Let AI Create Offering Memorandums Automatically

pip install openai pandas matplotlib jinja2 weasyprint pillow

from openai import OpenAI

client = OpenAI()

def generate_executive_summary(data: dict) -> str:
    prompt = f"""
    Write a professional executive summary for a commercial
    real estate Offering Memorandum.

    Property: {data["property_name"]}
    Location: {data["location"]}
    NOI: ${data["noi"]:,}
    Cap Rate: {data["cap_rate"]}%
    Year Built: {data["year_built"]}
    Square Feet: {data["square_feet"]:,}

    Tone: authoritative, investor-focused, concise.
    Length: 3 paragraphs maximum.
    """
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

summary = generate_executive_summary(property_data)

import matplotlib.pyplot as plt

def generate_tenant_chart(df: pd.DataFrame, output_path: str = "tenant_chart.png"):
    fig, ax = plt.subplots(figsize=(7, 7))
    colors = ["#1e3a5f", "#2563eb", "#60a5fa"]

    wedges, texts, autotexts = ax.pie(
        df["rent"],
        labels=df["name"],
        autopct="%1.1f%%",
        colors=colors,
        startangle=90,
        wedgeprops=dict(width=0.6)   # Donut style
    )
    ax.set_title("Tenant Income Distribution", fontsize=16, fontweight="bold")
    fig.savefig(output_path, dpi=150, bbox_inches="tight", transparent=True)
    plt.close()
    return output_path

chart_path = generate_tenant_chart(rent_roll_df)

from PIL import Image as PILImage
from pathlib import Path
from jinja2 import Environment, FileSystemLoader

def normalize_images(paths: list[str], size=(1400, 900)) -> list[str]:
    """Standardize all images to the same resolution."""
    out = []
    for path in paths:
        img = PILImage.open(path).convert("RGB")
        img.thumbnail(size, PILImage.LANCZOS)
        out_path = f"normalized_{Path(path).name}"
        img.save(out_path, quality=92)
        out.append(out_path)
    return out

def render_om_html(data, summary, rent_roll_df, metrics, chart_path) -> str:
    env = Environment(loader=FileSystemLoader("."))
    template = env.get_template("om_template.html")

    return template.render(
        property_name=data["property_name"],
        location=data["location"],
        broker_logo=data["broker_logo"],
        images=normalize_images(data["images"]),
        summary=summary,
        rent_roll_table=rent_roll_df.to_html(classes="rent-roll-table", index=False),
        chart_path=chart_path,
        metrics=metrics,
    )

html_content = render_om_html(property_data, summary, rent_roll_df, metrics, chart_path)