import pandas as pd
import os
import random
from faker import Faker

def generate_advanced_data(n=1000):
    """
    Generate synthetic patient data for hospital readmission prediction.
    Includes clinical and social features commonly used in care management models.
    """
    fake = Faker()
    diagnoses = ['Diabetes', 'CHF', 'COPD', 'CKD', 'Depression']
    data = []

    for i in range(n):
        chronic = random.randint(0, 5)
        er_visits = random.randint(0, 4)
        adherence = random.choice(['Low', 'Medium', 'High'])
        prev_readmits = random.randint(0, 3)
        age = random.randint(45, 90)

        risk_score = (
            chronic * 1.2 +
            er_visits +
            (0 if adherence == 'High' else 1) +
            prev_readmits +
            (1 if random.random() > 0.5 else 0)
        )
        readmitted = 1 if risk_score > 5 else 0

        record = {
            "Patient_ID": f"P{i+1:04}",
            "Age": age,
            "Gender": random.choice(["M", "F"]),
            "Chronic_Conditions": chronic,
            "Primary_Diagnosis": random.choice(diagnoses),
            "Num_ER_Visits": er_visits,
            "Last_Discharge_Days_Ago": random.randint(1, 60),
            "Previous_Readmissions": prev_readmits,
            "FollowUp_Scheduled": random.choice(["Yes", "No"]),
            "Medication_Adherence": adherence,
            "Language_Barrier": random.choice(["Yes", "No"]),
            "Housing_Instability": random.choice(["Yes", "No"]),
            "Caregiver_Support": random.choice(["Yes", "No"]),
            "Readmitted_30_Days": readmitted
        }

        data.append(record)

    return pd.DataFrame(data)

def load_or_generate_data(path="data/patients.csv", n=1000):
    """
    Load existing patient data if available, otherwise generate synthetic data.
    """
    if os.path.exists(path):
        return pd.read_csv(path)
    
    df = generate_advanced_data(n=n)
    os.makedirs(os.path.dirname(path), exist_ok=True)
    df.to_csv(path, index=False)
    return df