Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,115 +4,119 @@ import numpy as np
 from sklearn.preprocessing import StandardScaler
 from sklearn.neighbors import KNeighborsRegressor
 
-# ---------------------------
-# 1. Configuration & Custom CSS (MacOS Theme)
-# ---------------------------
-st.set_page_config(
-    page_title="DynoPro for Mac",
-    page_icon="🖥️",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
+# ---------------------------------------------------------
+# 1. macOS STYLING (FIXED FOR DARK MODE ISSUES)
+# ---------------------------------------------------------
+st.set_page_config(page_title="Mac-Mod Tuner", page_icon="🖥️", layout="wide")
 
-# Custom CSS for the Apple/MacOS Aesthetic
 st.markdown("""
-    <style>
-    /* Main Background - Apple Light Gray */
+<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;
+    }
+    
+    /* Main Background - Apple Light Grey */
     .stApp {
-        background-color: #F5F5F7;
+        background-color: #f5f5f7;
         font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
     }
     
-    /* Sidebar - White with border */
+    /* Sidebar Styling */
     section[data-testid="stSidebar"] {
-        background-color: #FFFFFF;
-        border-right: 1px solid #E5E5E5;
+        background-color: #e8e8ed;
+        border-right: 1px solid #d1d1d6;
     }
     
-    /* Headings */
-    h1, h2, h3 {
-        color: #1D1D1F;
-        font-weight: 600;
+    /* Fix Sidebar Text Colors specifically */
+    section[data-testid="stSidebar"] * {
+        color: #1d1d1f !important;
     }
     
-    /* Metrics - Card Style */
-    div[data-testid="stMetric"] {
-        background-color: #FFFFFF;
-        padding: 15px;
+    /* Card/Container Styling */
+    div[data-testid="stVerticalBlock"] > div[style*="background-color"] {
+        background-color: white;
+        border-radius: 18px;
+        padding: 20px;
+        box-shadow: 0 4px 20px rgba(0,0,0,0.05);
+        border: 1px solid #e5e5ea;
+    }
+
+    /* Input Widgets - Fix text inside inputs */
+    .stSelectbox > div > div, .stNumberInput > div > div > input {
+        background-color: white !important;
+        color: #1d1d1f !important;
         border-radius: 12px;
-        box-shadow: 0 2px 8px rgba(0,0,0,0.04);
-        border: 1px solid #EAEAEA;
+        border: 1px solid #d1d1d6;
     }
     
-    /* Custom Container Card */
-    .mac-card {
-        background-color: #FFFFFF;
-        padding: 20px;
-        border-radius: 16px;
-        box-shadow: 0 4px 12px rgba(0,0,0,0.05);
-        margin-bottom: 20px;
+    /* Dropdown menu text fix */
+    ul[data-testid="stSelectboxVirtualDropdown"] li {
+        color: black !important;
+    }
+    
+    /* Metrics Styling */
+    div[data-testid="stMetricValue"] {
+        font-weight: 700 !important;
+        color: #1d1d1f !important;
+    }
+    div[data-testid="stMetricLabel"] {
+        color: #86868b !important;
     }
     
-    /* Button Styling */
-    .stButton>button {
-        border-radius: 8px;
-        background-color: #0071E3;
-        color: white;
-        border: none;
+    /* Custom "Traffic Lights" */
+    .traffic-lights {
+        display: flex;
+        gap: 8px;
+        margin-bottom: 20px;
     }
-    </style>
-    """, unsafe_allow_html=True)
+    .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)
+# ---------------------------------------------------------
 
-# ---------------------------
-# 2. Advanced Dataset Generation
-# ---------------------------
 @st.cache_resource
-def build_engine_model(n=800):
+def train_model():
     np.random.seed(42)
+    n = 1000 
     data = []
 
     for _ in range(n):
-        # Expanded Engine Options
-        cyl = np.random.choice([3, 4, 5, 6, 8, 10, 12])
+        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)) 
         
-        # Displacement logic based on cyl
-        if cyl <= 4: engine = np.random.uniform(1.0, 2.5)
-        elif cyl <= 6: engine = np.random.uniform(2.5, 4.0)
-        elif cyl <= 8: engine = np.random.uniform(4.0, 6.2)
-        else: engine = np.random.uniform(5.0, 8.4) # V10/V12
+        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])
+        fuel = np.random.choice([0, 1, 2, 3])
+        tune = np.random.choice([0, 1, 2])
         
-        # Base HP Calculation (more realistic variance)
-        specific_output = np.random.uniform(60, 110) # HP per Liter
-        base_hp = int(engine * specific_output)
-
-        # Mods (0=Stock)
-        intake = np.random.choice([0, 1, 2])       # Stock/CAI/Race
-        headers = np.random.choice([0, 1])         # Stock/Aftermarket
-        exhaust = np.random.choice([0, 1, 2])      # Stock/Sport/Straight
-        induction = np.random.choice([0, 1, 2, 3]) # None/Turbo/Twin-Turbo/Super
-        intercooler = np.random.choice([0, 1])     # Stock/Upgraded
-        fuel = np.random.choice([0, 1, 2, 3])      # 87/91/93/E85
-        tune = np.random.choice([0, 1, 2, 3])      # None/Stage1/Stage2/Custom
-        
-        # Logic: V12s gain more from exhaust/headers, Turbos gain huge from intercoolers
-        hp_gain = (
-            (intake * 4) +
-            (headers * (cyl * 1.5)) + 
-            (exhaust * 8) +
-            (induction * (base_hp * 0.30)) +  # % gain based on base HP
-            (intercooler * (15 if induction > 0 else 0)) +
-            (tune * 15) +
-            (fuel * 5) +
+        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)
         )
         
-        # Diminishing returns for N/A engines
-        if induction == 0:
-            hp_gain = hp_gain * 0.7
+        if cyl < 6 and gain > 200: 
+            gain *= 0.8
 
-        data.append([engine, cyl, base_hp, intake, headers, exhaust, induction, intercooler, fuel, tune, hp_gain])
+        data.append([engine, cyl, base_hp, intake, exhaust, induction, cams, nitrous, fuel, tune, gain])
 
-    columns = ["engine", "cyl", "base_hp", "intake", "headers", "exhaust", "induction", "intercooler", "fuel", "tune", "hp_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)
@@ -126,140 +130,121 @@ def build_engine_model(n=800):
     
     return scaler, model
 
-scaler, model = build_engine_model()
+scaler, model = train_model()
+
+# ---------------------------------------------------------
+# 3. SIDEBAR CONTROLS
+# ---------------------------------------------------------
 
-# ---------------------------
-# 3. Sidebar (The "Control Center")
-# ---------------------------
 with st.sidebar:
-    st.title("🎛️ Tuner Studio")
-    st.caption("Vehicle Configuration")
-    
-    st.subheader("Base Vehicle")
-    cyl = st.select_slider("Cylinders", options=[3, 4, 5, 6, 8, 10, 12], value=6)
-    engine = st.number_input("Displacement (L)", 1.0, 8.4, 3.0, step=0.1)
-    base_hp = st.number_input("Factory Horsepower", 80, 1200, 300, step=10)
-    
+    st.header("⚙️ Configuration")
     st.markdown("---")
-    st.subheader("Modifications")
     
-    # Air & Exhaust
-    with st.expander("💨 Air & Exhaust", expanded=True):
-        intake = st.selectbox("Intake", ["Stock", "Cold Air", "Race Intake"])
-        headers = st.toggle("Aftermarket Headers")
-        exhaust = st.select_slider("Exhaust System", options=["Stock", "Sport Cat-back", "Straight Pipe"])
-
-    # Engine & Power Adders
-    with st.expander("⚡ Induction & Tune", expanded=True):
+    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"])
-        intercooler = st.toggle("Upgraded Intercooler")
-        tune = st.select_slider("ECU Tune", options=["Stock Map", "Stage 1", "Stage 2", "Custom Dyno"])
-        fuel = st.selectbox("Fuel Type", ["87 Octane", "91 Octane", "93 Octane", "E85 / Race Gas"])
+        cams = st.selectbox("Camshafts", ["Stock", "Street Profile", "Track/Race Profile"])
+        nitrous = st.checkbox("Nitrous Oxide System (NOS)", value=False)
 
-# ---------------------------
-# 4. Processing
-# ---------------------------
-# Mappings
-map_intake = {"Stock":0, "Cold Air":1, "Race Intake":2}
-map_exhaust = {"Stock":0, "Sport Cat-back":1, "Straight Pipe":2}
-map_induct = {"Naturally Aspirated":0, "Single Turbo":1, "Twin Turbo":2, "Supercharger":3}
-map_tune = {"Stock Map":0, "Stage 1":1, "Stage 2":2, "Custom Dyno":3}
-map_fuel = {"87 Octane":0, "91 Octane":1, "93 Octane":2, "E85 / Race Gas":3}
+    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
+# ---------------------------------------------------------
 
-input_vector = np.array([[
+# 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,
-    map_intake[intake],
-    1 if headers else 0,
-    map_exhaust[exhaust],
-    map_induct[induction],
-    1 if intercooler else 0,
-    map_fuel[fuel],
-    map_tune[tune]
+    intake_map[intake], exhaust_map[exhaust], induction_map[induction],
+    cams_map[cams], nitrous_map[nitrous], fuel_map[fuel], tune_map[tune]
 ]])
 
-input_scaled = scaler.transform(input_vector)
-pred_gain = model.predict(input_scaled)[0]
-final_hp = base_hp + pred_gain
-
-# ---------------------------
-# 5. Main Dashboard (MacOS Style)
-# ---------------------------
+pred = model.predict(scaler.transform(input_data))[0]
+new_hp = base_hp + pred
+pct_gain = (pred / base_hp) * 100
 
-# Header
-st.markdown("### 🏎️ Dyno Simulation Results")
-st.markdown("This dashboard estimates power output based on component synergy.")
-st.write("") # Spacer
+# UI Header
+st.markdown("""
+    <div class="traffic-lights">
+        <div class="dot red"></div>
+        <div class="dot yellow"></div>
+        <div class="dot green"></div>
+    </div>
+""", unsafe_allow_html=True)
+
+st.title("Performance Estimator Pro")
+st.markdown(f"Analysis for **{cyl}-Cylinder {engine}L Engine**")
+st.divider()
 
-# Top Row: The "Hero" Cards
-col1, col2 = st.columns([1, 2])
+# UI Body
+col1, col2 = st.columns([1.5, 1])
 
 with col1:
-    # Summary Card
-    with st.container(border=True):
-        st.markdown("**Vehicle Profile**")
-        st.markdown(f"<h1 style='margin:0; font-size: 40px;'>{str(cyl)}<span style='font-size:20px; color:gray'>cyl</span></h1>", unsafe_allow_html=True)
-        st.caption(f"{engine}L Displacement")
-        st.divider()
-        st.write(f"**Induction:** {induction}")
-        st.write(f"**Tune:** {tune}")
+    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:
-    # Results Card
-    with st.container(border=True):
-        st.markdown("**Projected Output**")
+    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>
+        </div>
+        """, unsafe_allow_html=True)
         
-        m1, m2, m3 = st.columns(3)
-        with m1:
-            st.metric("Base Power", f"{base_hp} HP")
-        with m2:
-            st.metric("Gain", f"+{pred_gain:.0f} HP", delta=f"{((pred_gain/base_hp)*100):.1f}%")
-        with m3:
-            st.metric("Total Power", f"{final_hp:.0f} HP", delta="Peak Output")
+        st.markdown("<br>", unsafe_allow_html=True)
         
-        # Progress Bar visual
-        st.write("")
-        st.write("Power Utilization")
-        pct_gain = min((final_hp / (base_hp * 2)), 1.0) # Cap bar at 200% base
-        st.progress(pct_gain)
-
-# Bottom Row: Visualization
-st.write("")
-st.markdown("### 📈 Power Curve Analysis")
-
-with st.container(border=True):
-    # Creating a synthetic RPM curve for visualization
-    rpms = np.linspace(2000, 8000, 50)
-    
-    # Physics approximation for torque curve shape
-    def torque_curve(rpm, peak_hp):
-        peak_rpm = 6500
-        # simplified curve logic
-        return -((rpm - peak_rpm)**2) + (peak_hp * 5000) 
-    
-    # Normalized curves scaled to HP
-    base_curve = [base_hp * (1 - ((x - 6500)/5000)**2) * (x/8000) for x in rpms]
-    mod_curve = [final_hp * (1 - ((x - 6500)/5000)**2) * (x/8000) for x in rpms]
-    
-    chart_data = pd.DataFrame({
-        "RPM": np.tile(rpms, 2),
-        "Horsepower": np.concatenate([base_curve, mod_curve]),
-        "Setup": ["Factory Stock"] * 50 + ["Modified"] * 50
-    })
-
-    # Use Streamlit's native area chart but configure it to look clean
-    st.area_chart(
-        chart_data, 
-        x="RPM", 
-        y="Horsepower", 
-        color="Setup",
-        stack=False # Overlay them
-    )
-
-# Footer / "Dock" feel
-st.divider()
-cols = st.columns(4)
-cols[0].info(f"Fuel: {fuel}")
-cols[1].info(f"Exhaust: {exhaust}")
-cols[2].info(f"Headers: {'Yes' if headers else 'No'}")
-cols[3].info(f"Intercooler: {'Yes' if intercooler else 'No'}")
-
+        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="⚠️")