Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-from scipy.stats import norm
 from io import BytesIO
 
 st.title("KPI Std. Deviation")
@@ -58,16 +57,26 @@ if max_val > mean_val > min_val:
 
     df = pd.DataFrame({
         f"{kpi_selected} Value ({unit})": x,
-        f"Probability Density": y
+        "Probability Density": y
     })
 
     # Plot
     fig, ax = plt.subplots()
-    ax.plot(x, y, color='royalblue', linewidth=2)
-    ax.set_title(f"{kpi_selected} - Custom Bell Curve")
+    ax.plot(x, y, color='royalblue', linewidth=2, label='Deviation Curve')
+
+    # Markers for Min, Mean, Max
+    ax.axvline(min_val, color='blue', linestyle='--', linewidth=1)
+    ax.axvline(mean_val, color='green', linestyle='--', linewidth=1)
+    ax.axvline(max_val, color='red', linestyle='--', linewidth=1)
+
+    ax.text(min_val, max_pdf * 0.1, f'Min\n{min_val} {unit}', color='blue', ha='center')
+    ax.text(mean_val, mean_pdf + 0.05, f'Mean\n{mean_val} {unit}', color='green', ha='center')
+    ax.text(max_val, max_pdf * 0.1, f'Max\n{max_val} {unit}', color='red', ha='center')
+
+    ax.set_title(f"{kpi_selected} - Std. Deviation Curve")
     ax.set_xlabel(f"{kpi_selected} ({unit})")
     ax.set_ylabel("Probability Density")
-    ax.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)  # Fine grid
+    ax.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)
     st.pyplot(fig)
 
     # Download data as CSV
@@ -89,4 +98,4 @@ if max_val > mean_val > min_val:
         mime="image/png"
     )
 else:
-    st.warning("Please ensure that: Min < Mean < Max to generate a valid bell curve.")
+    st.warning("Please ensure that: Min < Mean < Max to generate a valid bell curve.")