Switch to using polars

utils.py

@@ -1,7 +1,7 @@
 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 # Keep for now, might remove later if generate_plot is fully replaced
 import math
@@ -110,32 +110,7 @@ def get_distinct_colors(n):
         colors.append(hex_color)
     return colors
 
-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)
-
-    # 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=False, config={'staticPlot': True})
 
 
 def calculate_estimated_1rm(weight, repetitions):