Speed up sql plot generation

utils.py

@@ -106,38 +106,33 @@ def get_distinct_colors(n):
         colors.append(hex_color)
     return colors
 
-def generate_plot(df, title):
+def generate_plot(df: pd.DataFrame, title: str) -> str:
     """
     Analyzes the DataFrame and generates an appropriate Plotly visualization.
     Returns the Plotly figure as a div string.
+    Optimized for speed.
     """
     if df.empty:
         return "<p>No data available to plot.</p>"
 
     num_columns = len(df.columns)
 
-    # Simple logic to decide plot type based on DataFrame structure
-    if num_columns == 1:
-        # Single column: perhaps a histogram or bar chart
-        column = df.columns[0]
-        if pd.api.types.is_numeric_dtype(df[column]):
-            fig = px.histogram(df, x=column, title=title)
-        else:
-            fig = px.bar(df, x=column, title=title)
-    elif num_columns == 2:
-        # Two columns: scatter plot or line chart
-        col1, col2 = df.columns
-        if pd.api.types.is_numeric_dtype(df[col1]) and pd.api.types.is_numeric_dtype(df[col2]):
-            fig = px.scatter(df, x=col1, y=col2, title=title)
-        else:
-            fig = px.bar(df, x=col1, y=col2, title=title)
-    else:
-        # More than two columns: heatmap or other complex plots
-        fig = px.imshow(df.corr(), text_auto=True, title=title)
+    # Dictionary-based lookup for faster decision-making
+    plot_funcs = {
+        1: lambda: px.histogram(df, x=df.columns[0], title=title) 
+        if pd.api.types.is_numeric_dtype(df.iloc[:, 0]) else px.bar(df, x=df.columns[0], title=title),
+
+        2: lambda: px.scatter(df, x=df.columns[0], y=df.columns[1], title=title)
+        if pd.api.types.is_numeric_dtype(df.iloc[:, 0]) and pd.api.types.is_numeric_dtype(df.iloc[:, 1])
+        else px.bar(df, x=df.columns[0], y=df.columns[1], title=title)
+    }
+
+    # Select plot function based on column count
+    fig = plot_funcs.get(num_columns, lambda: px.imshow(df.corr(numeric_only=True), text_auto=True, title=title))()
+
+    # Use static rendering for speed
+    return pio.to_html(fig, full_html=False, include_plotlyjs='cdn', config={'staticPlot': True})
 
-    # Convert Plotly figure to HTML div
-    plot_div = pio.to_html(fig, full_html=False)
-    return plot_div
 
 def calculate_estimated_1rm(weight, repetitions):
     # Ensure the inputs are numeric