Add ability to plot saved queries using plotly, need to check performance in production, also need to improve generate_plot function

utils.py

@@ -2,6 +2,8 @@ import colorsys
 from datetime import datetime, date, timedelta
 import numpy as np
 import pandas as pd
+import plotly.express as px
+import plotly.io as pio
 
 def get_workouts(topsets):
     # Ensure all entries have 'WorkoutId' and 'TopSetId', then sort by 'WorkoutId' and 'TopSetId'
@@ -439,4 +441,37 @@ def get_distinct_colors(n):
         rgb = colorsys.hls_to_rgb(hue, 0.6, 0.4)  # Fixed lightness and saturation
         hex_color = '#{:02x}{:02x}{:02x}'.format(int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255))
         colors.append(hex_color)
-    return colors
+    return colors
+
+def generate_plot(df, title):
+    """
+    Analyzes the DataFrame and generates an appropriate Plotly visualization.
+    Returns the Plotly figure as a div string.
+    """
+    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)
+
+    # Convert Plotly figure to HTML div
+    plot_div = pio.to_html(fig, full_html=False)
+    return plot_div