.json")

# ── 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(filename: str):
    try:
        from huggingface_hub import hf_hub_download
        import tensorflow as tf
        from model_def import FeedForwardNetwork, Conv1DNetwork  # noqa: F401

        model_path = hf_hub_download(
            repo_id="Group3DActRecog/actRecog",
            filename=filename,
            repo_type="space",
        )
        model = tf.keras.models.load_model(
            model_path,
            custom_objects={
                "FeedForwardNetwork": FeedForwardNetwork,
                "Conv1DNetwork": Conv1DNetwork,
            },
        )
        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="wide",
)

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("**Models**")
    st.markdown("""
    **FFN**: Feedforward Network
    Dense(512) → Dense(256) → Dense(128)
    BatchNorm + Dropout(0.3) per layer

    **CNN**: 1D Convolutional Network
    Conv1D(64) → Conv1D(128) → Conv1D(256)
    GlobalAvgPool → Dense(128)
    """)
    st.markdown("---")
    st.caption("DAT606 Group Assignment · Pan-Atlantic University")

# ── Load both models at startup ───────────────────────────────────────────────

ffn_model, ffn_status = load_model("model.keras")
cnn_model, cnn_status = load_model("har_cnn.keras")

if ffn_status != "ready" or cnn_status != "ready":
    if ffn_status != "ready":
        st.warning(f"FFN not loaded: {ffn_status}")
    if cnn_status != "ready":
        st.warning(f"CNN not loaded: {cnn_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(_SAMPLES_PATH)
        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 ffn_status != "ready" or cnn_status != "ready":
                st.error("One or both models not loaded: cannot predict yet.")
            else:
                arr = feature_vector.reshape(1, -1)

                ffn_probs = ffn_model.predict(arr, verbose=0)[0]
                cnn_probs = cnn_model.predict(arr, verbose=0)[0]

                ffn_idx   = int(np.argmax(ffn_probs))
                cnn_idx   = int(np.argmax(cnn_probs))
                ffn_label = LABEL_MAP[ffn_idx]
                cnn_label = LABEL_MAP[cnn_idx]
                ffn_conf  = float(ffn_probs[ffn_idx]) * 100
                cnn_conf  = float(cnn_probs[cnn_idx]) * 100

                st.markdown("---")
                st.subheader("Model comparison")

                left, right = st.columns(2)

                # ── FFN column ──────────────────────────────────────────────
                with left:
                    st.markdown("#### Feedforward Network")
                    if ffn_label == true_label:
                        st.success(f"**{ffn_label}** · {ffn_conf:.1f}% confidence · ✓ Correct")
                    else:
                        st.error(f"**{ffn_label}** · {ffn_conf:.1f}% confidence · ✗ Incorrect (true: {true_label})")

                    st.markdown(f"_{EXPLANATIONS[ffn_label]}_")
                    st.markdown("**Confidence across all classes**")
                    st.bar_chart(pd.DataFrame(
                        {"Confidence (%)": [float(ffn_probs[i]) * 100 for i in range(6)]},
                        index=[LABEL_MAP[i] for i in range(6)]
                    ))

                # ── CNN column ──────────────────────────────────────────────
                with right:
                    st.markdown("#### 1D Convolutional Network")
                    if cnn_label == true_label:
                        st.success(f"**{cnn_label}** · {cnn_conf:.1f}% confidence · ✓ Correct")
                    else:
                        st.error(f"**{cnn_label}** · {cnn_conf:.1f}% confidence · ✗ Incorrect (true: {true_label})")

                    st.markdown(f"_{EXPLANATIONS[cnn_label]}_")
                    st.markdown("**Confidence across all classes**")
                    st.bar_chart(pd.DataFrame(
                        {"Confidence (%)": [float(cnn_probs[i]) * 100 for i in range(6)]},
                        index=[LABEL_MAP[i] for i in range(6)]
                    ))

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

# ── Tab 2: Phyphox upload ─────────────────────────────────────────────────────

with tab2:
    from phyphox_app_block import render_phyphox_tab
    render_phyphox_tab(ffn_model, ffn_status, cnn_model, cnn_status, _NORM_PATH)