utils/report_generator.py

import matplotlib.pyplot as plt
import pandas as pd
import io
import base64

def generate_report(df, spending_analysis, recommendations):
    report = f"""
    ## Invoice Reader & Budget Categorizer Report

    ### Categorized Transactions
    {df.to_markdown(index=False)}

    ### Spending Insights
    {spending_analysis}

    ### Budget Recommendations
    {recommendations}
    """
    
    # Generate visualizations
    if not df.empty:
        # Category-wise spending pie chart
        category_spending = df.groupby("category")["amount"].sum()
        plt.figure(figsize=(6, 6))
        plt.pie(category_spending, labels=category_spending.index, autopct="%1.1f%%")
        plt.title("Category-wise Spending")
        buf = io.BytesIO()
        plt.savefig(buf, format="png")
        buf.seek(0)
        category_plot = base64.b64encode(buf.getvalue()).decode("utf-8")
        plt.close()
        
        # Monthly spending trend
        monthly_spending = df.groupby("month")["amount"].sum()
        plt.figure(figsize=(8, 4))
        monthly_spending.plot(kind="line", marker="o")
        plt.title("Monthly Spending Trend")
        plt.xlabel("Month")
        plt.ylabel("Amount")
        buf = io.BytesIO()
        plt.savefig(buf, format="png")
        buf.seek(0)
        monthly_plot = base64.b64encode(buf.getvalue()).decode("utf-8")
        plt.close()
        
        report += f"""
        ### Visualizations
        #### Category-wise Spending
        ![Category-wise Spending](data:image/png;base64,{category_plot})

        #### Monthly Spending Trend
        ![Monthly Spending Trend](data:image/png;base64,{monthly_plot})
        """
    
    return report