Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,247 +4,469 @@ import numpy as np
 from sklearn.preprocessing import StandardScaler
 from sklearn.neighbors import KNeighborsRegressor
 
-# ---------------------------------------------------------
-# 1. macOS STYLING (FIXED FOR DARK MODE ISSUES)
-# ---------------------------------------------------------
-st.set_page_config(page_title="Mac-Mod Tuner", page_icon="🖥️", layout="wide")
-
-st.markdown("""
-<style>
-    /* FORCE TEXT COLOR TO BLACK (Overrides System Dark Mode) */
-    .stApp, .stMarkdown, p, h1, h2, h3, h4, h5, h6, span, div, label {
-        color: #1d1d1f !important;
+# ---------------------------
+# Page config
+# ---------------------------
+st.set_page_config(
+    page_title="Neural Tuner – Car Mod Performance Estimator",
+    page_icon="🔥",
+    layout="wide"
+)
+
+# ---------------------------
+# Global CSS – crazy but clean
+# ---------------------------
+st.markdown(
+    """
+    <style>
+    html, body, [data-testid="stAppViewContainer"] {
+        background: radial-gradient(circle at top, #020617 0, #020617 35%, #020617 40%, #000000 100%) !important;
+        color: #e5e7eb;
+        font-family: system-ui, -apple-system, BlinkMacSystemFont, "SF Pro Text",
+                     "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
     }
-    
-    /* Main Background - Apple Light Grey */
-    .stApp {
-        background-color: #f5f5f7;
-        font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+
+    /* Hide default Streamlit header/menu */
+    [data-testid="stHeader"] { background: transparent; }
+    [data-testid="stToolbar"] { display: none; }
+
+    .hero {
+        border-radius: 24px;
+        padding: 18px 22px;
+        background: radial-gradient(circle at top left, rgba(96,165,250,0.35), transparent 55%),
+                    radial-gradient(circle at bottom right, rgba(236,72,153,0.35), transparent 55%),
+                    rgba(15,23,42,0.94);
+        box-shadow: 0 25px 60px rgba(15,23,42,0.9);
+        border: 1px solid rgba(148,163,184,0.35);
+        position: relative;
+        overflow: hidden;
     }
-    
-    /* Sidebar Styling */
-    section[data-testid="stSidebar"] {
-        background-color: #e8e8ed;
-        border-right: 1px solid #d1d1d6;
+
+    .hero-title {
+        font-size: 1.9rem;
+        font-weight: 700;
+        background: linear-gradient(90deg, #f97316, #facc15, #22c55e, #38bdf8, #a855f7, #f97316);
+        background-size: 400% 100%;
+        -webkit-background-clip: text;
+        color: transparent;
+        animation: moveGradient 9s ease infinite;
     }
-    
-    /* Fix Sidebar Text Colors specifically */
-    section[data-testid="stSidebar"] * {
-        color: #1d1d1f !important;
+
+    .hero-sub {
+        color: #9ca3af;
+        font-size: 0.95rem;
+    }
+
+    .hero-pill {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 3px 11px;
+        border-radius: 999px;
+        background: rgba(15,118,110,0.2);
+        border: 1px solid rgba(34,197,94,0.6);
+        font-size: 0.75rem;
+        color: #bbf7d0;
+        margin-right: 8px;
+    }
+
+    @keyframes moveGradient {
+        0% { background-position: 0% 50%; }
+        50% { background-position: 100% 50%; }
+        100% { background-position: 0% 50%; }
     }
-    
-    /* Card/Container Styling */
-    div[data-testid="stVerticalBlock"] > div[style*="background-color"] {
-        background-color: white;
+
+    .glass {
+        background: radial-gradient(circle at top left, rgba(148,163,184,0.24), transparent 55%),
+                    rgba(15,23,42,0.96);
         border-radius: 18px;
-        padding: 20px;
-        box-shadow: 0 4px 20px rgba(0,0,0,0.05);
-        border: 1px solid #e5e5ea;
+        padding: 18px 18px 14px 18px;
+        border: 1px solid rgba(148,163,184,0.4);
+        box-shadow: 0 20px 40px rgba(15,23,42,0.6);
     }
 
-    /* Input Widgets - Fix text inside inputs */
-    .stSelectbox > div > div, .stNumberInput > div > div > input {
-        background-color: white !important;
-        color: #1d1d1f !important;
-        border-radius: 12px;
-        border: 1px solid #d1d1d6;
+    .chip {
+        display:inline-block;
+        padding:4px 10px;
+        margin:3px 4px 3px 0;
+        border-radius:999px;
+        font-size:0.78rem;
+        background:rgba(59,130,246,0.14);
+        border:1px solid rgba(59,130,246,0.45);
+        color:#bfdbfe;
     }
-    
-    /* Dropdown menu text fix */
-    ul[data-testid="stSelectboxVirtualDropdown"] li {
-        color: black !important;
+
+    .meter-label {
+        font-size: 0.85rem;
+        color: #9ca3af;
+        margin-bottom: 3px;
     }
-    
-    /* Metrics Styling */
-    div[data-testid="stMetricValue"] {
-        font-weight: 700 !important;
-        color: #1d1d1f !important;
+
+    .muted {
+        font-size: 0.8rem;
+        color: #6b7280;
     }
-    div[data-testid="stMetricLabel"] {
-        color: #86868b !important;
+
+    /* Tabs */
+    button[data-baseweb="tab"] {
+        background: transparent !important;
+        border-radius: 999px !important;
+        padding: 0.5rem 1rem !important;
+        margin-right: 0.4rem;
+        color: #9ca3af !important;
     }
-    
-    /* Custom "Traffic Lights" */
-    .traffic-lights {
-        display: flex;
-        gap: 8px;
-        margin-bottom: 20px;
+    button[data-baseweb="tab"][aria-selected="true"] {
+        background: rgba(59,130,246,0.2) !important;
+        color: #e5e7eb !important;
+        box-shadow: 0 0 0 1px rgba(59,130,246,0.7);
     }
-    .dot { width: 12px; height: 12px; border-radius: 50%; }
-    .red { background-color: #ff5f57; border: 1px solid #e0443e; }
-    .yellow { background-color: #febc2e; border: 1px solid #d89e24; }
-    .green { background-color: #28c840; border: 1px solid #1aab29; }
-</style>
-""", unsafe_allow_html=True)
-
-# ---------------------------------------------------------
-# 2. DATA & MODEL (V12 & NITROUS)
-# ---------------------------------------------------------
-
-@st.cache_resource
-def train_model():
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# ---------------------------
+# Data & model (same logic)
+# ---------------------------
+def generate_dataset(n=400):
     np.random.seed(42)
-    n = 1000 
     data = []
-
     for _ in range(n):
-        engine = np.random.choice([1.6, 2.0, 2.4, 3.0, 3.8, 4.0, 5.0, 5.2, 6.0, 6.5])
-        cyl = np.random.choice([4, 6, 8, 10, 12])
-        base_hp = int(engine * np.random.uniform(60, 110)) 
-        
+        engine = np.random.choice([1.6, 2.0, 2.5, 3.0, 3.5, 5.0])
+        cyl = np.random.choice([4, 6, 8])
+        base_hp = int(engine * cyl * np.random.uniform(18, 22))
+
         intake = np.random.choice([0, 1, 2])
-        exhaust = np.random.choice([0, 1, 2, 3])
-        induction = np.random.choice([0, 1, 2, 3])
-        cams = np.random.choice([0, 1, 2])
-        nitrous = np.random.choice([0, 1])
+        exhaust = np.random.choice([0, 1, 2])
+        induction = np.random.choice([0, 1, 2])
         fuel = np.random.choice([0, 1, 2, 3])
         tune = np.random.choice([0, 1, 2])
-        
-        gain = (
-            (intake * 3) + 
-            (exhaust * 5) + 
-            (induction * (base_hp * 0.35)) + 
-            (cams * (base_hp * 0.10)) + 
-            (nitrous * 50) + 
-            (tune * (base_hp * 0.08)) + 
-            (fuel * 4) +
-            np.random.uniform(-5, 5)
+        altitude = np.random.uniform(0, 2000)
+
+        hp_gain = (
+            intake * np.random.uniform(3, 10) +
+            exhaust * np.random.uniform(5, 20) +
+            induction * np.random.uniform(25, 100) +
+            tune * np.random.uniform(10, 35) +
+            fuel * np.random.uniform(2, 8) -
+            altitude * 0.01 +
+            np.random.uniform(-3, 3)
         )
-        
-        if cyl < 6 and gain > 200: 
-            gain *= 0.8
-
-        data.append([engine, cyl, base_hp, intake, exhaust, induction, cams, nitrous, fuel, tune, gain])
-
-    columns = ["engine", "cyl", "base_hp", "intake", "exhaust", "induction", "cams", "nitrous", "fuel", "tune", "hp_gain"]
-    df = pd.DataFrame(data, columns=columns)
-
-    X = df.drop("hp_gain", axis=1)
-    y = df["hp_gain"]
-    
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(X)
-    
-    model = KNeighborsRegressor(n_neighbors=7, weights='distance')
-    model.fit(X_scaled, y)
-    
-    return scaler, model
-
-scaler, model = train_model()
-
-# ---------------------------------------------------------
-# 3. SIDEBAR CONTROLS
-# ---------------------------------------------------------
-
-with st.sidebar:
-    st.header("⚙️ Configuration")
-    st.markdown("---")
-    
-    with st.expander("🚙 Base Vehicle Stats", expanded=True):
-        col_eng_1, col_eng_2 = st.columns(2)
-        with col_eng_1:
-            cyl = st.selectbox("Cylinders", [4, 5, 6, 8, 10, 12])
-        with col_eng_2:
-            engine = st.selectbox("Size (L)", [1.6, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 5.2, 6.0, 6.5, 8.4])
-            
-        base_hp = st.number_input("Factory HP", 100, 1000, 300)
-
-    with st.expander("🔧 Bolt-on Modifications", expanded=True):
-        intake = st.selectbox("Intake", ["Stock", "High Flow Filter", "Cold Air Intake"])
-        exhaust = st.selectbox("Exhaust", ["Stock", "Cat-back", "Long Tube Headers", "Full Straight Pipe"])
-
-    with st.expander("🔥 Internals & Boost", expanded=True):
-        induction = st.selectbox("Forced Induction", ["Naturally Aspirated", "Single Turbo", "Twin Turbo", "Supercharger"])
-        cams = st.selectbox("Camshafts", ["Stock", "Street Profile", "Track/Race Profile"])
-        nitrous = st.checkbox("Nitrous Oxide System (NOS)", value=False)
-
-    with st.expander("💻 Tuning & Fuel", expanded=True):
-        fuel = st.selectbox("Fuel Type", ["87 Octane", "91 Octane", "93 Octane", "E85 (Ethanol)"])
-        tune = st.selectbox("ECU Map", ["Stock Map", "Stage 1", "Stage 2"])
-
-# ---------------------------------------------------------
-# 4. MAIN DASHBOARD
-# ---------------------------------------------------------
-
-# Mappings
-intake_map = {"Stock":0, "High Flow Filter":1, "Cold Air Intake":2}
-exhaust_map = {"Stock":0, "Cat-back":1, "Long Tube Headers":2, "Full Straight Pipe":3}
-induction_map = {"Naturally Aspirated":0, "Single Turbo":1, "Twin Turbo":2, "Supercharger":3}
-cams_map = {"Stock":0, "Street Profile":1, "Track/Race Profile":2}
-nitrous_map = {False:0, True:1}
-fuel_map = {"87 Octane":0, "91 Octane":1, "93 Octane":2, "E85 (Ethanol)":3}
-tune_map = {"Stock Map":0, "Stage 1":1, "Stage 2":2}
-
-# Logic
-input_data = np.array([[
-    engine, cyl, base_hp,
-    intake_map[intake], exhaust_map[exhaust], induction_map[induction],
-    cams_map[cams], nitrous_map[nitrous], fuel_map[fuel], tune_map[tune]
-]])
 
-pred = model.predict(scaler.transform(input_data))[0]
-new_hp = base_hp + pred
-pct_gain = (pred / base_hp) * 100
+        data.append([engine, cyl, base_hp, intake, exhaust,
+                     induction, fuel, tune, altitude, hp_gain])
+
+    columns = [
+        "engine", "cyl", "base_hp", "intake", "exhaust",
+        "induction", "fuel", "tune", "altitude", "hp_gain"
+    ]
+    return pd.DataFrame(data, columns=columns)
+
+df = generate_dataset()
+X = df.drop("hp_gain", axis=1)
+y = df["hp_gain"]
 
-# UI Header
-st.markdown("""
-    <div class="traffic-lights">
-        <div class="dot red"></div>
-        <div class="dot yellow"></div>
-        <div class="dot green"></div>
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+model = KNeighborsRegressor(n_neighbors=5, weights="distance")
+model.fit(X_scaled, y)
+
+# ---------------------------
+# HERO SECTION
+# ---------------------------
+st.markdown(
+    """
+    <div class="hero">
+        <div style="display:flex;justify-content:space-between;align-items:center;gap:14px;">
+            <div>
+                <div class="hero-pill">⚙️ Powered by KNN + synthetic dyno data</div>
+                <div class="hero-title">Neural Tuner – Live Car Mod Performance Lab</div>
+                <p class="hero-sub">
+                    Mix intakes, turbos, tunes & fuel in real time. Watch the HP jump, 
+                    the power-to-weight spike and your virtual build go absolutely feral. 🐺
+                </p>
+            </div>
+            <div style="
+                width:170px;height:110px;
+                border-radius:22px;
+                background:conic-gradient(from 220deg,
+                    #22c55e, #38bdf8, #a855f7, #f97316, #facc15, #22c55e);
+                padding:2px;
+            ">
+                <div style="
+                    width:100%;height:100%;
+                    border-radius:19px;
+                    background:radial-gradient(circle at 30% 0%, rgba(248,250,252,0.2), transparent 55%),
+                               #020617;">
+                    <div style="display:flex;flex-direction:column;justify-content:center;align-items:center;height:100%;">
+                        <div style="font-size:0.78rem;color:#9ca3af;">Live Build</div>
+                        <div style="font-size:1.6rem;font-weight:700;color:#e5e7eb;">HP Lab</div>
+                        <div style="font-size:0.75rem;color:#22c55e;">Realtime estimator</div>
+                    </div>
+                </div>
+            </div>
+        </div>
     </div>
-""", unsafe_allow_html=True)
+    """,
+    unsafe_allow_html=True
+)
 
-st.title("Performance Estimator Pro")
-st.markdown(f"Analysis for **{cyl}-Cylinder {engine}L Engine**")
-st.divider()
+st.markdown("")
 
-# UI Body
-col1, col2 = st.columns([1.5, 1])
+# ---------------------------
+# Layout: left (controls) / right (dashboard)
+# ---------------------------
+left, right = st.columns([1.15, 1])
 
-with col1:
-    st.markdown("### 📈 Dyno Projection")
-    chart_data = pd.DataFrame({
-        "Horsepower": [base_hp, new_hp],
-        "Stage": ["Factory", "Modified"]
-    })
-    
-    st.vega_lite_chart(chart_data, {
-        "mark": {"type": "bar", "cornerRadiusEnd": 6, "color": "#007AFF"},
-        "encoding": {
-            "x": {"field": "Stage", "type": "nominal", "axis": {"labelAngle": 0, "labelColor": "#1d1d1f"}},
-            "y": {"field": "Horsepower", "type": "quantitative", "axis": {"labelColor": "#1d1d1f"}},
-            "tooltip": ["Stage", "Horsepower"]
-        }
-    }, use_container_width=True)
-
-with col2:
-    st.markdown("### ⚡ Results")
-    
-    with st.container():
-        # Custom HTML Metric for better styling
-        st.markdown(f"""
-        <div style="background-color: white; border-radius: 12px; padding: 15px; border: 1px solid #e5e5ea;">
-            <div style="font-size: 14px; color: #86868b; text-transform: uppercase; font-weight: 600;">Total Power Output</div>
-            <div style="font-size: 42px; font-weight: 800; color: #1d1d1f; letter-spacing: -1px;">{int(new_hp)} HP</div>
+# ===== LEFT: TUNING CONTROLS =====
+with left:
+    st.markdown('<div class="glass">', unsafe_allow_html=True)
+    st.subheader("🎛️ Tune Your Build")
+
+    tabs = st.tabs(["Core Specs", "Bolt-Ons", "Boost & Cooling", "ECU & Fuel"])
+
+    # --- Core specs tab ---
+    with tabs[0]:
+        col1, col2 = st.columns(2)
+        engine_disp_options = [0.8,1.0,1.2,1.4,1.6,1.8,2.0,2.2,2.4,2.5,
+                               2.8,3.0,3.2,3.5,4.0,4.4,5.0,6.0,7.0,8.0]
+        engine_disp = col1.selectbox("Engine Displacement (L)", engine_disp_options, index=engine_disp_options.index(2.0))
+        engine_layout = col2.selectbox("Engine Layout", ["I3","I4","I6","V6","V8","V10","V12"], index=2)
+
+        layout_to_cyl = {"I3":3,"I4":4,"I6":6,"V6":6,"V8":8,"V10":10,"V12":12}
+        cyl = layout_to_cyl[engine_layout]
+
+        base_hp = st.number_input(
+            "Base Horsepower (stock dyno)",
+            min_value=60, max_value=1400,
+            value=int(max(90, round(engine_disp * cyl * 20)))
+        )
+
+        col3, col4 = st.columns(2)
+        weight_kg = col3.number_input("Vehicle Weight (kg)", min_value=700, max_value=4000, value=1500)
+        weight_reduction = col4.slider("Weight Reduction (%)", 0, 40, 0)
+
+    # --- Bolt-ons tab ---
+    with tabs[1]:
+        col1, col2, col3 = st.columns(3)
+        intake = col1.selectbox("Intake", ["Stock", "Cold Air", "Performance"])
+        headers = col2.selectbox("Headers", ["Stock", "Shorty", "Long Tube"])
+        exhaust = col3.selectbox("Exhaust", ["Stock", "Cat-back", "Straight Pipe"])
+        exhaust_dia = st.slider("Exhaust Diameter (mm)", 40, 120, 60)
+
+        cam = st.selectbox("Cam Profile", ["Stock", "Stage 1 – Road", "Stage 2 – Aggressive", "Stage 3 – Race"])
+        intake_manifold = st.selectbox("Intake Manifold", ["Stock", "High-flow", "Individual throttle bodies"])
+
+    # --- Boost & cooling tab ---
+    with tabs[2]:
+        induction = st.selectbox("Forced Induction Setup", ["None", "Turbo", "Twin-Turbo", "Supercharger", "Twincharged"])
+        boost_psi = st.slider("Target Boost (psi)", 0, 40, 10)
+        turbo_size = st.selectbox("Turbo Size", ["N/A", "Small 45-55mm", "Medium 56-65mm", "Big 66-75mm", "XL 76mm+"])
+        intercooler = st.selectbox("Intercooler Type", ["None", "Air-to-Air", "Air-to-Water", "Front-mount High-Flow"])
+        meth = st.checkbox("Methanol Injection Kit", value=False)
+
+    # --- ECU & fuel tab ---
+    with tabs[3]:
+        tune = st.selectbox("ECU Tune Level", ["None", "Mild Street", "Stage 1", "Stage 2", "Kill Mode"])
+        fuel = st.selectbox("Fuel Type", ["87", "91", "93", "E85 / Race Blend"])
+        altitude = st.slider("Altitude (meters)", 0, 3500, 200)
+        traction_mode = st.radio("Traction Mode Vibe", ["Daily", "Spirited", "Track / Drag"], horizontal=True)
+
+    st.markdown(
+        '<p class="muted" style="margin-top:8px;">Numbers are synthetic; this is a dyno-inspired playground, not a tuning bible. 🧪</p>',
+        unsafe_allow_html=True
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# ===== MODEL INPUT & CALC =====
+
+# maps for model
+intake_map = {"Stock":0, "Cold Air":1, "Performance":2}
+exhaust_map = {"Stock":0, "Cat-back":1, "Straight Pipe":2}
+induction_model_map = {"None":0, "Turbo":1, "Twin-Turbo":1, "Supercharger":2, "Twincharged":2}
+fuel_map = {"87":0, "91":1, "93":2, "E85 / Race Blend":3}
+tune_base_map = {"None":0, "Mild Street":1, "Stage 1":1, "Stage 2":2, "Kill Mode":2}
+
+input_for_model = np.array([[
+    engine_disp,
+    cyl,
+    base_hp,
+    intake_map.get(intake,0),
+    exhaust_map.get(exhaust,0),
+    induction_model_map.get(induction,0),
+    fuel_map.get(fuel,0),
+    tune_base_map.get(tune,0),
+    altitude
+]])
+
+input_scaled = scaler.transform(input_for_model)
+pred_base = float(model.predict(input_scaled)[0])
+
+# --- heuristic extras ---
+cam_gain_map = {
+    "Stock":0.0,
+    "Stage 1 – Road":6.0,
+    "Stage 2 – Aggressive":12.0,
+    "Stage 3 – Race":20.0
+}
+headers_gain_map = {"Stock":0.0, "Shorty":5.0, "Long Tube":9.0}
+intake_man_gain_map = {
+    "Stock":0.0,
+    "High-flow":5.0,
+    "Individual throttle bodies":10.0
+}
+intercooler_gain_map = {
+    "None":0.0,
+    "Air-to-Air":4.0,
+    "Air-to-Water":6.5,
+    "Front-mount High-Flow":9.0
+}
+turbo_size_map = {
+    "N/A":0.0,
+    "Small 45-55mm":5.0,
+    "Medium 56-65mm":12.0,
+    "Big 66-75mm":20.0,
+    "XL 76mm+":28.0
+}
+
+cam_gain = cam_gain_map.get(cam, 0.0)
+headers_gain = headers_gain_map.get(headers, 0.0)
+intake_man_gain = intake_man_gain_map.get(intake_manifold, 0.0)
+intercooler_gain = intercooler_gain_map.get(intercooler, 0.0)
+turbo_size_gain = turbo_size_map.get(turbo_size, 0.0)
+
+if induction in ["Turbo", "Twin-Turbo"]:
+    boost_gain = boost_psi * 1.8
+elif induction in ["Supercharger", "Twincharged"]:
+    boost_gain = boost_psi * 1.4
+else:
+    boost_gain = 0.0
+
+meth_gain = 12.0 if meth else 0.0
+exhaust_dia_gain = max(0.0, (exhaust_dia - 55) * 0.1)
+
+extra_gain = (
+    cam_gain +
+    headers_gain +
+    intake_man_gain +
+    intercooler_gain +
+    turbo_size_gain +
+    boost_gain * 0.9 +
+    meth_gain +
+    exhaust_dia_gain
+)
+
+# traction / vibe adjusts "usable feel"
+traction_multiplier = {"Daily":0.9, "Spirited":1.0, "Track / Drag":1.05}[traction_mode]
+
+pred_total_gain = (pred_base + extra_gain) * traction_multiplier
+pred_total_gain = max(pred_total_gain, -5.0)  # clamp
+new_hp = max(base_hp + pred_total_gain, 40.0)
+
+effective_weight = weight_kg * (1 - weight_reduction / 100.0)
+hp_per_ton = new_hp / (effective_weight / 1000.0)
+
+# normalized hype level 0–100
+hype_raw = np.clip(pred_total_gain / 120 * 100, 0, 100)
+hype_level = int(hype_raw)
+
+# verdict text
+if hype_level < 20:
+    verdict = "Sleeper grocery getter 🚙"
+elif hype_level < 40:
+    verdict = "Respectable street build 🌃"
+elif hype_level < 70:
+    verdict = "Serious weekend weapon ⚔️"
+else:
+    verdict = "Full send, tyres cry for mercy 🏁"
+
+# ===== RIGHT: DASHBOARD =====
+with right:
+    st.markdown('<div class="glass">', unsafe_allow_html=True)
+    st.subheader("💥 Build Outcome")
+
+    m1, m2, m3 = st.columns(3)
+    m1.metric("Estimated HP Gain", f"{pred_total_gain:.1f} HP")
+    m2.metric("New Output", f"{new_hp:.1f} HP")
+    m3.metric("HP per Ton", f"{hp_per_ton:.1f}")
+
+    st.markdown("")
+    st.markdown('<div class="meter-label">Hype Meter (relative craziness of this build)</div>', unsafe_allow_html=True)
+    st.progress(hype_level)
+
+    st.markdown(f"**Verdict:** {verdict}")
+
+    # small bar chart: stock vs tuned
+    st.markdown("")
+    st.bar_chart(
+        pd.DataFrame(
+            {"Horsepower": [base_hp, new_hp]},
+            index=["Stock", "Tuned"]
+        )
+    )
+
+    # chips summary
+    st.markdown(
+        f"""
+        <div style="margin-top:6px;">
+            <span class="chip">{engine_disp}L {engine_layout}</span>
+            <span class="chip">Weight: {weight_kg} kg ▸ −{weight_reduction}%</span>
+            <span class="chip">Intake: {intake}</span>
+            <span class="chip">Headers: {headers}</span>
+            <span class="chip">Exhaust: {exhaust} ({exhaust_dia}mm)</span><br/>
+            <span class="chip">Induction: {induction} @ {boost_psi} psi</span>
+            <span class="chip">IC: {intercooler}</span>
+            <span class="chip">Tune: {tune}</span>
+            <span class="chip">Fuel: {fuel}</span>
         </div>
-        """, unsafe_allow_html=True)
-        
-        st.markdown("<br>", unsafe_allow_html=True)
-        
-        c1, c2 = st.columns(2)
-        with c1:
-            st.metric("Gain", f"+{int(pred)} HP")
-        with c2:
-            st.metric("Improvement", f"{pct_gain:.1f}%")
-
-st.markdown("### 📋 Build Summary")
-with st.container():
-    c1, c2, c3, c4 = st.columns(4)
-    c1.info(f"**Induction:** {induction}")
-    c2.info(f"**Fuel:** {fuel}")
-    c3.info(f"**Camshafts:** {cams}")
-    c4.info(f"**Nitrous:** {'Enabled' if nitrous else 'Disabled'}")
-
-if pct_gain > 50:
-    st.toast("🚀 Massive gains! Ensure engine internals are forged.", icon="⚠️")
+        """,
+        unsafe_allow_html=True
+    )
+
+    st.markdown("---")
+
+    # Contribution breakdown (no Styler.hide_index)
+    breakdown = pd.DataFrame({
+        "Component": [
+            "Model base (from dataset)",
+            "Cam profile",
+            "Headers",
+            "Intake manifold",
+            "Intercooler",
+            "Turbo size",
+            "Boost (net)",
+            "Meth kit",
+            "Exhaust diameter tweak"
+        ],
+        "Approx HP": [
+            pred_base,
+            cam_gain,
+            headers_gain,
+            intake_man_gain,
+            intercooler_gain,
+            turbo_size_gain,
+            boost_gain * 0.9,
+            meth_gain,
+            exhaust_dia_gain
+        ]
+    })
+
+    st.caption("🔬 Where is that extra power coming from?")
+    st.dataframe(breakdown, use_container_width=True, height=260)
+
+    st.markdown(
+        '<p class="muted" style="margin-top:8px;">Model: distance-weighted KNN on synthetic dyno-style data + extra heuristic math for advanced mods.</p>',
+        unsafe_allow_html=True
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# ===== FOOTER =====
+st.markdown(
+    """
+    <div style="text-align:center;margin-top:10px;" class="muted">
+        Built for fun, learning & ridiculous builds – drop this in a Space and watch car nerds lose it. 🔧🔥
+    </div>
+    """,
+    unsafe_allow_html=True
+)