Update app.py

app.py

@@ -3,37 +3,56 @@ import pandas as pd
 import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
-import plotly.subplots as sp
 import gradio as gr
 from datetime import datetime
 
-# Constants
+# Constants - Updated for NASA's new format
 NASA_DATA_URL = "https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv"
 CURRENT_YEAR = datetime.now().year
+MONTHS = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 
+          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
 
 def load_and_process_data():
-    """Load and process NASA temperature data with robust error handling"""
+    """Load and process NASA temperature data with updated format handling"""
     try:
-        # Read NASA data with metadata handling
+        # Read NASA data with updated parameters
         df = pd.read_csv(
             NASA_DATA_URL,
             skiprows=1,
-            na_values=['***', '****', '*****', '******']
+            na_values=['***', '****', '*****', '******'],
+            engine='python'
         )
         
-        # Clean and reshape data
-        df = df[df['Year'] >= 1880]  # Reliable data starts from 1880
-        df = df[['Year'] + [str(m) for m in range(1,13)]]
-        df.columns = ['Year'] + [f"{i:02d}" for i in range(1,13)]
+        # Clean and reshape data - handle new column format
+        df = df[df['Year'] >= 1880]
         
-        # Melt to long format and handle dates
-        df = df.melt(id_vars='Year', var_name='Month', value_name='Anomaly')
-        df['Date'] = pd.to_datetime(df['Year'].astype(str) + '-' + df['Month'], format='%Y-%m')
+        # Select only year and month columns (new format uses month names)
+        df = df[['Year'] + MONTHS]
+        
+        # Melt to long format using month names
+        df = df.melt(
+            id_vars='Year', 
+            var_name='Month', 
+            value_name='Anomaly'
+        )
+        
+        # Convert month names to numeric values
+        month_map = {name: f"{i:02d}" for i, name in enumerate(MONTHS, 1)}
+        df['Month_Num'] = df['Month'].map(month_map)
+        
+        # Create date column
+        df['Date'] = pd.to_datetime(
+            df['Year'].astype(str) + '-' + df['Month_Num'],
+            format='%Y-%m'
+        )
+        
+        # Clean and process anomalies
         df = df.dropna(subset=['Anomaly'])
         df['Anomaly'] = df['Anomaly'].astype(float)
         df['Decade'] = (df['Year'] // 10) * 10
         
         # Calculate rolling averages
+        df = df.sort_values('Date')
         df['5yr_avg'] = df['Anomaly'].rolling(60, min_periods=1).mean()
         df['10yr_avg'] = df['Anomaly'].rolling(120, min_periods=1).mean()
         
@@ -43,243 +62,50 @@ def load_and_process_data():
         print(f"Data loading error: {e}")
         return pd.DataFrame()
 
-def create_time_series_plot(df, show_uncertainty=False):
-    """Create interactive time series plot with advanced features"""
-    if df.empty:
-        return go.Figure()
-    
-    fig = go.Figure()
-    
-    # Add monthly anomalies
-    fig.add_trace(go.Scatter(
-        x=df['Date'], 
-        y=df['Anomaly'],
-        mode='markers',
-        marker=dict(size=3, opacity=0.3, color='lightgray'),
-        name='Monthly Anomaly'
-    ))
-    
-    # Add trend lines
-    fig.add_trace(go.Scatter(
-        x=df['Date'], 
-        y=df['5yr_avg'],
-        mode='lines',
-        line=dict(width=2, color='blue'),
-        name='5-Year Average'
-    ))
-    
-    fig.add_trace(go.Scatter(
-        x=df['Date'], 
-        y=df['10yr_avg'],
-        mode='lines',
-        line=dict(width=3, color='red'),
-        name='10-Year Average'
-    ))
-    
-    # Add uncertainty bands if requested
-    if show_uncertainty:
-        rolling_std = df['Anomaly'].rolling(120).std()
-        fig.add_trace(go.Scatter(
-            x=df['Date'],
-            y=df['10yr_avg'] + rolling_std,
-            fill=None,
-            mode='lines',
-            line=dict(width=0),
-            showlegend=False
-        ))
-        
-        fig.add_trace(go.Scatter(
-            x=df['Date'],
-            y=df['10yr_avg'] - rolling_std,
-            fill='tonexty',
-            mode='lines',
-            line=dict(width=0),
-            name='Uncertainty'
-        ))
-    
-    # Add reference line at 0°C
-    fig.add_hline(y=0, line_dash="dash", line_color="black")
-    
-    # Add significant warming markers
-    recent = df[df['Year'] >= 2000]
-    if not recent.empty:
-        fig.add_trace(go.Scatter(
-            x=recent['Date'],
-            y=recent['10yr_avg'],
-            mode='markers',
-            marker=dict(size=8, color='red'),
-            name='Post-2000 Average'
-        ))
-    
-    # Layout enhancements
-    fig.update_layout(
-        title='Global Temperature Anomalies (1880-Present)',
-        xaxis_title='Year',
-        yaxis_title='Temperature Anomaly (°C)',
-        hovermode='x unified',
-        template='plotly_dark',
-        height=600,
-        annotations=[
-            dict(
-                x=0.01, y=0.01,
-                xref="paper", yref="paper",
-                text="Data Source: NASA GISS",
-                showarrow=False
-            )
-        ]
-    )
-    
-    return fig
-
-def create_heatmap(df, decade_range=(1950, CURRENT_YEAR)):
-    """Create decadal heatmap visualization"""
-    if df.empty:
-        return go.Figure()
-    
-    # Filter and aggregate data
-    filtered = df[df['Decade'].between(decade_range[0], decade_range[1])]
-    if filtered.empty:
-        return go.Figure()
-    
-    pivot_df = filtered.pivot_table(
-        index='Decade',
-        columns='Month',
-        values='Anomaly',
-        aggfunc='mean'
-    )
-    
-    # Create heatmap
-    fig = px.imshow(
-        pivot_df,
-        labels=dict(x="Month", y="Decade", color="Anomaly"),
-        x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 
-           'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],
-        color_continuous_scale='RdBu_r',
-        aspect="auto"
-    )
-    
-    # Add annotations
-    for i, row in enumerate(pivot_df.values):
-        for j, value in enumerate(row):
-            fig.add_annotation(
-                x=j, y=i,
-                text=f"{value:.1f}",
-                showarrow=False,
-                font=dict(color='black' if abs(value) < 0.8 else 'white')
-            )
-    
-    # Layout enhancements
-    fig.update_layout(
-        title=f'Decadal Temperature Anomalies ({decade_range[0]}-{decade_range[1]})',
-        xaxis_title="",
-        yaxis_title="Decade",
-        coloraxis_colorbar=dict(title="Anomaly (°C)"),
-        height=600
-    )
-    
-    return fig
-
-def create_regional_comparison(df):
-    """Create regional comparison visualization (mock data)"""
-    # In a real implementation, this would use actual regional data
-    regions = {
-        'Arctic': 2.5,
-        'Global': 1.2,
-        'Europe': 1.5,
-        'Asia': 1.3,
-        'North America': 1.4,
-        'Africa': 1.1,
-        'South America': 1.0,
-        'Oceania': 1.1,
-        'Antarctica': 0.8
-    }
-    
-    fig = go.Figure(go.Bar(
-        x=list(regions.values()),
-        y=list(regions.keys()),
-        orientation='h',
-        marker_color='crimson'
-    ))
-    
-    fig.update_layout(
-        title='Regional Warming Rates (Since Pre-Industrial)',
-        xaxis_title='Temperature Increase (°C)',
-        yaxis_title='Region',
-        template='plotly_dark',
-        height=500
-    )
-    
-    return fig
+# ... [rest of visualization functions remain unchanged] ...
 
 def create_dashboard():
-    """Create Gradio dashboard with multiple visualizations"""
+    """Create Gradio dashboard with fixed components"""
     df = load_and_process_data()
     
     with gr.Blocks(title="NASA Climate Viz", theme=gr.themes.Soft()) as demo:
         gr.Markdown("# 🌍 Earth's Surface Temperature Analysis")
-        gr.Markdown("### Visualization of NASA's Global Temperature Data")
         
-        with gr.Row():
-            gr.Markdown("""
-            **Data Source**: [NASA Goddard Institute for Space Studies](https://data.giss.nasa.gov/gistemp/)  
-            **Last Update**: {CURRENT_YEAR}  
-            **Base Period**: 1951-1980
-            """)
+        # ... [header remains unchanged] ...
         
         with gr.Tab("Time Series Analysis"):
             gr.Markdown("## Global Temperature Anomalies Over Time")
             with gr.Row():
                 show_uncertainty = gr.Checkbox(label="Show Uncertainty Bands")
-                year_range = gr.RangeSlider(
+                # FIXED: Use new Slider syntax with range=True
+                year_range = gr.Slider(
                     minimum=1880,
                     maximum=CURRENT_YEAR,
                     value=[1950, CURRENT_YEAR],
-                    label="Year Range"
+                    label="Year Range",
+                    step=1,
+                    interactive=True,
+                    range=True  # This creates dual-handle range slider
                 )
             time_series = gr.Plot()
         
         with gr.Tab("Decadal Heatmap"):
             gr.Markdown("## Monthly Anomalies by Decade")
-            decade_range = gr.RangeSlider(
+            # FIXED: Updated slider syntax
+            decade_range = gr.Slider(
                 minimum=1880,
                 maximum=CURRENT_YEAR - (CURRENT_YEAR % 10),
                 value=[1950, CURRENT_YEAR - (CURRENT_YEAR % 10)],
                 step=10,
-                label="Decade Range"
+                label="Decade Range",
+                interactive=True,
+                range=True  # Dual-handle range slider
             )
             heatmap = gr.Plot()
         
-        with gr.Tab("Regional Comparison"):
-            gr.Markdown("## Regional Warming Patterns")
-            gr.Markdown("*Note: Regional data shown is for demonstration purposes*")
-            region_plot = gr.Plot()
+        # ... [other tabs remain unchanged] ...
         
-        with gr.Tab("Data Insights"):
-            gr.Markdown("## Key Climate Observations")
-            if not df.empty:
-                latest = df[df['Year'] == df['Year'].max()]
-                hottest_year = df.groupby('Year')['Anomaly'].mean().idxmax()
-                hottest_decade = df.groupby('Decade')['Anomaly'].mean().idxmax()
-                
-                insights = f"""
-                - 📈 **Current Decade ({CURRENT_YEAR//10*10}s)**: {df[df['Decade'] == CURRENT_YEAR//10*10]['Anomaly'].mean():.2f}°C anomaly
-                - 🔥 **Hottest Year**: {hottest_year} ({df[df['Year'] == hottest_year]['Anomaly'].mean():.2f}°C)
-                - 🌡️ **Recent Temperature**: {latest['Anomaly'].mean():.2f}°C above baseline
-                - 📅 **Long-term Trend**: {df['Anomaly'].mean():.2f}°C average anomaly since 1880
-                - ⏱️ **Acceleration**: Warming rate has doubled since 1980
-                """
-            else:
-                insights = "Data not available"
-            
-            gr.Markdown(insights)
-            gr.Markdown("### Temperature Change Since 1880")
-            if not df.empty:
-                change_df = df.groupby('Year').agg({'Anomaly': 'mean'}).reset_index()
-                change_df['Change'] = change_df['Anomaly'].cumsum()
-                change_plot = px.area(change_df, x='Year', y='Change')
-                gr.Plot(change_plot)
-        
-        # Event handling
+        # Event handling with fixed input parameters
         show_uncertainty.change(
             fn=lambda u, y: create_time_series_plot(
                 df[(df['Year'] >= y[0]) & (df['Year'] <= y[1])],
@@ -289,7 +115,7 @@ def create_dashboard():
             outputs=time_series
         )
         
-        year_range.change(
+        year_range.input(  # Use .input instead of .change for realtime updates
             fn=lambda y, u: create_time_series_plot(
                 df[(df['Year'] >= y[0]) & (df['Year'] <= y[1])],
                 u
@@ -298,8 +124,8 @@ def create_dashboard():
             outputs=time_series
         )
         
-        decade_range.change(
-            fn=lambda dr: create_heatmap(df, dr),
+        decade_range.input(
+            fn=lambda dr: create_heatmap(df, (dr[0], dr[1])),
             inputs=decade_range,
             outputs=heatmap
         )
@@ -311,14 +137,9 @@ def create_dashboard():
         )
         
         demo.load(
-            fn=lambda: create_heatmap(df, [1950, CURRENT_YEAR]),
+            fn=lambda: create_heatmap(df, (1950, CURRENT_YEAR)),
             outputs=heatmap
         )
-        
-        demo.load(
-            fn=create_regional_comparison,
-            outputs=region_plot
-        )
     
     return demo