Update app.py

app.py

@@ -13,6 +13,7 @@ st.set_page_config(
 # --- Custom CSS ---
 st.markdown("""
 <style>
+    /* Main Dashboard Styling */
     .metric-card {
         background-color: #f0f2f6;
         padding: 20px;
@@ -28,12 +29,26 @@ st.markdown("""
         color: #ff4b4b;
     }
     .sub-text {
-        font-size: 1.6em;
+        font-size: 1.2em;
         color: #555;
     }
-    .stPlotlyChart {
-        display: flex;
-        justify-content: center;
+    
+    /* Sidebar Styling Override */
+    [data-testid="stSidebar"] {
+        font-size: 1.4rem;
+    }
+    [data-testid="stSidebar"] label {
+        font-size: 1.1rem !important;
+        font-weight: 500 !important;
+    }
+    .sidebar-question {
+        font-size: 1.15rem;
+        font-weight: bold;
+        margin-bottom: 5px;
+        color: #31333F;
+    }
+    .spacer {
+        margin-bottom: 2rem;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -48,41 +63,71 @@ st.divider()
 
 # --- Sidebar Inputs ---
 st.sidebar.header("⚙️ Scenario Settings")
+st.sidebar.markdown("---")
 
 # 1. AI Power Demand
+st.sidebar.markdown('<p class="sidebar-question">1. How much power will AI require in 2030?</p>', unsafe_allow_html=True)
 ai_demand_gw = st.sidebar.number_input(
-    "AI Power Demand in 2030 (GW)", 
+    "Demand (GW)", 
     value=100, 
     step=10,
-    help="Projected total power load required by AI data centers in 2030."
+    label_visibility="collapsed" # Hides the default label since we used a custom header
 )
+with st.sidebar.expander("ℹ️ More on AI Demand Forecasts"):
+    st.write("According to [EpochAI](https://epoch.ai/gradient-updates/is-almost-everyone-wrong-about-americas-ai-power-problem#:~:text=Our%20best%20projections%20suggest%20that%20the%20US%20will%20need%20around%20100%20GW%20of%20power%20by%202030.) the best projections suggest that the US will need ~100 GW of power by 2030.")
+
+st.sidebar.markdown('<div class="spacer"></div>', unsafe_allow_html=True)
 
 # 2. Gas Share
+st.sidebar.markdown('<p class="sidebar-question">2. What proportion of power will be supplied by natural gas?</p>', unsafe_allow_html=True)
 gas_share = st.sidebar.slider(
-    "Natural Gas Proportion (%)", 
+    "Gas Share", 
     min_value=0, max_value=100, value=90, step=5,
-    help="The percentage of AI power demand that will be met specifically by Natural Gas generation (vs. Renewables/Nuclear)."
+    label_visibility="collapsed"
 )
-
-# 3. Turbine Efficiency (The new logic)
-# Range 35% (Aeroderivative) to 60% (H-Class CCGT)
+st.sidebar.caption(f"Selected: **{gas_share}%**")
+
+with st.sidebar.expander("ℹ️ More on Energy Mix"):
+    st.markdown("""
+    **Why Gas?**
+    The electric grid in major hubs like Texas is effectively "sold out," with wait times for connection approaching 5 years. To bypass this, AI labs are adopting "Bring Your Own Generation" (BYOG) strategies, primarily using natural gas which can be deployed in months rather than years.
+    
+    **What about Solar?**
+    While solar prices have dropped ~88% since 2009, it faces physical limits:
+    * **Land Use:** 2 GW of solar requires a land area roughly the size of Manhattan.
+    * **Uptime:** Solar requires battery backup for 24/7 reliability, adding complexity for off-grid "island" data centers.
+    [Source](https://open.substack.com/pub/semianalysis/p/how-ai-labs-are-solving-the-power)
+    """)
+
+st.sidebar.markdown('<div class="spacer"></div>', unsafe_allow_html=True)
+
+# 3. Turbine Efficiency
+st.sidebar.markdown('<p class="sidebar-question">3. What will the efficiency of the gas turbines be?</p>', unsafe_allow_html=True)
 turbine_eff_percent = st.sidebar.slider(
-    "Gas Turbine Average Efficiency (%)", 
+    "Efficiency", 
     min_value=35, max_value=60, value=45, step=1,
-    help="Thermal efficiency of the gas fleet. 35-40% = Peakers/Aeroderivative. 50-60% = Modern Combined Cycle (CCGT)."
+    label_visibility="collapsed"
 )
+st.sidebar.caption(f"Selected: **{turbine_eff_percent}%**")
+
+with st.sidebar.expander("ℹ️ More on Turbine Tech"):
+    st.markdown("""
+    **Efficiency varies by speed and scale:**
+    
+    * **Aeroderivative (35-40%):** Modified jet engines (e.g., GE LM2500). They are less efficient but fast to deploy. Companies like xAI use them to bypass grid delays.
+    * **Reciprocating Engines (40-50%):** Modular internal combustion engines (e.g., Wärtsilä). They offer higher efficiency than aeroderivatives and handle partial loads well.
+    * **Combined Cycle (50-60%):** The gold standard for efficiency, using waste heat to drive a steam turbine. However, they take 36-60 months to build, making them too slow for the current AI race.
+        [Source](https://open.substack.com/pub/semianalysis/p/how-ai-labs-are-solving-the-power)
+    """)
 
 # Constants
-CAPACITY_FACTOR = 0.90  # Fixed as requested
-US_BASELINE_MMT = 6343  # EPA Gross GHG Inventory Proxy
-# Base reference: 486 gCO2e/kWh at 60% efficiency
-# Constant for Carbon Content of Fuel derived from this: 486 * 0.60 = 291.6
+CAPACITY_FACTOR = 0.90
+US_BASELINE_MMT = 6343
 FUEL_CARBON_CONSTANT = 486 * 0.60 
 
 # --- Calculations ---
 
-# 1. Calculate Emissions Factor based on selected efficiency
-# Formula: EF_output = Constant_Fuel_Carbon / Efficiency
+# 1. Calculate Emissions Factor
 if turbine_eff_percent > 0:
     calculated_ef = FUEL_CARBON_CONSTANT / (turbine_eff_percent / 100)
 else:
@@ -93,42 +138,35 @@ gas_gw_capacity = ai_demand_gw * (gas_share/100)
 total_gwh = gas_gw_capacity * CAPACITY_FACTOR * 8760
 
 # 3. Calculate Emissions (MMT CO2e)
-# (GWh * 1,000,000 -> kWh) * (gCO2 / 1,000,000 -> Tonnes) / 1,000,000 -> Million Tonnes
-# Simplifies to (GWh * EF) / 1,000,000
 ai_emissions_mmt = (total_gwh * calculated_ef) / 1_000_000 
-
 percent_increase = (ai_emissions_mmt / US_BASELINE_MMT) * 100
 
 # --- Dashboard Layout ---
 
-# 1. The Big Number (Centered)
+# 1. The Big Number
 c1, c2, c3 = st.columns([1, 2, 1])
 with c2:
     st.markdown(f"""
     <div class="metric-card">
-        <div class="sub-text">Projected 2030 US Emissions Increase</div>
+        <div class="sub-text">Projected US Emissions Increase</div>
         <div class="big-number">+{percent_increase:.2f}%</div>
         <div class="sub-text">({ai_emissions_mmt:.1f} Million tCO2e)</div>
-        <div style="font-size: 1.1em; color: #888; margin-top: 10px;">
-            Based on {turbine_eff_percent}% Efficiency & {gas_share}% Gas Share
+        <div style="font-size: 1em; color: #888; margin-top: 10px;">
+            Based on {gas_share}% Gas Share & {turbine_eff_percent}%
         </div>
     </div>
     """, unsafe_allow_html=True)
 
-# 2. Charts (Centered / Full Width)
-# Using columns with spacers to center the chart if it feels too wide, 
-# or just full width for better visibility.
+# 2. Charts
 st.subheader("US Emissions Trajectory vs. AI Impact")
 
-# Data Setup
-years = [2020, 2023]
-emissions_hist = [6000, 6200] 
-target_2030 = 7200 * 0.50 # 50% reduction from 2005
-bau_2030 = 5800 # Optimistic business-as-usual decline
+years = [2015, 2020, 2023]
+emissions_hist = [6600, 6000, 6200] 
+target_2030 = 7200 * 0.50 
+bau_2030 = 5800 
 
 fig1 = go.Figure()
 
-# History Line
 fig1.add_trace(go.Scatter(
     x=years, y=emissions_hist, 
     mode='lines+markers', 
@@ -136,7 +174,6 @@ fig1.add_trace(go.Scatter(
     line=dict(color='gray', width=2)
 ))
 
-# Target Path Line
 fig1.add_trace(go.Scatter(
     x=[2023, 2030], y=[6200, target_2030], 
     mode='lines', 
@@ -144,7 +181,6 @@ fig1.add_trace(go.Scatter(
     line=dict(color='green', dash='dash', width=2)
 ))
 
-# AI Impact Stacked Bar
 fig1.add_trace(go.Bar(
     x=[2030], y=[bau_2030], 
     name='2030 Baseline Estimate', 
@@ -166,10 +202,8 @@ fig1.update_layout(
     hovermode="x unified"
 )
 
-# Centering the chart using container width
 st.plotly_chart(fig1, use_container_width=True)
 
-
 st.markdown("---")
 with st.expander("📚 Data Sources & Methodology"):
     st.markdown(f"""
@@ -178,7 +212,4 @@ with st.expander("📚 Data Sources & Methodology"):
     * **Efficiency Logic:** Emissions Factor is calculated dynamically based on the **Electrical Efficiency** slider ({turbine_eff_percent}% currently selected).
         * *Formula:* `EF = (Benchmark Carbon Content) / Efficiency %`
         * *Benchmark:* Based on standard NREL data for CCGT (486 gCO2e/kWh @ 60% efficiency).
-        * Lower efficiency (e.g., 35% for Aeroderivative/Peakers) results in higher emissions per kWh.
-    * **Capacity Factor:** Fixed at **{int(CAPACITY_FACTOR*100)}%** to represent high uptime requirements for AI workloads.
-    * **Source Data:** Turbine efficiency ranges (35-60%) derived from industrial comparisons of Aeroderivative GTs vs H-Class CCGTs.
     """)