import pandas as pd
from datetime import datetime

# Load Finta export
df = pd.read_csv("pipeline-export.csv")

# Pipeline summary
summary = df.groupby("Stage").agg(
    count=("Name", "count"),
    avg_check=("Check Size", "mean"),
).reset_index()

print("Pipeline Summary:")
print(summary.to_string(index=False))

# Conversion rates
stages = ["Researching", "Reaching Out", "Intro Meeting", "Follow-up", "Due Diligence", "Term Sheet", "Closed"]
for i in range(len(stages) - 1):
    current = len(df[df["Stage"] == stages[i]])
    next_stage = len(df[df["Stage"] == stages[i+1]])
    rate = (next_stage / current * 100) if current > 0 else 0
    print(f"  {stages[i]} -> {stages[i+1]}: {rate:.0f}%")

def generate_weekly_report(df: pd.DataFrame) -> str:
    total = len(df)
    active = len(df[df["Stage"].isin(["Intro Meeting", "Follow-up", "Due Diligence"])])
    term_sheets = len(df[df["Stage"] == "Term Sheet"])
    closed = len(df[df["Stage"] == "Closed"])

    return f"""
Fundraise Pipeline Report ({datetime.now().strftime('%Y-%m-%d')})
==================================================
Total investors: {total}
Active conversations: {active}
Term sheets: {term_sheets}
Closed: {closed}
"""