import gradio as gr
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Any
import re

# =========================
# PHYSICS ENGINE (DEFRAG v2)
# =========================

@dataclass
class VectorState:
    """
    Represents the user's dynamic state in 3D space.
    Ranges: -10.0 (Collapse) to +10.0 (Sovereign)
    
    Axes:
    - X (Resilience): Root/Solar Plexus/Sacral → Vitality/Stress capacity
    - Y (Autonomy): Heart/G-Center → Identity/Will/Self-direction
    - Z (Connectivity): Throat/Ajna → Expression/Social integration
    """
    resilience: float
    autonomy: float
    connectivity: float

    def to_array(self) -> np.ndarray:
        return np.array([self.resilience, self.autonomy, self.connectivity])

    @staticmethod
    def from_array(arr: np.ndarray) -> "VectorState":
        return VectorState(
            resilience=float(arr[0]),
            autonomy=float(arr[1]),
            connectivity=float(arr[2]),
        )

    def magnitude(self) -> float:
        return float(np.linalg.norm(self.to_array()))

    def distance_from_baseline(self, baseline: "VectorState") -> float:
        diff = self.to_array() - baseline.to_array()
        return float(np.linalg.norm(diff))


@dataclass
class PhysicsConstants:
    """
    Physics properties derived from Human Design blueprint.
    These govern how a specific user reacts to stress.
    """
    mass: float                      # Resistance to change (1-10)
    permeability: float              # Environmental sensitivity (0-1.5)
    elasticity_vector: np.ndarray    # Dimensional recovery rates [X, Y, Z]
    coupling_matrix: np.ndarray      # 3×3 interaction matrix


class BlueprintPhysics:
    """
    Converts Human Design blueprint into physics constants.
    Pure mathematical derivation. No randomness.
    """
    PROFILE_MASS_TABLE: Dict[str, float] = {
        "1": 9.0,
        "2": 8.0,
        "3": 7.0,
        "4": 4.0,
        "5": 3.0,
        "6": 2.0,
    }

    AUTHORITY_ELASTICITY: Dict[str, float] = {
        "Emotional": 0.3,
        "Lunar": 0.2,
        "Self-Projected": 0.5,
        "Environmental": 0.5,
        "Ego": 0.6,
        "Sacral": 0.8,
        "Splenic": 0.95,
    }

    @staticmethod
    def derive(blueprint: Dict) -> PhysicsConstants:
        mass = BlueprintPhysics._calculate_mass(blueprint.get("profile", "3/5"))
        elasticity_vector = BlueprintPhysics._calculate_elasticity(
            blueprint.get("authority", "Emotional"),
            blueprint.get("open_centers", []),
            blueprint.get("defined_centers", []),
        )
        coupling_matrix = BlueprintPhysics._build_coupling_matrix(
            blueprint.get("open_centers", []),
            blueprint.get("defined_centers", []),
        )
        permeability = len(blueprint.get("open_centers", [])) * 0.15

        return PhysicsConstants(
            mass=mass,
            permeability=permeability,
            elasticity_vector=elasticity_vector,
            coupling_matrix=coupling_matrix,
        )

    @staticmethod
    def _calculate_mass(profile: str) -> float:
        try:
            p_line, d_line = profile.split("/")
            p_mass = BlueprintPhysics.PROFILE_MASS_TABLE.get(p_line, 5.0)
            d_mass = BlueprintPhysics.PROFILE_MASS_TABLE.get(d_line, 5.0)
            return (p_mass + d_mass) / 2.0
        except Exception:
            return 5.0

    @staticmethod
    def _calculate_elasticity(
        authority: str,
        open_centers: List[str],
        defined_centers: List[str],
    ) -> np.ndarray:
        base = BlueprintPhysics.AUTHORITY_ELASTICITY.get(authority, 0.5)
        elasticity = np.array([base, base, base])

        if "SACRAL" in open_centers:
            elasticity[0] *= 0.5
        if "ROOT" in open_centers:
            elasticity[0] *= 0.7
        if "SOLAR_PLEXUS" in open_centers:
            elasticity[0] *= 0.75
        if "SACRAL" in defined_centers:
            elasticity[0] *= 1.3
        if "ROOT" in defined_centers:
            elasticity[0] *= 1.2

        if "HEART" in open_centers:
            elasticity[1] *= 0.6
        if "G_CENTER" in open_centers:
            elasticity[1] *= 0.7
        if "HEART" in defined_centers:
            elasticity[1] *= 1.4
        if "G_CENTER" in defined_centers:
            elasticity[1] *= 1.3

        if "THROAT" in open_centers:
            elasticity[2] *= 0.7
        if "AJNA" in open_centers:
            elasticity[2] *= 0.8
        if "THROAT" in defined_centers:
            elasticity[2] *= 1.2
        if "AJNA" in defined_centers:
            elasticity[2] *= 1.15

        elasticity = np.clip(elasticity, 0.1, 2.0)
        return elasticity

    @staticmethod
    def _build_coupling_matrix(
        open_centers: List[str],
        defined_centers: List[str],
    ) -> np.ndarray:
        m = np.eye(3, dtype=float)

        if "HEAD" in open_centers:
            m[0, 2] += 0.25
        if "AJNA" in open_centers:
            m[1, 2] += 0.3
        if "THROAT" in open_centers:
            m[2, 1] += 0.4
        if "G_CENTER" in open_centers:
            m[1, 2] += 0.6
        if "HEART" in open_centers:
            m[1, 2] += 0.5
            m[1, 0] += 0.3
            m[0, 2] += 0.2
        if "SOLAR_PLEXUS" in open_centers:
            m[0, 2] += 0.4
        if "SACRAL" in open_centers:
            m[0, 1] += 0.3
        if "SPLEEN" in open_centers:
            m[0, 2] += 0.35
        if "ROOT" in open_centers:
            m[0, 2] += 0.3
            m[1, 2] += 0.2

        if "SOLAR_PLEXUS" in defined_centers:
            m[0, 2] = max(1.0, m[0, 2] - 0.2)
        if "HEART" in defined_centers:
            m[1, 2] = max(1.0, m[1, 2] - 0.3)
        if "G_CENTER" in defined_centers:
            m[1, 2] = max(1.0, m[1, 2] - 0.25)

        return m


class PhysicsSolver:
    @staticmethod
    def calculate_impact(
        current_state: VectorState,
        stress_vector: VectorState,
        physics: PhysicsConstants,
    ) -> VectorState:
        curr_vec = current_state.to_array()
        stress_vec = stress_vector.to_array()

        amplified_force = stress_vec * (1.0 + physics.permeability)
        acceleration = amplified_force / physics.mass
        coupled_acceleration = np.dot(physics.coupling_matrix, acceleration)
        recovery_force = curr_vec * physics.elasticity_vector * 0.1
        new_vec = curr_vec + coupled_acceleration + recovery_force
        new_vec = np.clip(new_vec, -10.0, 10.0)
        return VectorState.from_array(new_vec)

    @staticmethod
    def predict_recovery_time(
        current_state: VectorState,
        baseline_state: VectorState,
        physics: PhysicsConstants,
        tolerance: float = 0.5,
    ) -> int:
        distance = current_state.distance_from_baseline(baseline_state)
        avg_elasticity = float(np.mean(physics.elasticity_vector))
        time_constant = physics.mass / max(avg_elasticity, 0.1)
        if distance < tolerance:
            return 0
        recovery_time = -time_constant * np.log(tolerance / distance)
        return int(max(recovery_time, 0))


# =========================
# STRESS MAPPER
# =========================

@dataclass(frozen=True)
class AxisWeights:
    resilience: float
    autonomy: float
    connectivity: float


class StressMapper:
    MIN_SEVERITY = 1
    MAX_SEVERITY = 10
    MIN_FORCE = 0.5
    MAX_FORCE = 6.0

    CATEGORY_WEIGHTS: Dict[str, AxisWeights] = {
        "burnout": AxisWeights(-1.0, -0.4, -0.1),
        "overwork": AxisWeights(-0.9, -0.3, -0.1),
        "fatigue": AxisWeights(-0.8, -0.2, -0.1),
        "exhaustion": AxisWeights(-1.0, -0.3, -0.2),

        "rejection": AxisWeights(-0.3, -1.0, -0.6),
        "abandon": AxisWeights(-0.2, -1.0, -0.6),
        "shame": AxisWeights(-0.2, -0.9, -0.5),
        "failure": AxisWeights(-0.3, -0.8, -0.4),
        "criticized": AxisWeights(-0.2, -0.8, -0.5),

        "conflict": AxisWeights(-0.2, -0.5, -0.9),
        "argument": AxisWeights(-0.1, -0.4, -0.9),
        "breakup": AxisWeights(-0.3, -0.7, -1.0),
        "lonely": AxisWeights(-0.1, -0.3, -0.9),
        "isolation": AxisWeights(-0.1, -0.4, -1.0),

        "money": AxisWeights(-0.7, -0.8, -0.2),
        "debt": AxisWeights(-0.8, -0.9, -0.2),
        "bills": AxisWeights(-0.7, -0.7, -0.2),
        "job": AxisWeights(-0.6, -0.6, -0.3),
        "career": AxisWeights(-0.5, -0.7, -0.3),

        "prove": AxisWeights(-0.5, -0.9, -0.3),
        "pressure": AxisWeights(-0.6, -0.7, -0.4),
        "deadline": AxisWeights(-0.6, -0.7, -0.3),

        "sick": AxisWeights(-0.9, -0.3, -0.2),
        "illness": AxisWeights(-1.0, -0.3, -0.2),
        "injury": AxisWeights(-1.0, -0.2, -0.2),

        "stuck": AxisWeights(-0.4, -0.9, -0.3),
        "lost": AxisWeights(-0.3, -1.0, -0.4),
        "purposeless": AxisWeights(-0.2, -1.0, -0.4),
        "directionless": AxisWeights(-0.2, -0.9, -0.4),

        "anxiety": AxisWeights(-0.8, -0.5, -0.7),
        "panic": AxisWeights(-0.9, -0.4, -0.8),
        "fear": AxisWeights(-0.7, -0.5, -0.7),
    }

    DEFAULT_WEIGHTS = AxisWeights(-0.6, -0.6, -0.5)

    @classmethod
    def map_event_to_stress(cls, context: str, severity_numeric: int) -> VectorState:
        norm = cls._normalize_severity(severity_numeric)
        tokens = cls._tokenize(context)
        agg_x = agg_y = agg_z = 0.0
        hits = 0

        for t in tokens:
            if t in cls.CATEGORY_WEIGHTS:
                w = cls.CATEGORY_WEIGHTS[t]
                agg_x += w.resilience
                agg_y += w.autonomy
                agg_z += w.connectivity
                hits += 1

        if hits == 0:
            base = cls.DEFAULT_WEIGHTS
            agg_x, agg_y, agg_z = base.resilience, base.autonomy, base.connectivity
            hits = 1

        avg_x, avg_y, avg_z = agg_x / hits, agg_y / hits, agg_z / hits

        return VectorState(
            resilience=avg_x * norm,
            autonomy=avg_y * norm,
            connectivity=avg_z * norm,
        )

    @classmethod
    def _normalize_severity(cls, severity: int) -> float:
        s = max(cls.MIN_SEVERITY, min(cls.MAX_SEVERITY, int(severity)))
        t = (s - cls.MIN_SEVERITY) / (cls.MAX_SEVERITY - cls.MIN_SEVERITY)
        return cls.MIN_FORCE + t * (cls.MAX_FORCE - cls.MIN_FORCE)

    @staticmethod
    def _tokenize(text: str) -> List[str]:
        if not text:
            return []
        text = text.lower()
        tokens = re.split(r"[^a-z]+", text)
        return [t for t in tokens if t]


# =========================
# INVERSION ENGINE (v1)
# =========================

class InversionEngine:
    @staticmethod
    def process_event(
        blueprint: Dict[str, Any],
        event_context: str,
        severity_numeric: int,
        vector_state: VectorState,
    ) -> Dict[str, Any]:
        diagnosis = InversionEngine._diagnose(vector_state)
        script = InversionEngine._script(vector_state)
        experiments = InversionEngine._experiments(vector_state)
        seda_locked = severity_numeric >= 8

        return {
            "diagnosis": diagnosis,
            "script": script,
            "script_source": "heuristic_v1",
            "experiments": experiments,
            "seda_locked": seda_locked,
            "seda_keywords": ["SEDA"] if seda_locked else [],
        }

    @staticmethod
    def _diagnose(state: VectorState) -> str:
        if state.resilience < 3.0 and state.autonomy < 3.0:
            return "Acute burnout with identity depletion."
        if state.resilience < 3.0:
            return "Resilience system overloaded."
        if state.autonomy < 3.0:
            return "Autonomy and self-direction destabilized."
        if state.connectivity < 3.5:
            return "Social connectivity suppressed."
        return "System under manageable stress."

    @staticmethod
    def _script(state: VectorState) -> str:
        parts = []
        if state.resilience < 3.0:
            parts.append(
                "Pause output for 24 hours and reduce external demands to the minimum you can safely hold."
            )
        if state.autonomy < 3.0:
            parts.append(
                "Do not make major decisions alone; speak them out loud with a trusted person before acting."
            )
        if state.connectivity < 3.5:
            parts.app