Update app.py

app.py

@@ -27,6 +27,19 @@ st.markdown("""
 
     /* --- 3. Sidebar Typography --- */
     
+    /* Target the text inside the sidebar's markdown container */
+    [data-testid="stSidebarContent"] .stMarkdown {
+        color: white !important;
+    }
+    
+    /* Optional: Target specific headers or paragraphs in the sidebar for clarity */
+    [data-testid="stSidebarContent"] p, 
+    [data-testid="stSidebarContent"] h1, 
+    [data-testid="stSidebarContent"] h2, 
+    [data-testid="stSidebarContent"] h3 {
+        color: white !important;
+    }
+    
     /* The Custom Question Headers */
     .sidebar-question {
         font-size: 1.4rem; 
@@ -78,10 +91,10 @@ st.markdown("""
     
     .metric-card {
         background-color: #ffffff;
-        padding: 30px;
+        padding: 20px;
         border-radius: 12px;
         text-align: center;
-        margin-bottom: 25px;
+        margin-bottom: 15px;
         border: 1px solid #e5e7eb;
         box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
     }
@@ -118,7 +131,6 @@ st.sidebar.markdown("""
 **What will the US emissions of AI be in 2030?** Model the variables below, focused on the efficiency of Natural Gas deployment.
 """)
 st.sidebar.markdown('<div class="spacer"></div>', unsafe_allow_html=True)
-st.sidebar.header("⚙️ Scenario Settings")
 
 # 1. AI Power Demand
 st.sidebar.markdown('<p class="sidebar-question">1. How much power will AI require in 2030? (GW)</p>', unsafe_allow_html=True)
@@ -135,7 +147,7 @@ with st.sidebar.expander("More on AI Demand Forecasts"):
 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)
+st.sidebar.markdown('<p class="sidebar-question">2. What proportion of this power will be supplied by natural gas?</p>', unsafe_allow_html=True)
 gas_share = st.sidebar.slider(
     "Gas Share", 
     min_value=0, max_value=100, value=90, step=5,
@@ -144,17 +156,17 @@ gas_share = st.sidebar.slider(
 )
 
 with st.sidebar.expander("More on Energy Mix"):
-    st.markdown("""
+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. In fact, current projections suggest that **nearly a third of all new data center development will deploy behind-the-meter (BTM) gas generation** to circumvent these bottlenecks.<sup>1,2</sup>
+    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. In fact, current projections suggest that **nearly a third of all new data center development will deploy behind-the-meter (BTM) gas generation** to circumvent these bottlenecks.$^{1,2}$
     
-    * **Grid Dominance:** Natural gas is the backbone of the U.S. power system, accounting for approximately **43% of total utility-scale electricity generation** in 2023.<sup>3,4</sup>
-    * **The Flexibility Wildcard:** "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.<sup>5</sup> However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.<sup>5,6</sup>
+    * **Grid Dominance:** Natural gas is the backbone of the U.S. power system, accounting for approximately **43% of total utility-scale electricity generation** in 2023.$^{3,4}$
+    * **The Flexibility Wildcard:** "Demand response" (or data center flexibility) could theoretically pull gigawatts "out of thin air" by matching AI training jobs to times when the grid has spare capacity.$^{5}$ However, many experts remain skeptical of the true magnitude of this solution, as large-scale implementation faces significant technical hurdles and pushback from major grid operators like PJM.$^{5,6}$
 
     **What about Solar?**
-    While solar prices have dropped ~88% since 2009, it faces physical limits:<sup>5</sup>
-    * **Land Use:** 2 GW of solar requires a land area roughly the size of Manhattan (approx. 60 km²).<sup>5</sup>
-    * **Uptime:** Solar requires massive battery storage for 24/7 reliability, adding complexity for off-grid "island" data centers that cannot draw on spare grid capacity at night.<sup>5</sup>
+    While solar prices have dropped ~88% since 2009, it faces physical limits:$^{5}$
+    * **Land Use:** 2 GW of solar requires a land area roughly the size of Manhattan (approx. 60 km²).$^{5}$
+    * **Uptime:** Solar requires massive battery storage for 24/7 reliability, adding complexity for off-grid "island" data centers that cannot draw on spare grid capacity at night.$^{5}$
       
     ---
     *Sources:*