import pandas as pd
from datetime import datetime, timedelta

# Mock Patients for Clinicians and CHWs
MOCK_PATIENTS = [
    {"id": "P001", "name": "John Doe", "last_visit": "2023-10-01", "assigned_chw": "Alice Smith"},
    {"id": "P002", "name": "Jane Roe", "last_visit": "2023-10-05", "assigned_chw": "Bob Jones"},
    {"id": "P003", "name": "Sam S.", "last_visit": "2023-10-10", "assigned_chw": "Alice Smith"},
]

# Mock Risk History
history_data = {
    "patient_id": ["P001", "P001", "P001", "P002", "P002", "P003"],
    "date": [
        (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d"),
        (datetime.now() - timedelta(days=15)).strftime("%Y-%m-%d"),
        datetime.now().strftime("%Y-%m-%d"),
        (datetime.now() - timedelta(days=10)).strftime("%Y-%m-%d"),
        datetime.now().strftime("%Y-%m-%d"),
        datetime.now().strftime("%Y-%m-%d"),
    ],
    "rri": [0.15, 0.45, 0.78, 0.22, 0.25, 0.65],
    "mood": ["Good", "Anxious", "Stressed", "Stable", "Good", "Lonely"],
}
MOCK_HISTORY = pd.DataFrame(history_data)

def get_patient_history(patient_id):
    return MOCK_HISTORY[MOCK_HISTORY["patient_id"] == patient_id]

def get_aggregated_stats():
    # Mock data for Gov/NGO reports
    return {
        "total_patients": 150,
        "high_risk_alerts": 12,
        "readmission_reduction": "15%",
        "avg_rri_trend": [0.45, 0.42, 0.38, 0.35], # Last 4 months
    }