Combining Python with a modern viz library and a bit of automation can give you a repeatable, AI-augmented view of your financial data. Here’s a condensed walkthrough of how I built a first version—and what I’d do differently next time.

Goal

I wanted a single dashboard that could:

  • Pull data from our internal sources (in this example, assume CSV exports or a simple API).
  • Compute a few key metrics and ratios.
  • Optionally use an LLM to generate a short narrative summary of the period.

Stack

  • Python 3.10+ for scripting.
  • Pandas for data loading and transformation.
  • Streamlit for the UI (fast to prototype, easy to share).
  • OpenAI API (or another provider) for the narrative summary—optional.

Step 1: Load and Clean the Data

import pandas as pd

def load_financial_data(path: str) -> pd.DataFrame:
    df = pd.read_csv(path)
    df["date"] = pd.to_datetime(df["date"])
    return df

Keep cleaning logic in small functions so you can test and reuse them.

Step 2: Compute Metrics

def compute_metrics(df: pd.DataFrame) -> dict:
    total_revenue = df["revenue"].sum()
    total_costs = df["costs"].sum()
    margin_pct = (total_revenue - total_costs) / total_revenue * 100 if total_revenue else 0
    return {"revenue": total_revenue, "costs": total_costs, "margin_pct": margin_pct}

Centralizing metrics makes it easy to plug them into the UI and, later, into a prompt for the LLM.

Step 3: Build the Streamlit UI

Streamlit lets you go from script to app quickly. Use columns and metrics displays to show the numbers, and add a text area or auto-generated block for the narrative if you’re calling an LLM.

Step 4: (Optional) Add an LLM Summary

Pass the metrics and maybe a short table summary into a prompt like: “Summarize these financial results in 2–3 sentences for an executive.” Always treat the output as a draft and validate numbers separately.

What I’d Do Next

  • Move from CSV to a proper database or warehouse connection.
  • Add basic auth and deployment (e.g., Streamlit Cloud or internal server).
  • Introduce simple caching so repeated views don’t recompute everything.

This first version proved the concept; the next one will focus on robustness and scale.