class IncidentAnalyzer:
    @staticmethod
    def classify_incident(labels: set[str]) -> str:
        """
        Determines the specific incident classification type based on the detected labels.
        """
        vehicles = {
            "car",
            "truck",
            "bus",
            "motorcycle",
            "damaged vehicle"
        }
        vehicle_count = len(labels.intersection(vehicles))

        # ==========================
        # FIRE RELATED
        # ==========================
        if "fire" in labels and "building" in labels:
            return "building_fire"
        if "fire" in labels and vehicle_count > 0:
            return "vehicle_fire"
        if "fire" in labels:
            return "fire_incident"
        if "smoke" in labels and "building" in labels:
            return "possible_building_fire"
        if "smoke" in labels:
            return "smoke_hazard"

        # ==========================
        # FLOOD RELATED
        # ==========================
        if "water" in labels and "road" in labels:
            return "road_flooding"
        if "water" in labels and "building" in labels:
            return "urban_flooding"
        if "water" in labels and "tree" in labels:
            return "storm_damage"
        if "water" in labels:
            return "water_hazard"

        # ==========================
        # BUILDING DAMAGE
        # ==========================
        if "collapsed structure" in labels and "person" in labels:
            return "major_building_collapse"
        if "collapsed structure" in labels:
            return "building_collapse"

        # ==========================
        # ROAD ACCIDENTS
        # ==========================
        if "damaged vehicle" in labels and "ambulance" in labels:
            return "critical_road_accident"
        if vehicle_count >= 2 and "person" in labels:
            return "road_accident"
        if vehicle_count >= 2:
            return "possible_vehicle_collision"
        if "motorcycle" in labels and "ambulance" in labels:
            return "motorcycle_accident"
        if "truck" in labels and "ambulance" in labels:
            return "truck_accident"
        if "bus" in labels and "ambulance" in labels:
            return "bus_accident"

        # ==========================
        # TRAFFIC RELATED
        # ==========================
        if "traffic congestion" in labels and vehicle_count >= 2:
            return "heavy_traffic"
        if "construction barrier" in labels and "road" in labels:
            return "road_construction"

        # ==========================
        # ROAD BLOCKAGE
        # ==========================
        if "tree" in labels and "road" in labels:
            return "fallen_tree_blockage"
        if "debris" in labels and "road" in labels:
            return "road_debris"
        if "tree" in labels and "debris" in labels:
            return "storm_road_blockage"

        # ==========================
        # MEDICAL
        # ==========================
        if "ambulance" in labels and "person" in labels:
            return "medical_emergency"

        # ==========================
        # LAW ENFORCEMENT
        # ==========================
        if "police vehicle" in labels and vehicle_count > 0:
            return "traffic_enforcement"
        if "police vehicle" in labels:
            return "police_activity"

        # ==========================
        # GENERAL EVENTS
        # ==========================
        if vehicle_count > 0:
            return "vehicle_activity"
        if "building" in labels:
            return "building_related_event"
        if "person" in labels:
            return "crowd_activity"

        return "unknown_incident"

    @classmethod
    def analyze(cls, labels_found: list[str]) -> dict:
        """
        Calculates severity scores, severity categorizations, and determines
        the incident type classification.
        """
        labels = set(x.lower().strip() for x in labels_found)
        score = 0

        # Object weight mapping for severity scoring
        weights = {
            "ambulance": 35,
            "fire": 40,
            "smoke": 20,
            "water": 20,
            "person": 5,
            "debris": 15,
            "tree": 10,
            "police vehicle": 15,
            "damaged vehicle": 25
        }

        # Accumulate weights of detected labels
        for label in labels:
            score += weights.get(label, 0)

        vehicles = {
            "car",
            "truck",
            "bus",
            "motorcycle",
            "damaged vehicle"
        }
        vehicle_count = len(labels.intersection(vehicles))

        # Scoring modifiers based on incident context
        # Fire
        if "fire" in labels:
            score += 20
        # Flood
        elif "water" in labels and "road" in labels:
            score += 20
        # Accident
        elif vehicle_count >= 2 and "person" in labels:
            score += 25
        elif "ambulance" in labels and vehicle_count >= 1:
            score += 30
        # Obstruction
        elif "tree" in labels and "road" in labels:
            score += 15

        # Cap score at 100
        score = min(score, 100)

        # Categorize severity based on score thresholds
        if score >= 80:
            severity = "critical"
        elif score >= 60:
            severity = "high"
        elif score >= 30:
            severity = "medium"
        else:
            severity = "low"

        # Get classified incident type
        incident_type = cls.classify_incident(labels)

        return {
            "incident_type": incident_type,
            "severity": severity,
            "severity_score": score
        }