working placeholder app with sample selector and phyphox tab

README.md

@@ -1,19 +1,17 @@
 ---
-title: ActRecog
-emoji: 🚀
-colorFrom: red
-colorTo: red
+title: Human Activity Recognition
+emoji: 🏃
+colorFrom: blue
+colorTo: green
 sdk: docker
-app_port: 8501
-tags:
-- streamlit
+app_file: src/streamlit_app.py
 pinned: false
-short_description: Streamlit template space
 ---
 
-# Welcome to Streamlit!
+## Human Activity Recognition
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+6-class activity classifier using 561 smartphone sensor features
+from the UCI HAR dataset. Built for DAT606 — Advanced Machine Learning,
+Pan-Atlantic University.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+**Activities:** LAYING · SITTING · STANDING · WALKING · WALKING_DOWNSTAIRS · WALKING_UPSTAIRS

requirements.txt

@@ -1,3 +1,5 @@
-altair
+streamlit
 pandas
-streamlit
+numpy
+tensorflow-cpu==2.16.2
+scipy

src/streamlit_app.py

@@ -1,40 +1,204 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ── Constants ──────────────────────────────────────────────────────────────
+
+LABEL_MAP = {
+    0: "LAYING",
+    1: "SITTING",
+    2: "STANDING",
+    3: "WALKING",
+    4: "WALKING_DOWNSTAIRS",
+    5: "WALKING_UPSTAIRS",
+}
+
+ACTIVITY_ICONS = {
+    "LAYING": "🛏️",
+    "SITTING": "🪑",
+    "STANDING": "🧍",
+    "WALKING": "🚶",
+    "WALKING_DOWNSTAIRS": "⬇️",
+    "WALKING_UPSTAIRS": "⬆️",
+}
+
+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
+        model = tf.keras.models.load_model("model.keras")
+        return model
+    except Exception:
+        return None
+
+# ── 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("Accuracy", "—")
+    st.metric("Macro F1", "—")
+    st.markdown("---")
+    st.caption("DAT606 Group Assignment · Pan-Atlantic University")
+
+# ── Model status ─────────────────────────────────────────────────────────────
+
+model = load_model()
+
+if model is None:
+    st.warning(
+        "⚠️ Model not yet available. "
+        "The interface is fully built — predictions will activate once "
+        "`model.keras` is uploaded.",
+        icon="⚠️"
+    )
+
+# ── 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",
+                f"{ACTIVITY_ICONS.get(true_label, '')} {true_label}"
+            )
+        with col2:
+            st.metric("Feature count", len(feature_vector))
+
+        if st.button("🔍 Classify this sample", type="primary"):
+            if model is None:
+                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"{ACTIVITY_ICONS.get(pred_label, '')} **{pred_label}** "
+                        f"· {confidence:.1f}% confidence · ✓ Correct"
+                    )
+                else:
+                    st.error(
+                        f"{ACTIVITY_ICONS.get(pred_label, '')} **{pred_label}** "
+                        f"· {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.",
+            icon="🔧"
+        )
+        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}")