clean up UI

.gitattributes

@@ -33,4 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-model.keras filter=lfs diff=lfs merge=lfs -text
+.DS_Store

src/streamlit_app.py

@@ -13,15 +13,6 @@ LABEL_MAP = {
     5: "LAYING",
 }
 
-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.",
@@ -54,7 +45,7 @@ st.set_page_config(
     layout="centered"
 )
 
-st.title("🏃 Human Activity Recognition")
+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). "
@@ -86,15 +77,14 @@ model, model_status = load_model()
 
 if model_status == "no_model":
     st.warning(
-        "⚠️ Model not yet available. "
+        "Model not yet available. "
         "The interface is fully built — predictions will activate once "
-        "`model.keras` is uploaded.",
-        icon="⚠️"
+        "`model.keras` is uploaded."
     )
 
 # ── Tabs ─────────────────────────────────────────────────────────────────────
 
-tab1, tab2 = st.tabs(["📋 Select a Sample", "📁 Upload Phyphox CSV"])
+tab1, tab2 = st.tabs(["Select a Sample", "Upload Phyphox CSV"])
 
 # ── Tab 1: Sample selector ───────────────────────────────────────────────────
 
@@ -125,14 +115,11 @@ with tab1:
 
         col1, col2 = st.columns(2)
         with col1:
-            st.metric(
-                "True Activity",
-                f"{ACTIVITY_ICONS.get(true_label, '')} {true_label}"
-            )
+            st.metric("True Activity", true_label)
         with col2:
             st.metric("Feature count", len(feature_vector))
 
-        if st.button("🔍 Classify this sample", type="primary"):
+        if st.button("Classify this sample", type="primary"):
             if model_status == "no_model":
                 st.error("Model not loaded — cannot predict yet.")
             else:
@@ -148,13 +135,11 @@ with tab1:
 
                 if correct:
                     st.success(
-                        f"{ACTIVITY_ICONS.get(pred_label, '')} **{pred_label}** "
-                        f"· {confidence:.1f}% confidence · ✓ Correct"
+                        f"**{pred_label}** · {confidence:.1f}% confidence · ✓ Correct"
                     )
                 else:
                     st.error(
-                        f"{ACTIVITY_ICONS.get(pred_label, '')} **{pred_label}** "
-                        f"· {confidence:.1f}% confidence · "
+                        f"**{pred_label}** · {confidence:.1f}% confidence · "
                         f"✗ Incorrect (true: {true_label})"
                     )
 
@@ -192,10 +177,9 @@ with tab2:
 
     if uploaded_file is not None:
         st.info(
-            "📌 Phyphox pipeline coming soon. "
+            "Phyphox pipeline coming soon. "
             "Feature extraction from raw sensor readings "
-            "(filtering → jerk → FFT → 561 features) is under development.",
-            icon="🔧"
+            "(filtering → jerk → FFT → 561 features) is under development."
         )
         try:
             preview = pd.read_csv(uploaded_file)