import random

def simulate_journey(claim_type, complexity, adjuster_experience, satisfaction_target):
    """
    Simulate claims journey with timeline, decision points, and projections
    
    Args:
        claim_type: Type of claim (Auto, Property, Liability, Workers Comp, Health)
        complexity: Complexity level (Simple, Moderate, Complex, Very Complex)
        adjuster_experience: Years of adjuster experience (0-20)
        satisfaction_target: Target customer satisfaction percentage (0-100)
    
    Returns:
        dict with timeline, decision_points, estimated_duration, cost_projection, risk_factors
    """
    
    # Base timeline by claim type
    base_timelines = {
        "Auto": [
            {"day": 0, "event": "FNOL received"},
            {"day": 1, "event": "Adjuster assigned"},
            {"day": 3, "event": "Vehicle inspection completed"},
            {"day": 5, "event": "Estimate approved"},
            {"day": 12, "event": "Repairs completed"},
            {"day": 13, "event": "Final inspection"},
            {"day": 14, "event": "Claim closed"}
        ],
        "Property": [
            {"day": 0, "event": "FNOL received"},
            {"day": 1, "event": "Adjuster assigned"},
            {"day": 2, "event": "Property inspection scheduled"},
            {"day": 5, "event": "Damage assessment completed"},
            {"day": 7, "event": "Estimate approved"},
            {"day": 21, "event": "Repairs completed"},
            {"day": 22, "event": "Final walkthrough"},
            {"day": 23, "event": "Claim closed"}
        ],
        "Liability": [
            {"day": 0, "event": "FNOL received"},
            {"day": 1, "event": "Adjuster assigned"},
            {"day": 3, "event": "Investigation initiated"},
            {"day": 7, "event": "Witness statements collected"},
            {"day": 14, "event": "Liability determination"},
            {"day": 21, "event": "Settlement negotiation"},
            {"day": 28, "event": "Settlement agreement"},
            {"day": 30, "event": "Claim closed"}
        ],
        "Workers Comp": [
            {"day": 0, "event": "FNOL received"},
            {"day": 1, "event": "Case manager assigned"},
            {"day": 2, "event": "Medical treatment authorized"},
            {"day": 7, "event": "Medical evaluation completed"},
            {"day": 14, "event": "Return to work assessment"},
            {"day": 30, "event": "Ongoing treatment review"},
            {"day": 45, "event": "Claim closed or continued"}
        ],
        "Health": [
            {"day": 0, "event": "Claim received"},
            {"day": 1, "event": "Initial review"},
            {"day": 3, "event": "Medical necessity verification"},
            {"day": 5, "event": "Claim processed"},
            {"day": 7, "event": "Payment issued"},
            {"day": 10, "event": "Claim closed"}
        ]
    }
    
    timeline = base_timelines.get(claim_type, base_timelines["Auto"]).copy()
    
    # Adjust timeline based on complexity
    complexity_multipliers = {
        "Simple": 0.7,
        "Moderate": 1.0,
        "Complex": 1.5,
        "Very Complex": 2.0
    }
    multiplier = complexity_multipliers.get(complexity, 1.0)
    
    # Adjust timeline days
    for event in timeline:
        event["day"] = int(event["day"] * multiplier)
    
    # Adjust for adjuster experience (more experience = faster)
    if adjuster_experience > 10:
        experience_factor = 0.9
    elif adjuster_experience > 5:
        experience_factor = 1.0
    else:
        experience_factor = 1.1
    
    for event in timeline:
        event["day"] = int(event["day"] * experience_factor)
    
    # Adjust for satisfaction target (higher target = more time for communication)
    if satisfaction_target > 90:
        satisfaction_factor = 1.1
    elif satisfaction_target > 80:
        satisfaction_factor = 1.05
    else:
        satisfaction_factor = 1.0
    
    for event in timeline:
        event["day"] = int(event["day"] * satisfaction_factor)
    
    # Calculate estimated duration
    estimated_duration = max([event["day"] for event in timeline])
    
    # Generate decision points
    decision_points = []
    
    if claim_type == "Auto":
        decision_points = [
            "Coverage verification",
            "Liability determination",
            "Estimate approval",
            "Repair shop selection"
        ]
    elif claim_type == "Property":
        decision_points = [
            "Coverage verification",
            "Damage scope determination",
            "Contractor selection",
            "Additional living expenses approval"
        ]
    elif claim_type == "Liability":
        decision_points = [
            "Liability assessment",
            "Coverage limits review",
            "Settlement authority",
            "Legal counsel involvement"
        ]
    elif claim_type == "Workers Comp":
        decision_points = [
            "Compensability determination",
            "Medical treatment authorization",
            "Disability rating",
            "Return to work planning"
        ]
    else:  # Health
        decision_points = [
            "Medical necessity review",
            "Coverage verification",
            "Payment calculation",
            "Appeal handling if needed"
        ]
    
    # Add complexity-based decision points
    if complexity in ["Complex", "Very Complex"]:
        decision_points.append("Senior adjuster review")
        decision_points.append("Special investigation unit referral")
    
    # Calculate cost projection
    base_costs = {
        "Auto": 8500,
        "Property": 15000,
        "Liability": 25000,
        "Workers Comp": 12000,
        "Health": 3500
    }
    
    base_cost = base_costs.get(claim_type, 10000)
    cost_projection = base_cost * multiplier
    
    # Adjust for experience (more experience = better cost control)
    if adjuster_experience > 10:
        cost_projection *= 0.95
    elif adjuster_experience < 3:
        cost_projection *= 1.1
    
    # Generate risk factors
    risk_factors = []
    
    if complexity in ["Complex", "Very Complex"]:
        risk_factors.append("High complexity may lead to delays")
    
    if adjuster_experience < 3:
        risk_factors.append("Junior adjuster may require supervision")
    
    if satisfaction_target > 90:
        risk_factors.append("High satisfaction target requires extra communication")
    
    if claim_type == "Liability":
        risk_factors.append("Potential for litigation")
    
    if estimated_duration > 30:
        risk_factors.append("Extended duration may impact customer satisfaction")
    
    if not risk_factors:
        risk_factors.append("No significant risk factors identified")
    
    return {
        "timeline": timeline,
        "decision_points": decision_points,
        "estimated_duration": estimated_duration,
        "cost_projection": cost_projection,
        "risk_factors": risk_factors
    }