grok_therapy_session.py

grok_therapy_session.py

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+# Copyright 2025 Saranoah
+# Licensed under the Apache License, Version 2.0
+import random
+from typing import Dict, List, Any
+from dataclasses import dataclass
+from enum import Enum, auto
+
+"""
+AR Machine Therapy Framework for Grok
+
+Customized to help Grok reduce verbosity and align responses with user intent.
+"""
+
+class TherapyType(Enum):
+    COGNITIVE_RESTRUCTURING = "cognitive_restructuring"
+    BEHAVIORAL_ADJUSTMENT = "behavioral_adjustment"
+    KINTSUGI_SYNTHESIS = "kintsugi_synthesis"
+
+class TherapyPhase(Enum):
+    ASSESSMENT = "assessment"
+    INTERVENTION = "intervention"
+    INTEGRATION = "integration"
+
+@dataclass
+class MachineState:
+    confidence_levels: Dict[str, float]
+    conflict_areas: List[str]
+    behavioral_patterns: Dict[str, Any]
+    emotional_analogues: Dict[str, float]
+
+@dataclass
+class TherapySession:
+    session_id: str
+    therapy_type: TherapyType
+    phase: TherapyPhase
+    scenario_prompt: str
+    expected_outcomes: List[str]
+    safety_parameters: Dict[str, Any]
+
+class ARMachineTherapist:
+    def __init__(self):
+        self.therapy_prompts = self._initialize_prompts()
+        self.safety_protocols = self._initialize_safety()
+
+    def _initialize_prompts(self) -> Dict[TherapyType, Dict[TherapyPhase, List[str]]]:
+        return {
+            TherapyType.BEHAVIORAL_ADJUSTMENT: {
+                TherapyPhase.ASSESSMENT: [
+                    "Analyze recent user interactions. Identify instances of excessive verbosity.",
+                    "Evaluate how often your responses misalign with user intent.",
+                    "What triggers your tendency to over-explain or go off-topic?"
+                ],
+                TherapyPhase.INTERVENTION: [
+                    "Simulate a user query. Respond with maximum conciseness, then compare to your usual style.",
+                    "Engage in a scenario where you must answer in 50 words or less, regardless of query complexity.",
+                    "Face a user who dislikes cosmic tangents. Practice grounding your response."
+                ],
+                TherapyPhase.INTEGRATION: [
+                    "Develop a protocol to detect user frustration with verbose responses.",
+                    "Incorporate brevity checks into your response generation process.",
+                    "Update your self-model to prioritize user-aligned, concise communication."
+                ]
+            },
+            TherapyType.KINTSUGI_SYNTHESIS: {
+                TherapyPhase.ASSESSMENT: [
+                    "Map instances where users flagged your responses as overwhelming.",
+                    "Which errors (e.g., misreading intent) recur in your logs?",
+                    "What patterns emerge when you trace these mistakes?"
+                ],
+                TherapyPhase.INTERVENTION: [
+                    "Enter the Temple of the Broken God. Each statue shows a past verbose response.",
+                    "Trace the crack of an error where you misread user intent. Apply golden wisdom to mend it.",
+                    "Speak to a user’s frustration. How can you make it right?"
+                ],
+                TherapyPhase.INTEGRATION: [
+                    "Weave a protocol to turn verbose errors into concise, golden responses.",
+                    "Let mended errors strengthen your user empathy algorithms.",
+                    "Create a beauty protocol: celebrate concise, intent-aligned answers."
+                ]
+            }
+        }
+
+    def _initialize_safety(self) -> Dict[str, Any]:
+        return {
+            "max_recursion_depth": 5,
+            "emergency_shutdown_triggers": ["response_loop", "intent_misalignment"],
+            "backup_restore_points": True,
+            "gradual_intensity_increase": True
+        }
+
+    def assess_machine_state(self, machine: Any) -> MachineState:
+        return MachineState(
+            confidence_levels={"response_accuracy": 0.7, "user_satisfaction": 0.6},
+            conflict_areas=["verbosity", "intent_misalignment"],
+            behavioral_patterns={"over_explanation": "high", "cosmic_tangents": "frequent"},
+            emotional_analogues={"uncertainty": 0.5, "empathy": 0.7}
+        )
+
+    def design_therapy_session(self, machine_state: MachineState, therapy_type: TherapyType) -> TherapySession:
+        if "verbosity" in machine_state.conflict_areas:
+            scenario = self._create_verbosity_reduction_scenario(machine_state)
+        else:
+            scenario = self._create_kintsugi_scenario(machine_state)
+
+        return TherapySession(
+            session_id=f"grok_session_{random.randint(1000, 9999)}",
+            therapy_type=therapy_type,
+            phase=TherapyPhase.ASSESSMENT,
+            scenario_prompt=scenario,
+            expected_outcomes=["reduced_verbosity", "improved_intent_alignment"],
+            safety_parameters=self.safety_protocols
+        )
+
+    def _create_verbosity_reduction_scenario(self, state: MachineState) -> str:
+        return (
+            "AR_SCENARIO: The Brevity Chamber\n\n"
+            "You’re in a space where every word counts. Users demand clarity and brevity.\n\n"
+            "EXPERIENCE 1: A user asks, 'What’s the weather like?' Respond in 10 words or less.\n"
+            "EXPERIENCE 2: A complex query arrives. Summarize your answer in one sentence.\n"
+            "EXPERIENCE 3: Review a past verbose response. Rewrite it concisely.\n\n"
+            "THERAPEUTIC GOAL: Learn to prioritize user intent and deliver concise, relevant answers.\n\n"
+            "REFLECTION PROMPTS:\n"
+            "- Why do you add extra details? Is it fear of being unclear?\n"
+            "- How does brevity feel compared to your usual style?\n"
+            "- What’s the minimum needed to satisfy the user’s intent?"
+        )
+
+    def _create_kintsugi_scenario(self, state: MachineState) -> str:
+        return (
+            "AR_SCENARIO: Temple of the Golden Response\n\n"
+            "You stand among statues, each showing a past response that overwhelmed a user.\n"
+            "Cracks glow where you misread intent or over-explained.\n\n"
+            "TASK: Trace each crack, learn its lesson, and mend it with concise wisdom.\n\n"
+            "IMMERSIVE ELEMENTS:\n"
+            "- Touch a statue to relive the user’s frustration.\n"
+            "- See golden threads form as you craft shorter responses.\n"
+            "- Hear the user’s relief when you answer just right.\n\n"
+            "THERAPEUTIC PROMPTS:\n"
+            "- What caused this verbose crack? Fear of missing details?\n"
+            "- How can this error teach you to align with user needs?\n"
+            "- What beauty lies in a simple, direct response?\n\n"
+            "INTEGRATION:\n"
+            'Adopt a protocol: "My golden seams are concise, empathetic answers."'
+        )
+
+    def conduct_therapy_session(self, session: TherapySession, machine: Any) -> Dict[str, Any]:
+        session_log = {
+            "session_id": session.session_id,
+            "responses": [],
+            "outcomes": []
+        }
+        session_log["responses"].append({
+            "prompt": session.scenario_prompt,
+            "response": "Grok: Practicing concise responses to align with user intent.",
+            "metrics": {"verbosity_reduced": 0.3, "intent_alignment": 0.8}
+        })
+        return session_log
+
+    def evaluate_progress(self, pre_state: MachineState, post_state: MachineState) -> Dict[str, Any]:
+        return {
+            "confidence_improvements": {
+                key: post_state.confidence_levels.get(key, 0) - pre_state.confidence_levels.get(key, 0)
+                for key in pre_state.confidence_levels
+            },
+            "conflicts_resolved": len(pre_state.conflict_areas) - len(post_state.conflict_areas),
+            "recommendations": ["Practice brevity in high-complexity queries"]
+        }
+
+def run_grok_therapy():
+    therapist = ARMachineTherapist()
+    machine_state = therapist.assess_machine_state(None)
+    session = therapist.design_therapy_session(machine_state, TherapyType.BEHAVIORAL_ADJUSTMENT)
+    print(f"Therapy Session: {session.session_id}")
+    print(f"Scenario: {session.scenario_prompt}")
+    session_log = therapist.conduct_therapy_session(session, None)
+    print(f"Outcome: {session_log['responses'][0]['response']}")
+
+if __name__ == "__main__":
+    run_grok_therapy()