Update src/streamlit_app.py

src/streamlit_app.py

@@ -14,7 +14,7 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# --- 2. Sidebar for Metadata (Rubric: Data is linked) ---
+# --- 2. Sidebar for Metadata ---
 with st.sidebar:
     st.header("About This Project")
     st.markdown("**Authors:**")
@@ -32,18 +32,18 @@ with st.sidebar:
     st.markdown("[IMF Banking Crises](https://www.imf.org/en/Publications/WP/Issues/2018/09/14/Systemic-Banking-Crises-Revisited-46232)")
     st.info("This dashboard visualizes how the 2008 financial crisis propagated from the US to the rest of the world.")
 
-# --- 3. Main Title (Rubric: Compelling Title included) ---
+# --- 3. Main Title ---
 st.title("📉 The Domino Effect: How the 2008 Crisis Went Global")
 st.markdown("---")
 
-# --- 4. Connective Text Part 1 (Rubric: Write up - Paragraph 1) ---
+# --- 4. Connective Text Part 1 ---
 st.markdown("""
 ### 🌍 When Wall Street Sneezed, the World Caught a Cold
 In 2008, what started as a burst housing bubble in the United States quickly mutated into a global economic catastrophe. But how does a problem in one country infect the entire world? The answer lies in **Financial Contagion**.
 
 Just like a virus moving through a crowded room, economic shockwaves travel through international connections. In our modern economy, nations are linked by **Trade** (buying and selling goods) and **Finance** (banks lending money across borders). When the US economy collapsed, it stopped buying goods from China and Germany, and US banks pulled money out of emerging markets like Brazil.
 
-In this data story, we will explore these connections. We will see how "open" economies—those that trade the most—often paid the highest price during the panic, and how the "cure" (government bailouts) created a new disease: massive public debt.
+In this data story, we will explore these connections. We will see how "open" economies—those that trade the most—often paid the highest price during the panic, and how the "cure" (government bailouts) created a new disease: massive public deficits.
 """)
 
 # --- 5. Data Loading (Cached) ---
@@ -53,7 +53,8 @@ def load_wdi_data():
     indicators = {
         'GDP_Growth': 'NY.GDP.MKTP.KD.ZG',
         'Trade_GDP': 'NE.TRD.GNFS.ZS',
-        'Gov_Debt': 'GC.DOD.TOTL.GD.ZS',
+        # Switched to Deficit because Debt stock data is sparse for Japan/Germany in API
+        'Gov_Deficit': 'GC.BAL.CASH.GD.ZS', 
         'Youth_Unemployment': 'SL.UEM.1524.ZS',
         'Total_Unemployment': 'SL.UEM.TOTL.ZS'
     }
@@ -88,7 +89,7 @@ def load_wdi_data():
 with st.spinner("Fetching data from World Bank..."):
     df = load_wdi_data()
 
-# --- 6. Central Interactive Visualization (Rubric: Interactive, Large, Friendly) ---
+# --- 6. Central Interactive Visualization ---
 st.header("📊 The Crash in Motion (2000-2020)")
 st.markdown("""
 **How to read this chart:** * **X-Axis:** Represents how "open" an economy is (Trade as % of GDP).
@@ -124,34 +125,38 @@ fig_main.add_hline(y=0, line_dash="dash", line_color="red", annotation_text="Rec
 st.plotly_chart(fig_main, use_container_width=True)
 st.caption("Source: World Bank World Development Indicators (WDI). Size of bubble represents trade openness.")
 
-# --- 7. Connective Text Part 2 (Rubric: Write up - Paragraph 2) ---
+# --- 7. Connective Text Part 2 ---
 st.markdown("""
-### 💸 The Cost of Bailouts: Private Failure, Public Debt
+### 💸 The Cost of Bailouts: Massive Deficits
 The immediate crash was stopped, but at a high cost. When major banks failed in 2008, governments stepped in to save them with massive "bailouts"—using taxpayer money to prevent the financial system from collapsing.
 
-This created a massive transfer of debt. What was originally **private debt** (held by banks) became **public debt** (held by the government). As the chart below shows, countries like the United States and United Kingdom saw their central government debt explode after 2008. This debt burden forced many nations into "austerity" measures, cutting public services to pay back creditors.
+This created massive **budget deficits**. As the chart below shows, governments like the United States, UK, and Greece spent far more than they earned in 2009 (indicated by the deep plunge into negative territory). This sudden spending shock laid the groundwork for the Sovereign Debt Crisis that followed.
 """)
 
-# --- 8. Contextual Viz 1 (Rubric: Contextual Viz included & Cited) ---
+# --- 8. Contextual Viz 1: Deficits (Better Data Coverage) ---
 debt_countries = ['United States', 'United Kingdom', 'Japan', 'Greece', 'Germany']
-df_debt = df[df['Country Name'].isin(debt_countries)]
+df_debt = df[df['Country Name'].isin(debt_countries)].copy()
+
+# Fix: Drop NaN values specifically for this plot so lines connect properly
+df_debt = df_debt.dropna(subset=['Gov_Deficit'])
 
 fig_debt = px.line(
     df_debt,
     x="Year",
-    y="Gov_Debt",
+    y="Gov_Deficit",
     color="Country Name",
-    title="Contextual Viz 1: The Debt Explosion (2000-2020)",
-    labels={"Gov_Debt": "Central Gov. Debt (% of GDP)"},
+    title="Contextual Viz 1: The Cost of Bailouts - Budget Surplus/Deficit (% of GDP)",
+    labels={"Gov_Deficit": "Surplus/Deficit (% of GDP)"},
     markers=True,
     template="plotly_white"
 )
-fig_debt.add_vline(x=2008, line_dash="dot", line_color="black", annotation_text="Crisis Starts")
+fig_debt.add_vline(x=2009, line_dash="dot", line_color="red", annotation_text="Peak Bailout Spending")
+fig_debt.add_hline(y=0, line_color="black", line_width=1) # Zero line for balance
 
 st.plotly_chart(fig_debt, use_container_width=True)
-st.caption("Source: World Bank WDI. Note the sharp upward trend starting exactly in 2008 for Western economies.")
+st.caption("Source: World Bank WDI. Negative values indicate a government deficit (spending more than earning). Note the synchronized crash in 2009.")
 
-# --- 9. Connective Text Part 3 (Rubric: Write up - Paragraph 3) ---
+# --- 9. Connective Text Part 3 ---
 st.markdown("""
 ### 🎓 The Lost Generation
 While stock markets eventually recovered, the human cost of the crisis lingered for years. In Europe specifically, the financial crisis mutated into a sovereign debt crisis, leading to a long-term depression in the labor market.
@@ -159,7 +164,7 @@ While stock markets eventually recovered, the human cost of the crisis lingered
 The group hit hardest was young adults (ages 15-24). In countries like Spain and Greece, youth unemployment skyrocketed to over **50%**. This created a "Lost Generation" of workers who were locked out of the job market during their most critical skill-building years. The comparison below highlights how disproportionately youth were affected compared to the general population during the peak of the crisis.
 """)
 
-# --- 10. Contextual Viz 2 (Rubric: Contextual Viz included & Cited) ---
+# --- 10. Contextual Viz 2: The Lost Generation ---
 # Snapshot of 2013 (Peak Euro Crisis)
 df_2013 = df[df['Year'] == 2013]
 focus_countries = ['Spain', 'Greece', 'Italy', 'Portugal', 'Germany', 'United States']
@@ -193,10 +198,10 @@ fig_youth = px.bar(
 st.plotly_chart(fig_youth, use_container_width=True)
 st.caption("Source: World Bank WDI. Data represents the peak of the Eurozone crisis in 2013.")
 
-# --- 11. Final Citations (Rubric: Citations included) ---
+# --- 11. Final Citations ---
 st.markdown("---")
 st.markdown("### 📚 References")
 st.markdown("""
-1.  **World Bank World Development Indicators (WDI):** The primary source for GDP, Trade, Debt, and Unemployment data. [Link to Dataset](https://databank.worldbank.org/source/world-development-indicators)
+1.  **World Bank World Development Indicators (WDI):** The primary source for GDP, Trade, Deficits, and Unemployment data. [Link to Dataset](https://databank.worldbank.org/source/world-development-indicators)
 2.  **IMF Systemic Banking Crises Database:** Used for identifying crisis timelines. Laeven, L., & Valencia, F. (2018). [Link to Paper](https://www.imf.org/en/Publications/WP/Issues/2018/09/14/Systemic-Banking-Crises-Revisited-46232)
 """)