updated files and minor adjustments to ui

app.py

@@ -550,7 +550,7 @@ if predict_clicked or "prediction" in st.session_state:
     m1, m2, m3 = st.columns(3)
     m1.metric("Grid Position",  f"P{p['grid']}")
     m2.metric("Team",           p["constructor"])
-    m3.metric("Training Data", f"{min_season} – {latest_season}")
+    m3.metric("Data Range", f"{min_season} – {latest_season}")
 
     st.markdown("<div style='margin-top:1.2rem'></div>", unsafe_allow_html=True)
     st.markdown("### Class Probabilities")
@@ -558,6 +558,18 @@ if predict_clicked or "prediction" in st.session_state:
     proba_df = pd.DataFrame({"Probability": p["proba"]}, index=class_names)
     st.bar_chart(proba_df, color="#E10600")
 
+    _label_colors = {"Gained": "#00C853", "Held": "#FFFFFF", "Lost": "#E10600"}
+    prob_cols = st.columns(len(class_names))
+    for col, name, prob in zip(prob_cols, class_names, p["proba"]):
+        color = _label_colors.get(name, "#FFFFFF")
+        col.markdown(
+            f"<div style='text-align:center;'>"
+            f"<div style='color:#555;font-size:0.68rem;letter-spacing:0.12em;text-transform:uppercase;font-weight:600;'>{name}</div>"
+            f"<div style='color:{color};font-size:1.4rem;font-weight:700;'>{prob:.0%}</div>"
+            f"</div>",
+            unsafe_allow_html=True,
+        )
+
     st.divider()
     st.markdown("### Race Analysis")
 
@@ -608,8 +620,15 @@ if predict_clicked or "prediction" in st.session_state:
 st.markdown("<div style='margin-top:2rem'></div>", unsafe_allow_html=True)
 with st.expander("Model Details"):
     st.markdown(f"**Best Model:** {metadata['best_model_name']}")
-    st.markdown("**Validation Results:**")
+    st.markdown(f"**Splits:** Train {metadata['train_seasons']} · Val {metadata['val_seasons']} · Test {metadata['test_seasons']}")
+    st.markdown("**Model Comparison:**")
     for name, r in metadata["results"].items():
-        val = r.get("val_2024", r)
-        st.markdown(f"- {name}: Accuracy={val['accuracy']:.3f}, F1={val['f1_weighted']:.3f}")
+        val  = r.get("val_2024",  {})
+        test = r.get("test_2025", {})
+        marker = " ✓" if name == metadata["best_model_name"] else ""
+        st.markdown(
+            f"- **{name}{marker}** — "
+            f"Val 2024: Acc={val.get('accuracy', 0):.3f}, F1={val.get('f1_weighted', 0):.3f} · "
+            f"Test 2025: Acc={test.get('accuracy', 0):.3f}, F1={test.get('f1_weighted', 0):.3f}"
+        )
     st.markdown(f"**Features ({len(feature_cols)}):** {', '.join(feature_cols)}")