move-model-file

.gitattributes

@@ -34,4 +34,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 .DS_Store
-har_cnn.keras filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +1,4 @@
+model.keras
+*.keras
 __pycache__/
 .DS_Store

data/norm_params.json

@@ -1,2807 +0,0 @@
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-    "name": "fBodyBodyAccJerkMag-energy()",
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-    "name": "fBodyBodyAccJerkMag-entropy()",
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-    "name": "fBodyBodyAccJerkMag-maxInds",
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-    "name": "fBodyBodyAccJerkMag-meanFreq()",
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-    "name": "fBodyBodyAccJerkMag-skewness()",
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-  "527": {
-    "name": "fBodyBodyAccJerkMag-kurtosis()",
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-    "name": "fBodyBodyGyroMag-mean()",
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-    "name": "fBodyBodyGyroMag-std()",
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-    "name": "fBodyBodyGyroMag-mad()",
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-    "name": "fBodyBodyGyroMag-max()",
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-    "name": "fBodyBodyGyroMag-min()",
-    "min": 5.347162508886825e-05,
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-  },
-  "533": {
-    "name": "fBodyBodyGyroMag-sma()",
-    "min": 0.026278419794193035,
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-    "name": "fBodyBodyGyroMag-energy()",
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-    "name": "fBodyBodyGyroMag-iqr()",
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-    "name": "fBodyBodyGyroMag-entropy()",
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-    "name": "fBodyBodyGyroMag-maxInds",
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-    "name": "fBodyBodyGyroJerkMag-mean()",
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-    "name": "fBodyBodyGyroJerkMag-mad()",
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-    "name": "fBodyBodyGyroJerkMag-max()",
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-    "name": "fBodyBodyGyroJerkMag-sma()",
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-    "name": "fBodyBodyGyroJerkMag-energy()",
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-    "name": "fBodyBodyGyroJerkMag-iqr()",
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-  "549": {
-    "name": "fBodyBodyGyroJerkMag-entropy()",
-    "min": 3.080396163312534,
-    "max": 3.8726150126528016
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-  "550": {
-    "name": "fBodyBodyGyroJerkMag-maxInds",
-    "min": 1.0,
-    "max": 1.0
-  },
-  "551": {
-    "name": "fBodyBodyGyroJerkMag-meanFreq()",
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-  "552": {
-    "name": "fBodyBodyGyroJerkMag-skewness()",
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-  "553": {
-    "name": "fBodyBodyGyroJerkMag-kurtosis()",
-    "min": 4.6343499328124045,
-    "max": 57.36750813293581
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-  "554": {
-    "name": "angle(tBodyAccMean,gravity)",
-    "min": 0.03867344557044592,
-    "max": 3.119822768812346
-  },
-  "555": {
-    "name": "angle(tBodyAccJerkMean),gravityMean)",
-    "min": 0.02039567460261523,
-    "max": 3.1207880552139895
-  },
-  "556": {
-    "name": "angle(tBodyGyroMean,gravityMean)",
-    "min": 0.003262846088150804,
-    "max": 3.1346980802948465
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-  "557": {
-    "name": "angle(tBodyGyroJerkMean,gravityMean)",
-    "min": 0.010409743954799154,
-    "max": 3.132722940686013
-  },
-  "558": {
-    "name": "angle(X,gravityMean)",
-    "min": 0.0028986038760071575,
-    "max": 1.972033249755388
-  },
-  "559": {
-    "name": "angle(Y,gravityMean)",
-    "min": 0.007190985205403231,
-    "max": 2.1154456997791735
-  },
-  "560": {
-    "name": "angle(Z,gravityMean)",
-    "min": 0.1742479452264304,
-    "max": 2.9184235521275985
-  }
-}

har_cnn.keras

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:974c4a0507c5ae78f0d98c8aacbc3aad5419863e22bd130f00beea756fba573b
-size 2163544

src/model_def.py

@@ -1,7 +1,7 @@
-"""Model classes required for deserializing .keras files.
+"""FeedForwardNetwork definition required for deserializing model.keras.
 
-Must be imported before tf.keras.models.load_model() so that keras
-can resolve the registered custom classes.
+This must be imported before tf.keras.models.load_model() is called so
+that keras can resolve the registered custom class.
 """
 
 import keras
@@ -63,90 +63,3 @@ class FeedForwardNetwork(tf.keras.Model):
             "dropout_rate": self._dropout_rate,
         })
         return config
-
-
-@keras.saving.register_keras_serializable()
-class Conv1DNetwork(tf.keras.Model):
-    """1D-CNN for classification on pre-computed feature vectors.
-
-    Architecture: Reshape(561, 1)
-                  → Conv1D(64,  k=5) → BN → ReLU → MaxPool(2) → Dropout(0.3)
-                  → Conv1D(128, k=5) → BN → ReLU → MaxPool(2) → Dropout(0.3)
-                  → Conv1D(256, k=3) → BN → ReLU → GlobalAvgPool1D
-                  → Dense(128) → BN → ReLU → Dropout(0.5)
-                  → Dense(6, softmax)
-    """
-
-    def __init__(
-        self,
-        num_features,
-        num_classes,
-        dropout_rate=0.3,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self._num_features = num_features
-        self._num_classes = num_classes
-        self._dropout_rate = dropout_rate
-
-        self.reshape = tf.keras.layers.Reshape((num_features, 1))
-
-        self.conv1 = tf.keras.layers.Conv1D(64, kernel_size=5, padding="same", use_bias=False)
-        self.bn1   = tf.keras.layers.BatchNormalization()
-        self.relu1 = tf.keras.layers.ReLU()
-        self.pool1 = tf.keras.layers.MaxPooling1D(pool_size=2)
-        self.drop1 = tf.keras.layers.Dropout(dropout_rate)
-
-        self.conv2 = tf.keras.layers.Conv1D(128, kernel_size=5, padding="same", use_bias=False)
-        self.bn2   = tf.keras.layers.BatchNormalization()
-        self.relu2 = tf.keras.layers.ReLU()
-        self.pool2 = tf.keras.layers.MaxPooling1D(pool_size=2)
-        self.drop2 = tf.keras.layers.Dropout(dropout_rate)
-
-        self.conv3 = tf.keras.layers.Conv1D(256, kernel_size=3, padding="same", use_bias=False)
-        self.bn3   = tf.keras.layers.BatchNormalization()
-        self.relu3 = tf.keras.layers.ReLU()
-        self.gap   = tf.keras.layers.GlobalAveragePooling1D()
-
-        self.dense1  = tf.keras.layers.Dense(128, use_bias=False)
-        self.bn_fc   = tf.keras.layers.BatchNormalization()
-        self.relu_fc = tf.keras.layers.ReLU()
-        self.drop_fc = tf.keras.layers.Dropout(0.5)
-
-        self.output_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
-
-    def call(self, inputs, training=False):
-        x = self.reshape(inputs)
-
-        x = self.conv1(x)
-        x = self.bn1(x, training=training)
-        x = self.relu1(x)
-        x = self.pool1(x)
-        x = self.drop1(x, training=training)
-
-        x = self.conv2(x)
-        x = self.bn2(x, training=training)
-        x = self.relu2(x)
-        x = self.pool2(x)
-        x = self.drop2(x, training=training)
-
-        x = self.conv3(x)
-        x = self.bn3(x, training=training)
-        x = self.relu3(x)
-        x = self.gap(x)
-
-        x = self.dense1(x)
-        x = self.bn_fc(x, training=training)
-        x = self.relu_fc(x)
-        x = self.drop_fc(x, training=training)
-
-        return self.output_layer(x)
-
-    def get_config(self):
-        config = super().get_config()
-        config.update({
-            "num_features": self._num_features,
-            "num_classes": self._num_classes,
-            "dropout_rate": self._dropout_rate,
-        })
-        return config

src/phyphox_app_block.py

@@ -1,260 +0,0 @@
-"""Streamlit UI block for Tab 2: Phyphox live sensor upload.
-
-Call from streamlit_app.py:
-
-    from phyphox_app_block import render_phyphox_tab
-    with tab2:
-        render_phyphox_tab(ffn_model, ffn_status, cnn_model, cnn_status)
-"""
-
-import json
-import os
-
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-from phyphox_pipeline import process_phyphox_files, FS, WINDOW, STEP
-
-LABEL_MAP = {
-    0: "WALKING",
-    1: "WALKING_UPSTAIRS",
-    2: "WALKING_DOWNSTAIRS",
-    3: "SITTING",
-    4: "STANDING",
-    5: "LAYING",
-}
-
-EXPLANATIONS = {
-    "LAYING": "Minimal movement detected across all axes: consistent with a stationary horizontal posture.",
-    "SITTING": "Low dynamic acceleration with stable gravity: stationary upright posture.",
-    "STANDING": "Similar to sitting with slight postural micro-movements.",
-    "WALKING": "Rhythmic periodic acceleration on the vertical axis: level walking at normal cadence.",
-    "WALKING_DOWNSTAIRS": "Downward gravitational shift with higher impact peaks: descending stairs.",
-    "WALKING_UPSTAIRS": "Elevated vertical acceleration effort: climbing stairs.",
-}
-
-@st.cache_resource
-def _load_norm_params(norm_path: str):
-    """Load per-feature min/max from norm_params.json (keys are string indices 0-560)."""
-    with open(norm_path) as f:
-        d = json.load(f)
-    min_vals = np.array([d[str(i)]["min"] for i in range(561)], dtype=np.float32)
-    max_vals = np.array([d[str(i)]["max"] for i in range(561)], dtype=np.float32)
-    return min_vals, max_vals
-
-
-def _normalize(features: np.ndarray, min_vals: np.ndarray, max_vals: np.ndarray) -> np.ndarray:
-    """Best-effort feature-level min-max scaling to [-1, 1].
-
-    Uses per-feature min/max observed in the UCI HAR training set. This is
-    an approximation: the UCI pipeline normalises raw signals before feature
-    extraction, so physical-unit features may fall outside the training range.
-    Values are clipped before scaling to keep outputs bounded.
-    """
-    rng = max_vals - min_vals
-    rng = np.where(rng < 1e-8, 1.0, rng)          # avoid div-by-zero
-    clipped = np.clip(features, min_vals, max_vals)
-    return 2.0 * (clipped - min_vals) / rng - 1.0
-
-
-def render_phyphox_tab(
-    ffn_model, ffn_status: str,
-    cnn_model, cnn_status: str,
-    norm_params_path: str,
-) -> None:
-    st.subheader("Upload Phyphox sensor recording")
-    with st.expander("How to record and export your data - step by step", expanded=True):
-        st.markdown("""
-**Step 1 - Install Phyphox**
-Download the free [Phyphox](https://phyphox.org/) app from the Play Store (Android) or App Store (iOS).
-
-**Step 2 - Create a new experiment**
-- Open the app and tap the **+** icon in the top-right corner
-- Select **Add simple experiment**
-- In the active sensors list, add **Accelerometer** and **Gyroscope**
-- Set the **sensor rate to 50** (Hz) for both
-- Give the experiment a title (e.g. "Walking") then tap **Proceed**
-
-**Step 3 - Configure a timed run**
-- Tap the **three-dot menu** in the top-right corner and select **Timed run**
-- Set a **recording duration** (15-20 seconds recommended)
-- Optionally set a **start delay** (e.g. 5 seconds) so you have time to get into position before recording begins
-
-**Step 4 - Position the device**
-Place your phone in your **trouser pocket** or attach it at your **waist**. This mirrors how the original UCI dataset was collected and gives the most accurate results.
-
-**Step 5 - Record one activity**
-- Press the **play button** to start
-- Perform a **single activity continuously** - do not stop and restart mid-recording
-- Stay in steady motion for the full duration before stopping
-
-**Step 6 - Export the data**
-- After the recording ends, tap the **three-dot menu** and select **Export data**
-- Choose **CSV (comma separated values)** and confirm
-- You will receive a **.zip file** - unzip it to find separate CSV files for the **Accelerometer** and **Gyroscope**
-
-**Step 7 - Upload below**
-Upload each CSV file into its corresponding field below, then the pipeline will extract features and classify your activity.
-
-> ⚠️ **Important:** Each export is a fresh experiment. Do not reuse an old experiment - it will contain data from previous recordings stitched together, which will confuse the classifier.
-        """)
-
-    if "phyphox_run" not in st.session_state:
-        st.session_state.phyphox_run = 0
-
-    col1, col2 = st.columns(2)
-    with col1:
-        acc_file = st.file_uploader(
-            "Accelerometer CSV",
-            type=["csv"],
-            key=f"acc_upload_{st.session_state.phyphox_run}",
-            help="Columns: Time (s), X (m/s²), Y (m/s²), Z (m/s²)",
-        )
-    with col2:
-        gyro_file = st.file_uploader(
-            "Gyroscope CSV",
-            type=["csv"],
-            key=f"gyro_upload_{st.session_state.phyphox_run}",
-            help="Columns: Time (s), X (rad/s), Y (rad/s), Z (rad/s)",
-        )
-
-    if acc_file is not None or gyro_file is not None:
-        if st.button("Clear / new recording"):
-            st.session_state.phyphox_run += 1
-            st.rerun()
-
-    if acc_file is None or gyro_file is None:
-        st.info("Upload both files to continue.")
-        return
-
-    try:
-        import io as _io
-        _raw = acc_file.read()
-        acc_file.seek(0)
-        _df = pd.read_csv(_io.StringIO(_raw.decode("utf-8") if isinstance(_raw, bytes) else _raw))
-        _df.columns = [c.strip('"').strip() for c in _df.columns]
-        _num_cols = [c for c in _df.columns if c != "Time (s)"]
-        _mag = float((_df[_num_cols].apply(pd.to_numeric, errors="coerce") ** 2).sum(axis=1).mean() ** 0.5)
-        if _mag < 3.0:
-            st.error(
-                f"Mean accelerometer magnitude is only {_mag:.2f} m/s² — this looks like "
-                "'Acceleration **without** g'. Please re-record using **'Acceleration (with g)'** "
-                "so gravity is included in the signal."
-            )
-            return
-        else:
-            st.caption(f"Signal check: mean magnitude = {_mag:.2f} m/s² (gravity present)")
-
-        # Detect Phyphox pause/resume sessions: large gaps in the time column
-        # mean different activities were stitched together in one CSV.
-        _t = pd.to_numeric(_df["Time (s)"], errors="coerce").dropna().values
-        _gaps = np.diff(_t)
-        _expected_dt = np.median(_gaps)
-        _session_breaks = int(np.sum(_gaps > _expected_dt * 20))
-        if _session_breaks > 0:
-            st.warning(
-                f"**{_session_breaks} session break(s) detected** in this recording. "
-                "Phyphox accumulates data across pause/resume cycles — your CSV contains "
-                f"{_session_breaks + 1} separate recordings stitched together. "
-                "Only windows from a single activity will predict correctly. "
-                "To fix: tap the **trash icon** in Phyphox to clear data before each new recording."
-            )
-    except Exception:
-        pass
-
-    try:
-        with st.spinner("Extracting 561 features from sensor data…"):
-            features, pipeline_warnings = process_phyphox_files(acc_file, gyro_file)
-    except ValueError as err:
-        st.error(str(err))
-        return
-    except Exception as err:
-        st.error(f"Unexpected error during feature extraction: {err}")
-        return
-
-    for w in pipeline_warnings:
-        st.warning(w)
-
-    n_windows = len(features)
-    duration_s = (n_windows - 1) * (STEP / FS) + (WINDOW / FS)
-
-    c1, c2, c3 = st.columns(3)
-    c1.metric("Windows extracted", n_windows)
-    c2.metric("Approx. duration", f"{duration_s:.1f} s")
-    c3.metric("Features per window", 561)
-    st.caption(
-        f"Each window = {WINDOW / FS:.2f} s at {FS} Hz · "
-        f"50% overlap ({STEP / FS:.2f} s hop)"
-    )
-
-    if os.path.exists(norm_params_path):
-        min_vals, max_vals = _load_norm_params(norm_params_path)
-        features = _normalize(features, min_vals, max_vals)
-        st.caption(
-            "Accelerometer converted from m/s² to g units to match UCI training data. "
-            "Features scaled to [−1, 1] using physical-unit min/max computed from the "
-            "UCI HAR training set raw inertial signals."
-        )
-    else:
-        st.warning(
-            "norm_params.json not found: features are in physical units. "
-            "Predictions will be unreliable until normalisation is applied."
-        )
-
-    if ffn_status != "ready" and cnn_status != "ready":
-        st.warning("Models not loaded: cannot predict yet.")
-        return
-
-    st.markdown("---")
-    st.subheader("Model comparison")
-
-    left, right = st.columns(2)
-
-    def _render_model_col(col, model, status, name):
-        with col:
-            st.markdown(f"#### {name}")
-            if status != "ready":
-                st.error(f"Model not loaded: {status}")
-                return
-
-            probs_all = model.predict(features, verbose=0)       # (n_windows, 6)
-            pred_labels = [LABEL_MAP[int(np.argmax(p))] for p in probs_all]
-
-            from collections import Counter
-            # Skip first and last window for the final vote: these are typically
-            # contaminated by recording start/stop transients (person not yet
-            # in full motion, or the gravity filter still warming up).
-            core = probs_all[1:-1] if n_windows > 3 else probs_all
-            core_labels = [LABEL_MAP[int(np.argmax(p))] for p in core]
-            vote = Counter(core_labels).most_common(1)[0][0]
-            avg_conf = float(np.mean(np.max(core, axis=1))) * 100
-
-            st.success(f"**{vote}** · {avg_conf:.1f}% avg confidence")
-            st.markdown(f"_{EXPLANATIONS[vote]}_")
-
-            if n_windows > 1:
-                with st.expander(f"Per-window breakdown ({n_windows} windows)"):
-                    rows = []
-                    for i, (p, label) in enumerate(zip(probs_all, pred_labels)):
-                        t_start = i * STEP / FS
-                        is_edge = (i == 0 or i == n_windows - 1) and n_windows > 3
-                        rows.append({
-                            "Window": i + 1,
-                            "Time (s)": f"{t_start:.1f}–{t_start + WINDOW/FS:.1f}",
-                            "Prediction": label + (" *" if is_edge else ""),
-                            "Confidence": f"{float(np.max(p))*100:.1f}%",
-                        })
-                    st.dataframe(pd.DataFrame(rows), use_container_width=True)
-                    if n_windows > 3:
-                        st.caption("* Edge windows excluded from overall vote (recording start/stop transient).")
-
-            mean_probs = core.mean(axis=0)
-            st.markdown("**Average confidence across all classes**")
-            st.bar_chart(pd.DataFrame(
-                {"Confidence (%)": [float(mean_probs[i]) * 100 for i in range(6)]},
-                index=[LABEL_MAP[i] for i in range(6)],
-            ))
-
-    _render_model_col(left,  ffn_model, ffn_status, "Feedforward Network")
-    _render_model_col(right, cnn_model, cnn_status, "1D Convolutional Network")

src/phyphox_pipeline.py

@@ -1,514 +0,0 @@
-"""Phyphox sensor pipeline for Human Activity Recognition.
-
-Converts raw Phyphox accelerometer + gyroscope CSV exports into the
-561-feature vector expected by the UCI HAR classifier.
-
-Feature order matches features.txt exactly:
-  1-200   : time-domain 3-axis signals  (5 signals × 40 features)
-  201-265 : time-domain magnitudes      (5 signals × 13 features)
-  266-502 : frequency-domain 3-axis     (3 signals × 79 features)
-  503-554 : frequency-domain magnitudes (4 signals × 13 features)
-  555-561 : angle features              (7 features)
-"""
-
-import io
-import numpy as np
-import pandas as pd
-from scipy import signal as sp_signal
-from scipy.stats import skew, kurtosis as sp_kurtosis
-
-FS = 50        # target sampling rate Hz
-WINDOW = 128   # samples per window (2.56 s)
-STEP = 64      # hop size — 50% overlap
-AR_ORDER = 4   # Burg AR model order
-
-# 14 frequency band pairs (1-indexed, inclusive) applied per axis
-BANDS = [
-    (1, 8), (9, 16), (17, 24), (25, 32), (33, 40), (41, 48),
-    (49, 56), (57, 64), (1, 16), (17, 32), (33, 48), (49, 64),
-    (1, 24), (25, 48),
-]
-
-ACC_COLS = ["Time (s)", "X (m/s^2)", "Y (m/s^2)", "Z (m/s^2)"]
-GYRO_COLS = ["Time (s)", "X (rad/s)", "Y (rad/s)", "Z (rad/s)"]
-
-
-def _parse_csv(file_obj, expected_cols: list) -> pd.DataFrame:
-    """Parse a Phyphox CSV export and validate required columns.
-
-    Args:
-        file_obj: file-like object (bytes or str) from Phyphox export
-        expected_cols: list of required column names
-
-    Returns:
-        DataFrame with numeric data, NaN rows dropped
-
-    Raises:
-        ValueError: if columns are missing or file cannot be parsed
-    """
-    try:
-        raw = file_obj.read()
-        if isinstance(raw, bytes):
-            raw = raw.decode("utf-8")
-        df = pd.read_csv(io.StringIO(raw), float_precision="high")
-    except Exception as exc:
-        raise ValueError(f"Cannot parse CSV: {exc}") from exc
-
-    df.columns = [c.strip('"').strip() for c in df.columns]
-    missing = [c for c in expected_cols if c not in df.columns]
-    if missing:
-        raise ValueError(
-            f"Missing columns {missing}. Found: {list(df.columns)}. "
-            "Check you uploaded the correct Phyphox CSV (Accelerometer or Gyroscope)."
-        )
-    return df[expected_cols].apply(pd.to_numeric, errors="coerce").dropna()
-
-
-def _butter_lp(data: np.ndarray, cutoff: float, fs: float = FS, order: int = 3) -> np.ndarray:
-    """Zero-phase Butterworth low-pass filter applied along axis 0.
-
-    Args:
-        data: 1-D or 2-D array
-        cutoff: cutoff frequency in Hz
-        fs: sampling rate in Hz
-        order: filter order
-
-    Returns:
-        Filtered array, same shape as input
-    """
-    b, a = sp_signal.butter(order, cutoff / (fs / 2.0), btype="low")
-    # Use maximum safe padding — critical for low cutoffs (e.g. 0.3 Hz needs
-    # ~167 samples to settle; scipy's default 9-sample pad is far too short).
-    padlen = min(len(data) - 1, max(3 * int(fs / cutoff), 9))
-    if data.ndim == 1:
-        return sp_signal.filtfilt(b, a, data, padlen=padlen)
-    return np.column_stack(
-        [sp_signal.filtfilt(b, a, data[:, i], padlen=padlen) for i in range(data.shape[1])]
-    )
-
-
-def _median_filt(data: np.ndarray, k: int = 3) -> np.ndarray:
-    """Median filter applied along axis 0.
-
-    Args:
-        data: 1-D or 2-D array
-        k: kernel size (must be odd)
-
-    Returns:
-        Filtered array, same shape as input
-    """
-    if data.ndim == 1:
-        return sp_signal.medfilt(data, kernel_size=k)
-    return np.column_stack(
-        [sp_signal.medfilt(data[:, i], kernel_size=k) for i in range(data.shape[1])]
-    )
-
-
-def _burg_ar(x: np.ndarray, order: int = AR_ORDER) -> np.ndarray:
-    """Burg method autoregressive coefficients.
-
-    Implements the standard Burg recursion with Levinson-Durbin update.
-    Mean-centres the signal before fitting.
-
-    Args:
-        x: 1-D signal array
-        order: AR model order
-
-    Returns:
-        Array of `order` AR coefficients [a1, a2, ..., ap]
-    """
-    x = np.asarray(x, dtype=np.float64)
-    x = x - x.mean()
-    N = len(x)
-    ef = x.copy()
-    eb = x.copy()
-    a = np.zeros(order)
-
-    for m in range(1, order + 1):
-        f = ef[m:].copy()
-        b = eb[m - 1: N - 1].copy()
-
-        denom = np.dot(f, f) + np.dot(b, b) + 1e-12
-        km = -2.0 * np.dot(f, b) / denom
-
-        # Levinson-Durbin update of AR polynomial
-        a_prev = a[:m - 1].copy()
-        for j in range(m - 1):
-            a[j] = a_prev[j] + km * a_prev[m - 2 - j]
-        a[m - 1] = km
-
-        # Update forward/backward prediction errors
-        ef[m:] = f + km * b
-        eb[m - 1: N - 1] = b + km * f
-
-    return a
-
-
-def _entropy(x: np.ndarray) -> float:
-    """Normalised signal entropy via absolute-value probability distribution.
-
-    Args:
-        x: 1-D array
-
-    Returns:
-        Entropy value (>= 0)
-    """
-    total = np.abs(x).sum()
-    if total < 1e-12:
-        return 0.0
-    p = np.abs(x) / total
-    p = p[p > 0]
-    return float(-np.sum(p * np.log(p)))
-
-
-def _bands_energy(fft_mag: np.ndarray) -> np.ndarray:
-    """Energy in each of the 14 UCI HAR frequency bands (1-indexed, inclusive).
-
-    Args:
-        fft_mag: FFT magnitude array, must contain at least 64 values
-
-    Returns:
-        Array of 14 energy values
-    """
-    m = fft_mag[:64]
-    return np.array([float(np.sum(m[s - 1: e] ** 2)) for s, e in BANDS])
-
-
-def _safe_corr(a: np.ndarray, b: np.ndarray) -> float:
-    """Pearson correlation, returns 0.0 if either signal is constant.
-
-    Args:
-        a: first 1-D array
-        b: second 1-D array
-
-    Returns:
-        Correlation coefficient in [-1, 1]
-    """
-    if a.std() < 1e-10 or b.std() < 1e-10:
-        return 0.0
-    r = np.corrcoef(a, b)[0, 1]
-    return 0.0 if not np.isfinite(r) else float(r)
-
-
-def _angle(u: np.ndarray, v: np.ndarray) -> float:
-    """Angle in radians between two 3-D vectors.
-
-    Args:
-        u: first vector, shape (3,)
-        v: second vector, shape (3,)
-
-    Returns:
-        Angle in radians, or 0.0 if either vector is zero
-    """
-    un, vn = np.linalg.norm(u), np.linalg.norm(v)
-    if un < 1e-10 or vn < 1e-10:
-        return 0.0
-    return float(np.arccos(np.clip(np.dot(u, v) / (un * vn), -1.0, 1.0)))
-
-
-def _t3ax(sig: np.ndarray) -> np.ndarray:
-    """40 time-domain features from a 3-axis signal (N, 3).
-
-    Order: mean×3, std×3, mad×3, max×3, min×3, sma,
-           energy×3, iqr×3, entropy×3, arCoeff×12, correlation×3
-    """
-    N = len(sig)
-    x, y, z = sig[:, 0], sig[:, 1], sig[:, 2]
-    out = []
-
-    out += [x.mean(), y.mean(), z.mean()]
-    out += [x.std(), y.std(), z.std()]
-    out += [
-        float(np.median(np.abs(x - np.median(x)))),
-        float(np.median(np.abs(y - np.median(y)))),
-        float(np.median(np.abs(z - np.median(z)))),
-    ]
-    out += [x.max(), y.max(), z.max()]
-    out += [x.min(), y.min(), z.min()]
-    out += [float((np.abs(x) + np.abs(y) + np.abs(z)).sum() / N)]   # sma
-    out += [float(np.sum(x ** 2) / N), float(np.sum(y ** 2) / N), float(np.sum(z ** 2) / N)]
-    out += [
-        float(np.percentile(x, 75) - np.percentile(x, 25)),
-        float(np.percentile(y, 75) - np.percentile(y, 25)),
-        float(np.percentile(z, 75) - np.percentile(z, 25)),
-    ]
-    out += [_entropy(x), _entropy(y), _entropy(z)]
-    out += _burg_ar(x).tolist()
-    out += _burg_ar(y).tolist()
-    out += _burg_ar(z).tolist()
-    out += [_safe_corr(x, y), _safe_corr(x, z), _safe_corr(y, z)]
-
-    return np.array(out, dtype=np.float64)   # 40 values
-
-
-def _tmag(sig: np.ndarray) -> np.ndarray:
-    """13 time-domain features from a 1-D magnitude signal.
-
-    Order: mean, std, mad, max, min, sma, energy, iqr, entropy, arCoeff×4
-    """
-    N = len(sig)
-    out = [
-        float(sig.mean()),
-        float(sig.std()),
-        float(np.median(np.abs(sig - np.median(sig)))),
-        float(sig.max()),
-        float(sig.min()),
-        float(np.abs(sig).sum() / N),            # sma (1-D)
-        float(np.sum(sig ** 2) / N),             # energy
-        float(np.percentile(sig, 75) - np.percentile(sig, 25)),
-        _entropy(sig),
-    ]
-    out += _burg_ar(sig).tolist()
-    return np.array(out, dtype=np.float64)   # 13 values
-
-
-def _f3ax(sig: np.ndarray) -> np.ndarray:
-    """79 frequency-domain features from a 3-axis signal (N, 3).
-
-    Order: mean×3, std×3, mad×3, max×3, min×3, sma,
-           energy×3, iqr×3, entropy×3,
-           maxInds×3, meanFreq×3,
-           (skewness, kurtosis)×3 interleaved,
-           bandsEnergy×14 per axis (×3 axes = 42)
-    """
-    x, y, z = sig[:, 0], sig[:, 1], sig[:, 2]
-
-    def _fft(s):
-        return np.abs(np.fft.rfft(s))[:64]
-
-    fx, fy, fz = _fft(x), _fft(y), _fft(z)
-    bins = np.arange(1, 65, dtype=np.float64)   # 1-indexed bin numbers
-
-    def _mfreq(fm):
-        t = fm.sum()
-        return float(np.dot(bins[:len(fm)], fm) / t) if t > 1e-12 else 0.0
-
-    def _maxinds(fm):
-        return float(np.argmax(fm) + 1)   # 1-indexed
-
-    out = []
-    out += [fx.mean(), fy.mean(), fz.mean()]
-    out += [fx.std(), fy.std(), fz.std()]
-    out += [
-        float(np.median(np.abs(fx - np.median(fx)))),
-        float(np.median(np.abs(fy - np.median(fy)))),
-        float(np.median(np.abs(fz - np.median(fz)))),
-    ]
-    out += [fx.max(), fy.max(), fz.max()]
-    out += [fx.min(), fy.min(), fz.min()]
-    n = len(fx)
-    out += [float((fx + fy + fz).sum() / n)]    # sma of FFT mags
-    out += [float(np.sum(fx ** 2) / n), float(np.sum(fy ** 2) / n), float(np.sum(fz ** 2) / n)]
-    out += [
-        float(np.percentile(fx, 75) - np.percentile(fx, 25)),
-        float(np.percentile(fy, 75) - np.percentile(fy, 25)),
-        float(np.percentile(fz, 75) - np.percentile(fz, 25)),
-    ]
-    out += [_entropy(fx), _entropy(fy), _entropy(fz)]
-    out += [_maxinds(fx), _maxinds(fy), _maxinds(fz)]
-    out += [_mfreq(fx), _mfreq(fy), _mfreq(fz)]
-    # skewness/kurtosis interleaved per axis (skX,kurX, skY,kurY, skZ,kurZ)
-    out += [float(skew(fx)), float(sp_kurtosis(fx))]
-    out += [float(skew(fy)), float(sp_kurtosis(fy))]
-    out += [float(skew(fz)), float(sp_kurtosis(fz))]
-    out += _bands_energy(fx).tolist()
-    out += _bands_energy(fy).tolist()
-    out += _bands_energy(fz).tolist()
-
-    return np.array(out, dtype=np.float64)   # 79 values
-
-
-def _fmag(sig: np.ndarray) -> np.ndarray:
-    """13 frequency-domain features from a 1-D magnitude signal.
-
-    Order: mean, std, mad, max, min, sma, energy, iqr, entropy,
-           maxInds, meanFreq, skewness, kurtosis
-    """
-    fm = np.abs(np.fft.rfft(sig))[:64]
-    n = len(fm)
-    bins = np.arange(1, n + 1, dtype=np.float64)
-    total = fm.sum()
-    out = [
-        float(fm.mean()),
-        float(fm.std()),
-        float(np.median(np.abs(fm - np.median(fm)))),
-        float(fm.max()),
-        float(fm.min()),
-        float(np.abs(fm).sum() / n),
-        float(np.sum(fm ** 2) / n),
-        float(np.percentile(fm, 75) - np.percentile(fm, 25)),
-        _entropy(fm),
-        float(np.argmax(fm) + 1),                                        # maxInds (1-indexed)
-        float(np.dot(bins, fm) / total) if total > 1e-12 else 0.0,      # meanFreq
-        float(skew(fm)),
-        float(sp_kurtosis(fm)),
-    ]
-    return np.array(out, dtype=np.float64)   # 13 values
-
-
-def _window_features(
-    body_acc: np.ndarray,
-    grav_acc: np.ndarray,
-    body_jerk: np.ndarray,
-    gyro: np.ndarray,
-    gyro_jerk: np.ndarray,
-) -> np.ndarray:
-    """Extract all 561 features from one pre-processed window.
-
-    Args:
-        body_acc:  body linear acceleration (128, 3) m/s²
-        grav_acc:  gravity component        (128, 3) m/s²
-        body_jerk: body jerk                (127, 3) m/s³
-        gyro:      angular velocity         (128, 3) rad/s
-        gyro_jerk: gyro jerk                (127, 3) rad/s²
-
-    Returns:
-        1-D array of 561 features
-    """
-    # Magnitudes
-    ba_mag = np.linalg.norm(body_acc, axis=1)
-    ga_mag = np.linalg.norm(grav_acc, axis=1)
-    bj_mag = np.linalg.norm(body_jerk, axis=1)
-    gy_mag = np.linalg.norm(gyro, axis=1)
-    gj_mag = np.linalg.norm(gyro_jerk, axis=1)
-
-    parts = []
-
-    # Time 3-axis (5 × 40 = 200)
-    for sig in [body_acc, grav_acc, body_jerk, gyro, gyro_jerk]:
-        parts.append(_t3ax(sig))
-
-    # Time magnitudes (5 × 13 = 65)
-    for mag in [ba_mag, ga_mag, bj_mag, gy_mag, gj_mag]:
-        parts.append(_tmag(mag))
-
-    # Freq 3-axis (3 × 79 = 237)
-    for sig in [body_acc, body_jerk, gyro]:
-        parts.append(_f3ax(sig))
-
-    # Freq magnitudes (4 × 13 = 52)
-    for mag in [ba_mag, bj_mag, gy_mag, gj_mag]:
-        parts.append(_fmag(mag))
-
-    # Angle features (7)
-    ba_mean = body_acc.mean(axis=0)
-    ga_mean = grav_acc.mean(axis=0)
-    bj_mean = body_jerk.mean(axis=0)
-    gy_mean = gyro.mean(axis=0)
-    gj_mean = gyro_jerk.mean(axis=0)
-
-    parts.append(np.array([
-        _angle(ba_mean, ga_mean),
-        _angle(bj_mean, ga_mean),
-        _angle(gy_mean, ga_mean),
-        _angle(gj_mean, ga_mean),
-        _angle(np.array([1.0, 0.0, 0.0]), ga_mean),
-        _angle(np.array([0.0, 1.0, 0.0]), ga_mean),
-        _angle(np.array([0.0, 0.0, 1.0]), ga_mean),
-    ]))
-
-    result = np.concatenate(parts)
-    assert result.shape == (561,), f"Feature count error: got {result.shape[0]}, expected 561"
-    return result
-
-
-def process_phyphox_files(
-    acc_file,
-    gyro_file,
-) -> tuple:
-    """Convert Phyphox CSV exports to (n_windows, 561) feature array.
-
-    Pipeline:
-      1. Parse + validate both CSVs
-      2. Convert accelerometer from m/s² to g (÷ 9.80665) to match UCI training units
-      3. Interpolate onto common 50 Hz grid
-      4. Segment: 128-sample windows, 64-sample hop (50% overlap)
-      5. Per window: median filter → 20 Hz Butterworth → gravity separation
-         at 0.3 Hz → jerk → magnitudes → 561 features
-
-    Args:
-        acc_file: file-like object — Phyphox Accelerometer CSV
-                  (columns: Time (s), X (m/s^2), Y (m/s^2), Z (m/s^2))
-        gyro_file: file-like object — Phyphox Gyroscope CSV
-                   (columns: Time (s), X (rad/s), Y (rad/s), Z (rad/s))
-
-    Returns:
-        Tuple of:
-          np.ndarray shape (n_windows, 561) — raw (un-normalised) features
-          list[str]                          — warning messages
-
-    Raises:
-        ValueError: invalid format, wrong columns, or < 3 s of data
-    """
-    warnings: list = []
-
-    acc_df = _parse_csv(acc_file, ACC_COLS)
-    gyro_df = _parse_csv(gyro_file, GYRO_COLS)
-
-    acc_t = acc_df["Time (s)"].values
-    acc_xyz = acc_df[["X (m/s^2)", "Y (m/s^2)", "Z (m/s^2)"]].values / 9.80665  # m/s² → g
-    gyro_t = gyro_df["Time (s)"].values
-    gyro_xyz = gyro_df[["X (rad/s)", "Y (rad/s)", "Z (rad/s)"]].values
-
-    t0 = max(acc_t[0], gyro_t[0])
-    t1 = min(acc_t[-1], gyro_t[-1])
-    duration = t1 - t0
-
-    if duration < 3.0:
-        raise ValueError(
-            f"Recording is {duration:.2f} s — minimum 3 seconds required. "
-            "Hold the phone still or walk for at least 3 seconds before exporting."
-        )
-
-    t_grid = np.arange(t0, t1, 1.0 / FS)
-    am = (acc_t >= t0) & (acc_t <= t1)
-    gm = (gyro_t >= t0) & (gyro_t <= t1)
-
-    acc_50 = np.column_stack(
-        [np.interp(t_grid, acc_t[am], acc_xyz[am, i]) for i in range(3)]
-    )
-    gyro_50 = np.column_stack(
-        [np.interp(t_grid, gyro_t[gm], gyro_xyz[gm, i]) for i in range(3)]
-    )
-
-    n = min(len(acc_50), len(gyro_50))
-    acc_50, gyro_50 = acc_50[:n], gyro_50[:n]
-
-    n_windows = max(0, (n - WINDOW) // STEP + 1)
-    if n_windows == 0:
-        raise ValueError(
-            f"Only {n} samples ({n / FS:.1f} s) after alignment — "
-            f"need at least {WINDOW} samples ({WINDOW / FS:.1f} s)."
-        )
-
-    if duration > 60:
-        warnings.append(f"Long recording ({duration:.0f} s) — {n_windows} windows extracted.")
-
-    # Apply noise filters to the full signal before windowing.
-    acc_50  = _butter_lp(_median_filt(acc_50), cutoff=20.0)
-    gyro_50 = _butter_lp(_median_filt(gyro_50), cutoff=20.0)
-
-    all_features = []
-    dt = 1.0 / FS
-
-    for start in range(0, n - WINDOW + 1, STEP):
-        end = start + WINDOW
-        aw = acc_50[start:end]   # (128, 3)
-        gw = gyro_50[start:end]  # (128, 3)
-
-        # Gravity separation: window mean as gravity estimate.
-        # The UCI pipeline used a 0.3 Hz LP on a full continuous recording.
-        # That filter needs ~167 samples to settle; a 10-second clip gives only
-        # ~500 samples total, so the filter corrupts all but the central window.
-        # The window mean is equivalent for symmetric activities (oscillations
-        # cancel over a stride cycle) and exact for static activities.
-        grav = np.tile(aw.mean(axis=0), (WINDOW, 1))
-        body = aw - grav
-
-        # Jerk: finite difference → (127, 3)
-        body_jerk = np.diff(body, axis=0) / dt
-        gyro_jerk = np.diff(gw, axis=0) / dt
-
-        all_features.append(_window_features(body, grav, body_jerk, gw, gyro_jerk))
-
-    return np.array(all_features), warnings

src/streamlit_app.py

@@ -1,13 +1,8 @@
-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",
@@ -19,41 +14,35 @@ LABEL_MAP = {
 }
 
 EXPLANATIONS = {
-    "LAYING": "Minimal movement detected across all axes with low acceleration magnitude: consistent with a stationary horizontal posture.",
+    "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": "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.",
+    "WALKING_UPSTAIRS": "Elevated vertical acceleration effort with upward body displacement — consistent with climbing stairs.",
 }
 
+# ── Model loader ────────────────────────────────────────────────────────────
+
 @st.cache_resource
-def load_model(filename: str):
+def load_model():
     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",
-        )
+        from model_def import FeedForwardNetwork
         model = tf.keras.models.load_model(
-            model_path,
-            custom_objects={
-                "FeedForwardNetwork": FeedForwardNetwork,
-                "Conv1DNetwork": Conv1DNetwork,
-            },
+            "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="wide",
+    layout="centered"
 )
 
 st.title("Human Activity Recognition")
@@ -63,6 +52,8 @@ st.markdown(
     "Classifies six daily activities from accelerometer and gyroscope readings."
 )
 
+# ── Sidebar ──────────────────────────────────────────────────────────────────
+
 with st.sidebar:
     st.header("About")
     st.markdown("""
@@ -74,29 +65,25 @@ with st.sidebar:
     **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("**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 ─────────────────────────────────────────────────────────────
 
-ffn_model, ffn_status = load_model("model.keras")
-cnn_model, cnn_status = load_model("har_cnn.keras")
+model, model_status = load_model()
 
-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}")
+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(
@@ -105,11 +92,14 @@ with tab1:
     )
 
     try:
-        samples_df = pd.read_csv(_SAMPLES_PATH)
-        feature_cols = [c for c in samples_df.columns if c not in ["Activity", "subject"]]
+        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']}"
+            f"Sample {i+1}  —  {row['Activity']}"
             for i, (_, row) in enumerate(samples_df.iterrows())
         ]
 
@@ -126,57 +116,73 @@ with tab1:
             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.")
+            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
 
-                ffn_probs = ffn_model.predict(arr, verbose=0)[0]
-                cnn_probs = cnn_model.predict(arr, verbose=0)[0]
+                st.markdown("---")
+                st.subheader("Result")
 
-                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
+                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("---")
-                st.subheader("Model comparison")
-
-                left, right = st.columns(2)
-
-                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)]
-                    ))
-
-                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)]
-                    ))
+                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:
-    from phyphox_app_block import render_phyphox_tab
-    render_phyphox_tab(ffn_model, ffn_status, cnn_model, cnn_status, _NORM_PATH)
+    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}")