recommender.py

# app/recommender.py

import pandas as pd
import json

def recommend_plans(bill_file, customer_type):
    """
    Recommend SME or Enterprise plans based on usage patterns.
    """
    # Load plan data
    if customer_type == "SME":
        plans = pd.read_csv("data/plans_sme.csv")
    elif customer_type == "Enterprise":
        plans = pd.read_csv("data/plans_enterprise.csv")
    else:
        return pd.DataFrame([{"message": "No plans available for this customer type"}])

    # Load bill data (simulate usage capture for now)
    try:
        if bill_file.name.endswith(".json"):
            bill_data = json.load(bill_file)
        else:
            bill_data = {"lines": 10, "data_usage_gb": 50, "current_cost": 500}
    except:
        bill_data = {"lines": 10, "data_usage_gb": 50, "current_cost": 500}

    # Simple matching logic: filter plans based on number of lines / data usage
    recommended = plans[
        (plans["min_lines"] <= bill_data["lines"]) &
        (plans["max_lines"] >= bill_data["lines"])
    ]

    recommended = recommended.sort_values(by="price_per_line")

    return recommended[["plan_name", "price_per_line", "data_quota_gb", "notes"]].head(5)