predictor/chart_plotter.py

import altair as alt
import pandas as pd
import streamlit as st


def plot_actual_vs_predicted(df: pd.DataFrame):
    if "Product_Store_Sales_Total" not in df.columns or "Predicted_Sales" not in df.columns:
        st.warning("ℹ️ Required columns not found for plotting.")
        return

    plot_df = df[["Product_Store_Sales_Total", "Predicted_Sales"]].dropna()

    if plot_df.empty:
        st.info("ℹ️ Not enough valid rows for plotting.")
        return

    x_min = plot_df["Product_Store_Sales_Total"].min()
    x_max = plot_df["Product_Store_Sales_Total"].max()

    # Scatter plot
    scatter = alt.Chart(plot_df).mark_circle(size=60, color='steelblue').encode(
        x=alt.X("Product_Store_Sales_Total", title="Actual Sales"),
        y=alt.Y("Predicted_Sales", title="Predicted Sales"),
        tooltip=["Product_Store_Sales_Total", "Predicted_Sales"]
    )

    # Parity line (y = x)
    parity_line = alt.Chart(pd.DataFrame({
        'x': [x_min, x_max],
        'y': [x_min, x_max]
    })).mark_line(color='red', strokeDash=[5, 5]).encode(
        x='x:Q',
        y='y:Q'
    )

    # Combine
    chart = (scatter + parity_line).properties(
        width=700,
        height=400,
        title="Actual vs Predicted Sales"
    ).configure_title(fontSize=18, anchor='middle')

    st.altair_chart(chart, use_container_width=True)