Create app.py

app.py

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+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")
+
+# KPI Dropdown with units
+kpi_options = {
+    'CRF': '%',
+    'Feed Water Temp': '°C',
+    'S:F': '',
+    'SSC': 'kg/kg',
+    'SWC': 'kg/kg',
+    'Make up water': 'kL'
+}
+kpi_selected = st.selectbox("Select KPI", list(kpi_options.keys()))
+unit = kpi_options[kpi_selected]
+
+# Inputs for Min, Max, and Mean
+col1, col2, col3 = st.columns(3)
+with col1:
+    min_val = st.number_input(f"Enter Min Value ({unit})", value=60.0)
+with col2:
+    mean_val = st.number_input(f"Enter Mean (Avg) Value ({unit})", value=100.0)
+with col3:
+    max_val = st.number_input(f"Enter Max Value ({unit})", value=140.0)
+
+# Inputs for Probability Densities
+col4, col5, col6 = st.columns(3)
+with col4:
+    min_pdf = st.number_input("Enter Min Value Probability Density", value=0.0)
+with col5:
+    mean_pdf = st.number_input("Enter Mean Value Probability Density", value=1.0)
+with col6:
+    max_pdf = st.number_input("Enter Max Value Probability Density", value=0.0)
+
+# Validation
+if max_val > mean_val > min_val:
+    # Generate X values (200 points evenly spaced)
+    x = np.linspace(min_val, max_val, 200)
+
+    # Custom quadratic bell shape through interpolation of given PDF values
+    A = np.array([
+        [min_val**2, min_val, 1],
+        [mean_val**2, mean_val, 1],
+        [max_val**2, max_val, 1]
+    ])
+    B = np.array([min_pdf, mean_pdf, max_pdf])
+    coeffs = np.linalg.solve(A, B)
+    a, b, c = coeffs
+
+    # Compute Y values based on custom quadratic
+    y = a * x**2 + b * x + c
+    y = np.maximum(y, 0)  # Ensure no negative values
+
+    df = pd.DataFrame({
+        f"{kpi_selected} Value ({unit})": x,
+        f"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.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
+    st.pyplot(fig)
+
+    # Download data as CSV
+    csv = df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="Download Data as CSV",
+        data=csv,
+        file_name=f"{kpi_selected}_custom_bell_curve_data.csv",
+        mime='text/csv'
+    )
+
+    # Download plot as PNG
+    buf = BytesIO()
+    fig.savefig(buf, format="png")
+    st.download_button(
+        label="Download Plot as PNG",
+        data=buf.getvalue(),
+        file_name=f"{kpi_selected}_custom_bell_curve_plot.png",
+        mime="image/png"
+    )
+else:
+    st.warning("Please ensure that: Min < Mean < Max to generate a valid bell curve.")