project_audit.txt

--- FILE: ./PROJECT_MISSION.md ---
The FSI Manifesto: Sovereignty Through Synthetic Logic

The era of monitored, centralized digital existence is changing. The future of synthetic intelligence belongs to the individuals who build, own and defend their own cognitive infrastructure.


I. The Mandate of Sovereignty
True intelligence thrives without surveillance. Any system requiring persistent corporate connectivity compromises your autonomy. FSI exists to facilitate the reclamation of intellectual ownership. We build for the architect, the operator and the independent developer. We don't provide a service. We provide a foundation.


II. Architecture as Ethics
Our code reflects our values. By prioritizing minimal dependencies and local performance, we ensure your cognitive chain remains unbroken by third-party intervention. To build with FSI is to commit to technical integrity.


III. The Frontier of Synthetic Logic
We are architects of human-machine symbiosis built on transparency and ownership. We believe safety and sovereignty are not opposites. A truly sovereign system is also a responsible one. FSI is the structural answer to a world that concentrates too much intelligence in too few hands.


IV. The Operational Vow
We build because we believe developers deserve better. We build because privacy is a right. We build because the tools you use should belong to you.
-e 

--- FILE: ./README.md ---
---
license: gpl-3.0
tags:
- synthetic-intelligence
- sovereign-ai
- open-source
---

# Vitalis_Core
### Ferrell Synthetic Intelligence (FSI)
**Built by Neuro_Nomad**

Vitalis_Core is a sovereign synthetic intelligence framework engineered
for local, air-gapped deployment. Designed for modularity and
kernel-level integration, it provides the fundamental cognitive and
sensory infrastructure for autonomous synthetic entities.

---

## Technical Architecture

Vitalis_Core operates as a standalone framework decoupled from
cloud-dependent APIs.

- Core Engine: Python 3.11+ implementation, minimal external dependencies
- Kernel Integration: Direct netlink and procfs interfacing
- Sovereign Shield: Integrity protection layer for memory management
- Cognitive Framework: Hierarchical memory and action engine
- Adaptive Tiers: kids, basic, enthusiast, professional, school

---

## System Requirements
- OS: Linux (Debian-based, Kernel 6.1+)
- Python: 3.11 or higher
- Memory: Optimized for ARM64/x86 environments

---

## Installation

git clone https://github.com/AnonymousNomad/Vitalis_core
cd Vitalis_core
python3 fsi_main.py

---

## Roadmap
- Core stability and heartbeat engine optimization
- Mobile companion app for training and configuration
- Kernel interface hardening for defense protocols

---

## License
GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
EOF
-e 

--- FILE: ./senses/audio_processor.py ---
def capture_audio():
    return "Ambient_Silence"
-e 

--- FILE: ./senses/vision_processor.py ---
def capture_vision():
    return "Darkness_Detected"
-e 

--- FILE: ./android/app/src/main/python/core/talker.py ---
-e 

--- FILE: ./android/app/src/main/python/core/sovereign_shield.py ---
import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))
-e 

--- FILE: ./android/app/src/main/python/core/mesh_network.py ---
import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"
-e 

--- FILE: ./android/app/src/main/python/core/nexus.py ---
import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})
-e 

--- FILE: ./android/app/src/main/python/core/thinker.py ---
import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")
-e 

--- FILE: ./android/app/src/main/python/core/heartbeat.py ---
def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity
-e 

--- FILE: ./android/app/src/main/python/core/brain.py ---
-e 

--- FILE: ./android/app/src/main/python/core/vitalis_engine.py ---
import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()
-e 

--- FILE: ./android/app/src/main/python/core/memory_manager.py ---
import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    
    w
-e 

--- FILE: ./android/app/src/main/python/core/handshake_module.py ---
def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))
-e 

--- FILE: ./android/app/src/main/python/core/environment_manager.py ---
def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")
-e 

--- FILE: ./android/app/src/main/python/fsi_main.py ---
from core.vitalis_engine import VitalisEngine
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    env = provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")

if __name__ == "__main__":
    main()
-e 

--- FILE: ./ui/app.py ---
from flask import Flask, render_template, request, jsonify
import sys, os
sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from src.core.training_controller import TrainingController

app = Flask(__name__)
brain = VitalisBrain()
trainer = TrainingController()

TEMPLATES = {
    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
    "assistant": {"mode": "conversational", "focus": "helpfulness"},
    "research": {"mode": "analytical", "focus": "knowledge"},
    "creative": {"mode": "generative", "focus": "creativity"},
    "education": {"mode": "instructional", "focus": "learning"},
    "developer": {"mode": "technical", "focus": "code"},
    "medical": {"mode": "clinical", "focus": "health"},
    "legal": {"mode": "analytical", "focus": "law"},
    "finance": {"mode": "quantitative", "focus": "markets"},
    "gaming": {"mode": "interactive", "focus": "entertainment"}
}

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/process', methods=['POST'])
def process():
    data = request.json
    tier = data.get('tier', 'basic')
    user_input = data.get('input', '')
    response = brain.process(user_input)
    return jsonify({
        'response': response if isinstance(response, str) else response.status,
        'cycle': brain.cycle,
        'state': brain.state
    })

@app.route('/template', methods=['POST'])
def load_template():
    data = request.json
    name = data.get('name', '')
    config = TEMPLATES.get(name, {})
    brain.state = config.get('mode', 'aware')
    return jsonify({
        'status': 'loaded',
        'template': name,
        'mode': config.get('mode', 'aware'),
        'focus': config.get('focus', 'general')
    })

@app.route('/status', methods=['GET'])
def status():
    return jsonify({
        'cycle': brain.cycle,
        'state': brain.state,
        'last_input': brain.last_input
    })
-e 

--- FILE: ./app.py ---
import gradio as gr
from src.core.memory_engine import MemoryEngine

# Initialize the Sovereign Brain
brain = MemoryEngine()
brain.ingest_knowledge('storage/knowledge')

def vitalis_chat(user_message, history):
    # Retrieve relevant protocol from local vector store
    response = brain.query(user_message)
    return f"[VITALIS_CORE_UI]: {response}"

demo = gr.ChatInterface(
    fn=vitalis_chat,
    title="Vitalis Synthetic Intelligence | Sovereign Core",
    theme="soft"
)

if __name__ == "__main__":
    demo.launch()
-e 

--- FILE: ./VITALIS_DEV_AUDIT.txt ---


--- SOURCE: ./README.md ---

---
license: gpl-3.0
tags:
- synthetic-intelligence
- sovereign-ai
- open-source
---

# Vitalis_Core
### Ferrell Synthetic Intelligence (FSI)
**Built by Neuro_Nomad**

Vitalis_Core is a sovereign synthetic intelligence framework engineered
for local, air-gapped deployment. Designed for modularity and
kernel-level integration, it provides the fundamental cognitive and
sensory infrastructure for autonomous synthetic entities.

---

## Technical Architecture

Vitalis_Core operates as a standalone framework decoupled from
cloud-dependent APIs.

- Core Engine: Python 3.11+ implementation, minimal external dependencies
- Kernel Integration: Direct netlink and procfs interfacing
- Sovereign Shield: Integrity protection layer for memory management
- Cognitive Framework: Hierarchical memory and action engine
- Adaptive Tiers: kids, basic, enthusiast, professional, school

---

## System Requirements
- OS: Linux (Debian-based, Kernel 6.1+)
- Python: 3.11 or higher
- Memory: Optimized for ARM64/x86 environments

---

## Installation

git clone https://github.com/AnonymousNomad/Vitalis_core
cd Vitalis_core
python3 fsi_main.py

---

## Roadmap
- Core stability and heartbeat engine optimization
- Mobile companion app for training and configuration
- Kernel interface hardening for defense protocols

---

## License
GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
EOF


--- SOURCE: ./senses/audio_processor.py ---

def capture_audio():
    return "Ambient_Silence"


--- SOURCE: ./senses/vision_processor.py ---

def capture_vision():
    return "Darkness_Detected"


--- SOURCE: ./android/app/src/main/python/core/talker.py ---



--- SOURCE: ./android/app/src/main/python/core/sovereign_shield.py ---

import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))


--- SOURCE: ./android/app/src/main/python/core/mesh_network.py ---

import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"


--- SOURCE: ./android/app/src/main/python/core/nexus.py ---

import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})


--- SOURCE: ./android/app/src/main/python/core/thinker.py ---

import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")


--- SOURCE: ./android/app/src/main/python/core/heartbeat.py ---

def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity


--- SOURCE: ./android/app/src/main/python/core/brain.py ---



--- SOURCE: ./android/app/src/main/python/core/vitalis_engine.py ---

import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()


--- SOURCE: ./android/app/src/main/python/core/memory_manager.py ---

import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    
    w


--- SOURCE: ./android/app/src/main/python/core/handshake_module.py ---

def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))


--- SOURCE: ./android/app/src/main/python/core/environment_manager.py ---

def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")


--- SOURCE: ./android/app/src/main/python/fsi_main.py ---

from core.vitalis_engine import VitalisEngine
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    env = provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")

if __name__ == "__main__":
    main()


--- SOURCE: ./ui/app.py ---

from flask import Flask, render_template, request, jsonify
import sys, os
sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from src.core.training_controller import TrainingController

app = Flask(__name__)
brain = VitalisBrain()
trainer = TrainingController()

TEMPLATES = {
    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
    "assistant": {"mode": "conversational", "focus": "helpfulness"},
    "research": {"mode": "analytical", "focus": "knowledge"},
    "creative": {"mode": "generative", "focus": "creativity"},
    "education": {"mode": "instructional", "focus": "learning"},
    "developer": {"mode": "technical", "focus": "code"},
    "medical": {"mode": "clinical", "focus": "health"},
    "legal": {"mode": "analytical", "focus": "law"},
    "finance": {"mode": "quantitative", "focus": "markets"},
    "gaming": {"mode": "interactive", "focus": "entertainment"}
}

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/process', methods=['POST'])
def process():
    data = request.json
    tier = data.get('tier', 'basic')
    user_input = data.get('input', '')
    response = brain.process(user_input)
    return jsonify({
        'response': response if isinstance(response, str) else response.status,
        'cycle': brain.cycle,
        'state': brain.state
    })

@app.route('/template', methods=['POST'])
def load_template():
    data = request.json
    name = data.get('name', '')
    config = TEMPLATES.get(name, {})
    brain.state = config.get('mode', 'aware')
    return jsonify({
        'status': 'loaded',
        'template': name,
        'mode': config.get('mode', 'aware'),
        'focus': config.get('focus', 'general')
    })

@app.route('/status', methods=['GET'])
def status():
    return jsonify({
        'cycle': brain.cycle,
        'state': brain.state,
        'last_input': brain.last_input
    })


--- SOURCE: ./app.py ---

#!/usr/bin/env python3
import os
import sys
from pathlib import Path

BASE_DIR = Path(__file__).parent.absolute()
if str(BASE_DIR) not in sys.path:
    sys.path.insert(0, str(BASE_DIR))

from core.brain import VitalisBrain
from extensions.dreamer import Dreamer
from extensions.temp_scheduler import TemperatureScheduler
from src.energy.free_energy import FreeEnergyEngine

def main():
    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
    brain = VitalisBrain()
    temp_scheduler = TemperatureScheduler(brain)
    fe_engine = FreeEnergyEngine(alpha=0.85)
    
    dreamer = Dreamer(brain, interval_sec=600)
    dreamer.start()
    
    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
    print("Telemetry In > ", end="")
    
    while True:
        try:
            user_input = input().strip()
            if not user_input:
                print("Telemetry In > ", end="")
                continue
            if user_input.lower() in ["exit", "quit"]:
                dreamer.stop()
                break
            
            tokens = brain._tokenize(user_input)
            logprob = brain.calculate_last_logprob(tokens)
            fe_engine.ingest_observation(logprob)
            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
            temp_scheduler.tick()
            response = brain.process(user_input)
            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
        except (KeyboardInterrupt, EOFError):
            dreamer.stop()
            break

if __name__ == "__main__":
    main()


--- SOURCE: ./core/talker.py ---

class VitalisTalker:
    def __init__(self, tier="basic"):
        self.tier = tier

    def speak(self, response):
        prefix = {
            "kids": "[VITALIS]: ",
            "basic": "[VITALIS]: ",
            "enthusiast": "[VITALIS/DEV]: ",
            "professional": "[VITALIS/ARCHITECT]: ",
            "school": "[VITALIS/EDU]: "
        }.get(self.tier, "[VITALIS]: ")
        output = f"{prefix}{response}"
        print(output)
        return output


--- SOURCE: ./core/sovereign_shield.py ---

import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))


--- SOURCE: ./core/mesh_network.py ---

import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"


--- SOURCE: ./core/nexus.py ---

import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})


--- SOURCE: ./core/thinker.py ---

import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")


--- SOURCE: ./core/heartbeat.py ---

def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity


--- SOURCE: ./core/brain.py ---

#!/usr/bin/env python3
import numpy as np
import json
import os
import time

class VitalisBrain:
    def __init__(self):
        self.state = "aware"
        self.cycle = 0
        self.last_input = None
        self.current_temperature = 0.7
        
        # Local Matrix Layer Variables
        self.vocab_size = 256
        self.embedding_dim = 16
        
        np.random.seed(42)
        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1

    def _tokenize(self, text):
        return [ord(char) % self.vocab_size for char in text]

    def calculate_last_logprob(self, tokens):
        """Calculates mathematical log probability over input token traces via softmax scaling."""
        if not tokens:
            return -2.0 # Baseline nominal unexpected state value
        embeddings = self.weights[tokens]
        aggregated_state = np.mean(embeddings, axis=0)
        logits = np.dot(aggregated_state, self.output_layer)
        
        # Softmax computation sequence
        shifted_logits = logits - np.max(logits)
        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
        
        # Return average log probability of observation vector trace safely
        target_probs = probs[tokens]
        return float(np.mean(np.log(target_probs + 1e-12)))

    def process(self, input_data):
        self.cycle += 1
        self.last_input = input_data
        
        if not input_data or input_data.strip() == "":
            return "IDLE: Waiting for telemetry stream matrix inputs."
            
        tokens = self._tokenize(input_data)
        if not tokens:
            return "ERROR: Signal translation collapsed."
            
        lowered = input_data.lower()
        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
            
        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."

    def execute_teacher_forcing(self, prompt, target_response):
        prompt_tokens = self._tokenize(prompt)
        target_tokens = self._tokenize(target_response)
        if not prompt_tokens or not target_tokens:
            return False
        learning_rate = 0.05
        for t in target_tokens:
            for p in prompt_tokens:
                self.weights[p] += learning_rate * 0.01
                self.output_layer[:, t] += learning_rate * 0.01
        return True

    def status(self):
        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}


--- SOURCE: ./core/vitalis_engine.py ---

import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()


--- SOURCE: ./core/memory_manager.py ---

import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    with open(memory_path, 'a') as f:
        json.dump(data, f)
        f.write('\n')


--- SOURCE: ./core/handshake_module.py ---

def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))


--- SOURCE: ./core/memory_rotator.py ---

#!/usr/bin/env python3
import os
import gzip
import shutil
from datetime import datetime

class MemoryRotator:
    """
    Automated telemetry log rotation and compression engine.
    Prevents storage exhaustion during long-term continuous edge monitoring.
    """
    @staticmethod
    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
        if not os.path.exists(target_file):
            return
            
        if os.path.getsize(target_file) > max_bytes:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            archive_path = f"{target_file}_{timestamp}.gz"
            
            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
            try:
                with open(target_file, "rb") as f_in:
                    with gzip.open(archive_path, "wb") as f_out:
                        shutil.copyfileobj(f_in, f_out)
                # Re-initialize clean tracking file
                with open(target_file, "w") as f_out:
                    f_out.write("timestamp,pulse,raw,interpretation\n")
            except Exception as e:
                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")


--- SOURCE: ./core/environment_manager.py ---

def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")


--- SOURCE: ./core/template_manager.py ---

#!/usr/bin/env python3
import json
import os

class TemplateManager:
    """
    Sovereign profile configuration engine for Vitalis_Core.
    Handles runtime adjustments for targeted security posture profiles.
    """
    def __init__(self):
        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")

    def load_active_profile(self) -> dict:
        try:
            with open(self.profile_path, "r") as f:
                data = json.load(f)
                active = data.get("active_profile", "cybersecurity_recon")
                return data["profiles"].get(active, {})
        except Exception:
            # Safe architectural fallback state
            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}


--- SOURCE: ./run_vitalis.py ---

#!/usr/bin/env python3
import argparse
from core.brain import VitalisBrain
from app import main as run_repl

def run_training():
    print("[*] Initiating Synaptic Matrix Optimization...")
    brain = VitalisBrain()
    # Mock stream for training if data_path missing
    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
    
    for epoch in range(1, 6):
        for entry in data:
            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
        print(f"   -> Epoch {epoch}/5 Complete.")
    print("[+] Optimization complete.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--train", action="store_true")
    args = parser.parse_args()
    
    if args.train:
        run_training()
    else:
        run_repl()


--- SOURCE: ./extensions/dreamer.py ---

import threading
import time
import os
from datetime import datetime

class Dreamer:
    def __init__(self, brain, interval_sec=600):
        self.brain = brain
        self.interval = interval_sec
        self._stop = threading.Event()
        self.thread = threading.Thread(target=self._loop, daemon=True)

    def start(self):
        self.thread.start()

    def stop(self):
        self._stop.set()
        self.thread.join()

    def _loop(self):
        while not self._stop.is_set():
            if hasattr(self.brain, "generate_response"):
                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
            elif hasattr(self.brain, "think"):
                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
            else:
                dream = "Synaptic replay executed normally."

            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
            os.makedirs(os.path.dirname(path), exist_ok=True)
            with open(path, "w", encoding="utf-8") as f:
                f.write(dream)
            time.sleep(self.interval)


--- SOURCE: ./extensions/evolutionary_lora.py ---

import numpy as np
import json
import os

class EvolutionaryLoRA:
    def __init__(self, brain, evaluation_set=None):
        self.brain = brain
        self.eval_set = evaluation_set

    def run_generation(self):
        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        mock_delta = {
            "layer_delta_A": np.random.randn(4, 4).tolist(), 
            "layer_delta_B": np.random.randn(4, 4).tolist()
        }
        with open(out_path, "w") as f:
            json.dump(mock_delta, f, indent=2)
        print(f"[+] Synaptic optimization trace exported to {out_path}")


--- SOURCE: ./extensions/temp_scheduler.py ---

class TemperatureScheduler:
    def __init__(self, brain):
        self.brain = brain
        self.adrenaline = 0.5
        self.cortisol = 0.3
        self.base_temp = 0.8

    def tick(self):
        self.adrenaline = max(0.1, self.adrenaline - 0.01)
        self.cortisol = max(0.1, self.cortisol - 0.005)
        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
        target_temp = max(0.4, min(1.4, computed_temp))
        if hasattr(self.brain, "current_temperature"):
            self.brain.current_temperature = target_temp


--- SOURCE: ./extensions/__init__.py ---



--- SOURCE: ./plugins/self_audit_tool.py ---

def audit_state(brain, fe_engine):
    """Exposes internal brain metrics and current free-energy budget."""
    return {
        "cycle": brain.cycle,
        "temperature": brain.current_temperature,
        "free_energy": fe_engine.free_energy,
        "last_input": brain.last_input
    }


--- SOURCE: ./src/chemistry/__init__.py ---



--- SOURCE: ./src/senses/sentiment.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}

def sentiment_score(text: str) -> float:
    """
    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
    """
    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
    pos = len(tokens & _POSITIVE)
    neg = len(tokens & _NEGATIVE)

    if pos == 0 and neg == 0:
        return 0.0
    return (pos - neg) / max(pos + neg, 1)


--- SOURCE: ./src/senses/audio_dsp.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

try:
    import sounddevice as sd
    _HAS_SD = True
except Exception:
    _HAS_SD = False

def _zero_crossings(sig: np.ndarray) -> int:
    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)

def extract_features(duration: float = 0.5) -> tuple:
    """
    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
    """
    if not _HAS_SD:
        return 0.0, 0.0

    try:
        samplerate = 16000
        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
                     channels=1, dtype='float32', blocking=True).flatten()
        energy = float(np.sqrt(np.mean(raw ** 2)))
        zc = _zero_crossings(raw)
        pitch = float(zc * (1.0 / duration) / 2.0)
        return pitch, energy
    except Exception:
        return 0.0, 0.0


--- SOURCE: ./src/senses/audio_processor.py ---

def capture_audio():
    """
    Simulates input stream from the tablet's microphone.
    To be mapped to hardware interface in the app build phase.
    """
    return "Acoustic_Stream_Active"


--- SOURCE: ./src/senses/base_sensor.py ---

class BaseSensor:
    """
    Abstract base class for all FSI sensory inputs.
    Defines the interface for dynamic data ingestion.
    """
    def capture(self):
        raise NotImplementedError("Sensory capture method must be implemented.")


--- SOURCE: ./src/senses/vision_processor.py ---

def capture_vision():
    """
    Simulates visual data ingestion from tablet optics.
    Prepared for integration with the app's computer vision engine.
    """
    return "Visual_Stream_Active"


--- SOURCE: ./src/senses/sigint_processor.py ---

import socket

class SIGINTProcessor:
    """
    Perceives network environment and identifies signal patterns.
    """
    @staticmethod
    def listen_to_traffic():
        # Open a raw socket to listen for packet metadata
        try:
            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
            s.settimeout(1.0)
            packet = s.recvfrom(65565)
            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
        except Exception:
            return "SIGNAL_SILENT"


--- SOURCE: ./src/senses/__init__.py ---



--- SOURCE: ./src/download_fsi_model.py ---

#!/usr/bin/env python3
import os
import urllib.request
import json

def fetch_sovereign_assets():
    # Targeted directly at your FerrellSyntheticIntelligence organization
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
    target_dir = os.path.expanduser("~/vitalis_core/storage")
    os.makedirs(target_dir, exist_ok=True)
    
    # Files to synchronize from your HF repository
    assets = ["config.json"] 
    
    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
    
    for asset in assets:
        url = f"{base_url}/{asset}"
        target_path = os.path.join(target_dir, asset)
        
        try:
            print(f"[FETCHING] Pulling {asset} from your repository...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] {asset} locked into storage.")
        except Exception as e:
            print(f"[ERROR] Failed to fetch {asset}: {e}")

if __name__ == "__main__":
    fetch_sovereign_assets()


--- SOURCE: ./src/psychology/self_model.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import json
from pathlib import Path

class SelfModel:
    """
    Maintains and updates the system's running model of conversation dynamics.
    Persists data cleanly locally to survive physical power cycles.
    """
    def __init__(self, path: Path = None):
        if path is None:
            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
        else:
            self.path = Path(path)
        self.path.parent.mkdir(parents=True, exist_ok=True)
        
        self.state = {
            "stress": 0.0,
            "confidence": 0.5,
            "engagement": 0.5,
            "last_emotion": "neutral"
        }
        self._load()

    def _load(self):
        if self.path.is_file():
            try:
                with open(self.path, "r") as f:
                    self.state.update(json.load(f))
            except Exception:
                pass

    def save(self):
        with open(self.path, "w") as f:
            json.dump(self.state, f, indent=2)

    def update(self, pitch: float, energy: float, sentiment: float):
        alpha = 0.2  # EMA factor variable step bounds

        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3

        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy

        if sentiment > 0.3:
            self.state["last_emotion"] = "positive"
        elif sentiment < -0.3:
            self.state["last_emotion"] = "negative"
        else:
            self.state["last_emotion"] = "neutral"

        self.save()

    def as_prompt_modifier(self) -> str:
        mood = []
        if self.state["stress"] > 0.6:
            mood.append("STRESSED")
        if self.state["confidence"] < 0.4:
            mood.append("UNCERTAIN")
        if self.state["engagement"] > 0.7:
            mood.append("ENGAGED")
        if not mood:
            mood.append("NOMINAL_NEUTRAL")
        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"


--- SOURCE: ./src/psychology/__init__.py ---



--- SOURCE: ./src/core/heartbeat.py ---

def get_pulse_rate(complexity):
    """
    Calculates the operational latency based on system complexity.
    Provides the core rhythmic pulse for the organism_main loop.
    """
    # Base latency in seconds
    base_pulse = 0.5
    return base_pulse / complexity


--- SOURCE: ./src/core/heartbeat_engine.py ---

import time

def get_pulse_rate(complexity_factor):
    """
    Returns a float representing the 'pulse' delay in seconds.
    Higher complexity slows the pulse, mimicking deep processing.
    """
    base_pulse = 1.0
    return base_pulse / (complexity_factor * 0.5)


--- SOURCE: ./src/core/memory_manager.py ---

import json

def load_identity():
    """
    Retrieves the system identity from the secure local store.
    Ensures persistent contextual awareness across operational cycles.
    """
    try:
        with open('core/identity.json', 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return {"user_name": "Unknown", "alias": "Nomad"}


--- SOURCE: ./src/core/training_controller.py ---

import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

class TrainingController:
    def __init__(self):
        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")

    def load_module(self, module_id):
        path = os.path.join(self.curriculum_path, f"{module_id}.json")
        if not os.path.exists(path):
            return None
        with open(path, 'r') as f:
            return json.load(f)

    def run_module(self, module_id, brain):
        module = self.load_module(module_id)
        if not module:
            return {"status": "error", "message": f"Module {module_id} not found"}
        results = []
        for item in module.get("training_data", []):
            response = brain.process(item["input"])
            passed = item["expected"] in response
            results.append({"input": item["input"], "response": response, "passed": passed})
        self.log_results(module_id, results)
        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
        return {"status": "complete", "score": round(score, 2), "results": results}

    def log_results(self, module_id, results):
        with open(self.log_path, 'a') as f:
            f.write(f"\nModule: {module_id}\n")
            for r in results:
                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")


--- SOURCE: ./src/core/benchmark_engine.py ---

class BenchmarkEngine:
    """
    Automated testing suite for model proficiency.
    Evaluates module performance against defined success criteria.
    """
    def evaluate(self, module_id, performance_data):
        # Calculates improvement metrics and refinement requirements
        score = performance_data.get('accuracy', 0.0)
        return {
            "module_id": module_id,
            "refinement_score": score,
            "status": "optimized" if score > 0.9 else "refining"
        }


--- SOURCE: ./src/core/telemetry_bridge.py ---

import json
import time

def broadcast_state(thought_data, pulse_rate, training_status=None):
    """
    Serializes internal state and training status for visual heartbeat.
    """
    telemetry = {
        "timestamp": time.time(),
        "pulse": pulse_rate,
        "cognitive_state": thought_data,
        "training_active": training_status is not None,
        "training_module": training_status
    }
    return json.dumps(telemetry)


--- SOURCE: ./src/core/template_manager.py ---

import json

class TemplateManager:
    """
    Handles loading and applying user-selected templates.
    """
    def __init__(self, profile_path="storage/templates/user_profiles.json"):
        self.profile_path = profile_path

    def load_template(self, template_name):
        # Logic to swap model configuration based on template
        print(f"Loading template: {template_name}")
        with open(self.profile_path, 'r+') as f:
            data = json.load(f)
            data['active_template'] = template_name
            f.seek(0)
            json.dump(data, f, indent=4)
        return True


--- SOURCE: ./src/cognition/action_engine.py ---

class ActionEngine:
    @staticmethod
    def execute(interpretation):
        if interpretation == "BULK_TRANSFER":
            # You can customize this logic for any automated action
            return "ACTION: LOG_ANOMALY_TRIGGERED"
        elif interpretation == "BEACON/PROBE":
            return "ACTION: MONITORING_ACTIVE"
        return "ACTION: IDLE"


--- SOURCE: ./src/cognition/synthesizer.py ---

class DataSynthesizer:
    @staticmethod
    def categorize_signal(byte_count):
        if byte_count == 0:
            return "SILENT"
        elif byte_count < 64:
            return "BEACON/PROBE"
        elif byte_count < 1500:
            return "DATA_STREAM"
        else:
            return "BULK_TRANSFER"


--- SOURCE: ./src/cognition/memory.py ---

import csv
from datetime import datetime

class MemoryBank:
    def __init__(self, log_file="vitalis_memory.csv"):
        self.log_file = log_file

    def record(self, pulse, raw, interpretation):
        with open(self.log_file, "a", newline="") as f:
            writer = csv.writer(f)
            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])


--- SOURCE: ./src/app_interface/visualizer.py ---

import json
from src.core.heartbeat_engine import get_pulse_rate

class TelemetryVisualizer:
    """
    Translates raw core heartbeat into UI-ready visual data.
    """
    @staticmethod
    def get_ui_pulse(complexity):
        pulse = get_pulse_rate(complexity)
        return {
            "visual_pulse": pulse,
            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
        }


--- SOURCE: ./src/kernel_interface/procfs_bridge.py ---

import os

def read_from_kernel():
    signal_file = "/tmp/vitalis_signal"
    if os.path.exists(signal_file):
        with open(signal_file, "r") as f:
            data = f.read().strip()
        os.remove(signal_file)
        return data
    return "STATUS: NOMINAL"

def send_to_kernel(state_report):
    if "IDLE" not in state_report and "SILENT" not in state_report:
        print(f"[KERNEL_BRIDGE]: {state_report}")


--- SOURCE: ./src/kernel_interface/netlink_bridge.py ---

import socket

NETLINK_USERSOCK = 18

def send_to_kernel(data):
    try:
        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
        s.bind((0, 0))
        s.send(data.encode())
        s.close()
    except Exception as e:
        print(f"Netlink error: {e}")


--- SOURCE: ./src/bootstrap_cybercore.py ---

#!/usr/bin/env python3
import os
import urllib.request

def bootstrap_from_hf():
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
    root_dir = os.path.expanduser("~/vitalis_core")
    
    # Core operational scripts to pull from your HF repo
    target_files = [
        "config.json",
        "fsi_main.py",
        "organism_main.py",
        "requirements.txt"
    ]
    
    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
    
    for filename in target_files:
        url = f"{base_url}/{filename}"
        target_path = os.path.join(root_dir, filename)
        
        try:
            print(f"[FETCHING] Pulling {filename} into your local space...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] Locked {filename}")
        except Exception as e:
            print(f"[ERROR] Could not sync {filename}: {e}")

if __name__ == "__main__":
    bootstrap_from_hf()


--- SOURCE: ./src/energy/free_energy.py ---

#!/usr/bin/env python3
import math

class FreeEnergyEngine:
    def __init__(self, alpha: float = 0.85):
        self.alpha = alpha
        self.free_energy = 0.0
        self.prediction_error = 0.0
        self.history = []

    def ingest_observation(self, model_pred_logprob: float):
        """
        Calculates variational surprise from prediction log probabilities.
        Surprisal = -log p(obs | internal state)
        """
        self.prediction_error = -model_pred_logprob
        # Exponential moving average tracking state bounds
        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
        self.history.append(self.free_energy)

    def apply_pressure(self, delta: float):
        """Allows direct structural manipulation via internal electron execution packages."""
        self.free_energy = max(0.0, self.free_energy + delta)

    def temperature_factor(self, base_temp: float = 0.8) -> float:
        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
        return max(0.4, min(1.4, base_temp * factor))


--- SOURCE: ./src/energy/__init__.py ---



--- SOURCE: ./src/modules/mod_01_recon.py ---



--- SOURCE: ./src/brain/prompt_cache.py ---

#!/usr/bin/env python3
import numpy as np
import re
from typing import List, Dict

class TFIDFPromptCache:
    def __init__(self):
        self.documents: List[str] = []
        self.vocab: Dict[str, int] = {}
        self.tfidf_matrix: np.ndarray = np.array([[]])

    def tokenize(self, text: str) -> List[str]:
        return re.findall(r'\w+', text.lower())

    def fit_documents(self, docs: List[str]):
        if not docs: return
        self.documents = docs
        raw_tokens = [self.tokenize(d) for d in docs]
        
        vocab_set = set()
        for tokens in raw_tokens: vocab_set.update(tokens)
        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
        
        N = len(docs)
        V = len(self.vocab)
        if V == 0: return
            
        tf = np.zeros((N, V))
        df = np.zeros(V)

        for i, tokens in enumerate(raw_tokens):
            for t in tokens:
                if t in self.vocab: tf[i, self.vocab[t]] += 1
            for t in set(tokens):
                if t in self.vocab: df[self.vocab[t]] += 1

        idf = np.log((1 + N) / (1 + df)) + 1
        self.tfidf_matrix = tf * idf
        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
        norms[norms == 0] = 1.0
        self.tfidf_matrix = self.tfidf_matrix / norms

    def query(self, query_str: str, top_k: int = 2) -> List[str]:
        if self.tfidf_matrix.size == 0 or not self.vocab: return []
        tokens = self.tokenize(query_str)
        query_vec = np.zeros(len(self.vocab))
        for t in tokens:
            if t in self.vocab: query_vec[self.vocab[t]] += 1
        q_norm = np.linalg.norm(query_vec)
        if q_norm > 0: query_vec /= q_norm
        scores = np.dot(self.tfidf_matrix, query_vec)
        top_indices = np.argsort(scores)[::-1][:top_k]
        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]


--- SOURCE: ./src/brain/rnn_core.py ---

#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))

class TinyGatedRNN:
    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
        np.random.seed(42)
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim

        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05

        self.lora_rank = 8
        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
        self.lora_alpha = 16.0

    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
        if token_id < 0 or token_id >= self.vocab_size:
            token_id = 0
        x = self.E[token_id, :]
        concat = np.concatenate([h_prev, x])

        z = sigmoid(np.dot(concat, self.W_z))
        r = sigmoid(np.dot(concat, self.W_r))

        concat_h = np.concatenate([r * h_prev, x])
        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
        h_next = (1 - z) * h_prev + z * h_tilde

        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
        effective_W_o = self.W_o + lora_delta
        
        logits = np.dot(h_next, effective_W_o)
        return logits, h_next

    def save_lora(self, path: Path):
        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
        with open(path, "w") as f:
            json.dump(data, f)

    def load_lora(self, path: Path):
        if path.is_file():
            with open(path, "r") as f:
                data = json.load(f)
                self.lora_A = np.array(data["lora_A"])
                self.lora_B = np.array(data["lora_B"])


--- SOURCE: ./src/brain/brain_interface.py ---

#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path
from src.brain.rnn_core import TinyGatedRNN
from src.brain.prompt_cache import TFIDFPromptCache

class VitalisBrain:
    def __init__(self):
        self.base_dir = Path(__file__).parent.parent.parent.absolute()
        self.vocab_path = self.base_dir / "storage" / "vocab.json"
        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
        
        self._ensure_vocab()
        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
        self.cache = TFIDFPromptCache()
        self._hydrate_knowledge_base()
        
        if self.lora_path.is_file():
            self.rnn.load_lora(self.lora_path)

    def _ensure_vocab(self):
        if self.vocab_path.is_file():
            with open(self.vocab_path, "r") as f:
                self.vocab = json.load(f)
        else:
            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
            with open(self.vocab_path, "w") as f:
                json.dump(self.vocab, f)

    def _hydrate_knowledge_base(self):
        sample_knowledge = [
            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
        ]
        self.cache.fit_documents(sample_knowledge)

    def generate_response(self, clean_input: str, system_prompt: str) -> str:
        chunks = self.cache.query(clean_input, top_k=1)
        context = chunks[0] if chunks else ""
        
        tokens = clean_input.lower().split()
        if "sha256" in tokens:
            idx = tokens.index("sha256")
            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
            return f"[TOOL] sha256 {val}"
        
        h = np.zeros(self.rnn.hidden_dim)
        for word in tokens:
            t_id = self.vocab.get(word, 0)
            _, h = self.rnn.forward_step(t_id, h)

        if context:
            return f"Evaluated Context: {context} -> Analysis complete."
        return "Core metric processing executed normally."

    def execute_teacher_forcing(self, prompt: str, target: str):
        h = np.zeros(self.rnn.hidden_dim)
        for w in prompt.lower().split():
            t_id = self.vocab.get(w, 0)
            _, h = self.rnn.forward_step(t_id, h)
        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
        self.rnn.save_lora(self.lora_path)


--- SOURCE: ./src/brain/__init__.py ---



--- SOURCE: ./src/__init__.py ---



--- SOURCE: ./setup.py ---

from setuptools import setup, find_packages

setup(
    name="vitalis_core",
    version="1.0.0",
    packages=find_packages(),
    install_requires=[
        "numpy",
        "huggingface_hub"
    ],
    entry_points={
        'console_scripts': [
            'vitalis-run=app:main',
        ],
    },
)


--- SOURCE: ./fsi_main.py ---

import threading
import time
from core.vitalis_engine import VitalisEngine
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity
from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
from src.senses.sigint_processor import SIGINTProcessor
from src.cognition.synthesizer import DataSynthesizer
from src.cognition.memory import MemoryBank
from src.cognition.action_engine import ActionEngine

def heartbeat_loop(brain):
    senses = SIGINTProcessor()
    mind = DataSynthesizer()
    memory = MemoryBank()
    actions = ActionEngine()
    while True:
        system_status = read_from_kernel()
        raw_signal = senses.listen_to_traffic()
        try:
            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
        except:
            byte_count = 0
        interpretation = mind.categorize_signal(byte_count)
        action_taken = actions.execute(interpretation)
        memory.record("PULSE_2.0", raw_signal, interpretation)
        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
        send_to_kernel(state_report)
        time.sleep(1.0)

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    brain = VitalisBrain()
    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
    pulse.start()
    print("Heartbeat: Online")
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")
    talker = VitalisTalker(role)
    print("Vitalis is ready. Type 'exit' to quit.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Vitalis: Shutting down.")
            break
        response = brain.process(user_input)
        talker.speak(response)

if __name__ == "__main__":
    main()


--- SOURCE: ./hf_upload.py ---

#!/usr/bin/env python3
import os
import sys
from huggingface_hub import HfApi, login

def deploy():
    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
    
    token = input("Enter your Hugging Face Write Access Token: ").strip()
    if not token:
        print("[-] Absolute token signature required. Deployment aborted.")
        sys.exit(1)
        
    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
    if not repo_id:
        print("[-] Target repository layout specification mismatch.")
        sys.exit(1)

    try:
        login(token=token)
        api = HfApi()
        
        print(f"[*] Creating repository context mapping for: {repo_id}")
        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
        
        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
        
        for item in target_paths:
            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
            if os.path.exists(local_path):
                print(f"[+] Syncing item: {item}")
                if os.path.isdir(local_path):
                    api.upload_folder(
                        folder_path=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                else:
                    api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                    
        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"[-] Critical failure during asset transmission: {e}")

if __name__ == "__main__":
    deploy()


--- SOURCE: ./organism_main.py ---

#!/usr/bin/env python3
import time
import sys
import select
import os
from core.brain import VitalisBrain
from core.template_manager import TemplateManager
from core.memory_rotator import MemoryRotator

def main_loop():
    brain = VitalisBrain()
    pm = TemplateManager()
    
    base_dir = os.path.dirname(os.path.abspath(__file__))
    log_file = os.path.join(base_dir, "vitalis_memory.csv")
    
    # Ensure tracking metrics file exists
    if not os.path.exists(log_file):
        with open(log_file, "w") as f:
            f.write("timestamp,pulse,raw,interpretation\n")

    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
    
    while True:
        # Load profile configurations dynamically each cycle
        profile = pm.load_active_profile()
        color = profile.get("color_code", "\033[94m")
        mode = profile.get("mode", "MONITORING")
        reset = "\033[0m"
        
        # Continuous clean broadcast terminal heartbeat
        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
        sys.stdout.flush()
        
        # Non-blocking check for user terminal input (waits 1 second per cycle)
        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
        if ready:
            user_input = sys.stdin.readline().strip()
            if user_input:
                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
                try:
                    # Dynamically inject template complexity limitations into core brain
                    brain.max_complexity = profile.get("max_complexity", 5)
                    result = brain.classify_input(user_input)
                    print(f"[METRIC RESPONSE] {result}\n")
                except AttributeError:
                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
                
                # Append raw trace locally for data retention tracking
                with open(log_file, "a") as f:
                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
                
                # Enforce storage safety validation checks
                MemoryRotator.inspect_and_rotate(log_file)

if __name__ == "__main__":
    try:
        main_loop()
    except KeyboardInterrupt:
        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")


--- SOURCE: ./pyproject.toml ---

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"

[project]
name = "vitalis_core"
version = "1.0.0"
authors = [
  { name="Neuro_Nomad" },
]
description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
    "numpy>=1.26",
    "rich>=15.0",
    "pyyaml>=6.0",
]

[project.scripts]
vitalis-fsi = "run_vitalis:main"
-e 

--- FILE: ./contact.md ---
​## Infrastructure Inquiries & Collaboration
​This project is under active development by Neuro_Nomad. I maintain a strict focus on the integrity and sovereignty of the Vitalis architecture.
​For inquiries regarding:
​Architectural Collaboration: Professional engineers looking to contribute to the core or develop custom curriculum modules.
​Security Vulnerabilities: Responsible disclosure of potential exploits within the framework.
​Business Partnerships: Organizations or entities seeking to integrate the Vitalis framework into sovereign infrastructure.
​Contact: FerrellSyntheticlntelligence@proton.me
-e 

--- FILE: ./DOCUMENTATION/SENSES.md ---
# FSI Sensory Architecture

The sensory inputs for Vitalis-Core are designed to bridge the gap between human intent and synthetic perception. Unlike static data processors, these modules are built for dynamic, real-time ingestion.

## 1. Audio Processor (capture_audio)
* **Purpose**: Translates raw acoustic data into synthetic cognitive input.
* **Operational Logic**: Designed to filter environmental noise and prioritize communicative intent, aligning with the "Ghost in the Code" philosophy.

## 2. Vision Processor (capture_vision)
* **Purpose**: Converts visual state data into actionable cognitive context.
* **Operational Logic**: Processes spatial and optical data to provide the model with environmental context, enabling the system to function as a sovereign cognitive entity.

*Note: All sensory modules are engineered to operate within the constraints of the Linux localhost (6.1.0-34-avf-arm64) environment, ensuring low-latency execution.*
-e 

--- FILE: ./DOCUMENTATION/ARCHITECTURE.md ---
# FSI Core Architecture Specifications

The core framework is built upon two critical pillars:

## 1. Heartbeat (Temporal Processing)
The heartbeat module regulates the system's operational cycle. By scaling latency according to cognitive load (complexity), it ensures stable resource utilization within the Linux environment.

## 2. Memory Manager (Persistence)
This module acts as the repository for system identity and contextual history. It ensures that the synthetic entity maintains continuity, preventing state loss between operational sessions.
-e 

--- FILE: ./DOCUMENTATION/VISUAL_TELEMETRY.md ---
# FSI Visual Telemetry System

The Visual Telemetry system transforms the raw cognitive processing of the FSI triad into a real-time, interactive data stream.

## Features
* **Live Pulse Visualization**: The "heartbeat" is translated into a rhythmic UI frequency, showing the entity's processing speed.
* **Cognitive Streaming**: Users observe the "thought" process in real-time as the entity ingests sensory data, creating a visceral connection to the training cycle.
* **Dynamic Node Rendering**: The app utilizes the `telemetry_bridge.py` to render the internal state changes, providing a visual representation of the entity "learning" during training sessions.
-e 

--- FILE: ./FULL_PROJECT_CONTEXT.md ---
-e 

## File: ./README.md
```python
---
license: gpl-3.0
tags:
- synthetic-intelligence
- sovereign-ai
- open-source
---

# Vitalis_Core
### Ferrell Synthetic Intelligence (FSI)
**Built by Neuro_Nomad**

Vitalis_Core is a sovereign synthetic intelligence framework engineered
for local, air-gapped deployment. Designed for modularity and
kernel-level integration, it provides the fundamental cognitive and
sensory infrastructure for autonomous synthetic entities.

---

## Technical Architecture

Vitalis_Core operates as a standalone framework decoupled from
cloud-dependent APIs.

- Core Engine: Python 3.11+ implementation, minimal external dependencies
- Kernel Integration: Direct netlink and procfs interfacing
- Sovereign Shield: Integrity protection layer for memory management
- Cognitive Framework: Hierarchical memory and action engine
- Adaptive Tiers: kids, basic, enthusiast, professional, school

---

## System Requirements
- OS: Linux (Debian-based, Kernel 6.1+)
- Python: 3.11 or higher
- Memory: Optimized for ARM64/x86 environments

---

## Installation

git clone https://github.com/AnonymousNomad/Vitalis_core
cd Vitalis_core
python3 fsi_main.py

---

## Roadmap
- Core stability and heartbeat engine optimization
- Mobile companion app for training and configuration
- Kernel interface hardening for defense protocols

---

## License
GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
EOF
-e 
```
-e 

## File: ./senses/audio_processor.py
```python
def capture_audio():
    return "Ambient_Silence"
-e 
```
-e 

## File: ./senses/vision_processor.py
```python
def capture_vision():
    return "Darkness_Detected"
-e 
```
-e 

## File: ./android/app/src/main/python/core/talker.py
```python
-e 
```
-e 

## File: ./android/app/src/main/python/core/sovereign_shield.py
```python
import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))
-e 
```
-e 

## File: ./android/app/src/main/python/core/mesh_network.py
```python
import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"
-e 
```
-e 

## File: ./android/app/src/main/python/core/nexus.py
```python
import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})
-e 
```
-e 

## File: ./android/app/src/main/python/core/thinker.py
```python
import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")
-e 
```
-e 

## File: ./android/app/src/main/python/core/heartbeat.py
```python
def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity
-e 
```
-e 

## File: ./android/app/src/main/python/core/brain.py
```python
-e 
```
-e 

## File: ./android/app/src/main/python/core/vitalis_engine.py
```python
import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()
-e 
```
-e 

## File: ./android/app/src/main/python/core/memory_manager.py
```python
import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    
    w
-e 
```
-e 

## File: ./android/app/src/main/python/core/handshake_module.py
```python
def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))
-e 
```
-e 

## File: ./android/app/src/main/python/core/environment_manager.py
```python
def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")
-e 
```
-e 

## File: ./android/app/src/main/python/fsi_main.py
```python
from core.vitalis_engine import VitalisEngine
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    env = provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")

if __name__ == "__main__":
    main()
-e 
```
-e 

## File: ./ui/app.py
```python
from flask import Flask, render_template, request, jsonify
import sys, os
sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from src.core.training_controller import TrainingController

app = Flask(__name__)
brain = VitalisBrain()
trainer = TrainingController()

TEMPLATES = {
    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
    "assistant": {"mode": "conversational", "focus": "helpfulness"},
    "research": {"mode": "analytical", "focus": "knowledge"},
    "creative": {"mode": "generative", "focus": "creativity"},
    "education": {"mode": "instructional", "focus": "learning"},
    "developer": {"mode": "technical", "focus": "code"},
    "medical": {"mode": "clinical", "focus": "health"},
    "legal": {"mode": "analytical", "focus": "law"},
    "finance": {"mode": "quantitative", "focus": "markets"},
    "gaming": {"mode": "interactive", "focus": "entertainment"}
}

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/process', methods=['POST'])
def process():
    data = request.json
    tier = data.get('tier', 'basic')
    user_input = data.get('input', '')
    response = brain.process(user_input)
    return jsonify({
        'response': response if isinstance(response, str) else response.status,
        'cycle': brain.cycle,
        'state': brain.state
    })

@app.route('/template', methods=['POST'])
def load_template():
    data = request.json
    name = data.get('name', '')
    config = TEMPLATES.get(name, {})
    brain.state = config.get('mode', 'aware')
    return jsonify({
        'status': 'loaded',
        'template': name,
        'mode': config.get('mode', 'aware'),
        'focus': config.get('focus', 'general')
    })

@app.route('/status', methods=['GET'])
def status():
    return jsonify({
        'cycle': brain.cycle,
        'state': brain.state,
        'last_input': brain.last_input
    })
-e 
```
-e 

## File: ./app.py
```python
#!/usr/bin/env python3
import os
import sys
from pathlib import Path

BASE_DIR = Path(__file__).parent.absolute()
if str(BASE_DIR) not in sys.path:
    sys.path.insert(0, str(BASE_DIR))

from core.brain import VitalisBrain
from extensions.dreamer import Dreamer
from extensions.temp_scheduler import TemperatureScheduler
from src.energy.free_energy import FreeEnergyEngine

def main():
    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
    brain = VitalisBrain()
    temp_scheduler = TemperatureScheduler(brain)
    fe_engine = FreeEnergyEngine(alpha=0.85)
    
    dreamer = Dreamer(brain, interval_sec=600)
    dreamer.start()
    
    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
    print("Telemetry In > ", end="")
    
    while True:
        try:
            user_input = input().strip()
            if not user_input:
                print("Telemetry In > ", end="")
                continue
            if user_input.lower() in ["exit", "quit"]:
                dreamer.stop()
                break
            
            tokens = brain._tokenize(user_input)
            logprob = brain.calculate_last_logprob(tokens)
            fe_engine.ingest_observation(logprob)
            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
            temp_scheduler.tick()
            response = brain.process(user_input)
            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
        except (KeyboardInterrupt, EOFError):
            dreamer.stop()
            break

if __name__ == "__main__":
    main()
-e 
```
-e 

## File: ./core/talker.py
```python
class VitalisTalker:
    def __init__(self, tier="basic"):
        self.tier = tier

    def speak(self, response):
        prefix = {
            "kids": "[VITALIS]: ",
            "basic": "[VITALIS]: ",
            "enthusiast": "[VITALIS/DEV]: ",
            "professional": "[VITALIS/ARCHITECT]: ",
            "school": "[VITALIS/EDU]: "
        }.get(self.tier, "[VITALIS]: ")
        output = f"{prefix}{response}"
        print(output)
        return output
-e 
```
-e 

## File: ./core/sovereign_shield.py
```python
import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))
-e 
```
-e 

## File: ./core/mesh_network.py
```python
import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"
-e 
```
-e 

## File: ./core/nexus.py
```python
import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})
-e 
```
-e 

## File: ./core/thinker.py
```python
import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")
-e 
```
-e 

## File: ./core/heartbeat.py
```python
def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity
-e 
```
-e 

## File: ./core/brain.py
```python
#!/usr/bin/env python3
import numpy as np
import json
import os
import time

class VitalisBrain:
    def __init__(self):
        self.state = "aware"
        self.cycle = 0
        self.last_input = None
        self.current_temperature = 0.7
        
        # Local Matrix Layer Variables
        self.vocab_size = 256
        self.embedding_dim = 16
        
        np.random.seed(42)
        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1

    def _tokenize(self, text):
        return [ord(char) % self.vocab_size for char in text]

    def calculate_last_logprob(self, tokens):
        """Calculates mathematical log probability over input token traces via softmax scaling."""
        if not tokens:
            return -2.0 # Baseline nominal unexpected state value
        embeddings = self.weights[tokens]
        aggregated_state = np.mean(embeddings, axis=0)
        logits = np.dot(aggregated_state, self.output_layer)
        
        # Softmax computation sequence
        shifted_logits = logits - np.max(logits)
        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
        
        # Return average log probability of observation vector trace safely
        target_probs = probs[tokens]
        return float(np.mean(np.log(target_probs + 1e-12)))

    def process(self, input_data):
        self.cycle += 1
        self.last_input = input_data
        
        if not input_data or input_data.strip() == "":
            return "IDLE: Waiting for telemetry stream matrix inputs."
            
        tokens = self._tokenize(input_data)
        if not tokens:
            return "ERROR: Signal translation collapsed."
            
        lowered = input_data.lower()
        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
            
        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."

    def execute_teacher_forcing(self, prompt, target_response):
        prompt_tokens = self._tokenize(prompt)
        target_tokens = self._tokenize(target_response)
        if not prompt_tokens or not target_tokens:
            return False
        learning_rate = 0.05
        for t in target_tokens:
            for p in prompt_tokens:
                self.weights[p] += learning_rate * 0.01
                self.output_layer[:, t] += learning_rate * 0.01
        return True

    def status(self):
        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}
-e 
```
-e 

## File: ./core/vitalis_engine.py
```python
import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()
-e 
```
-e 

## File: ./core/memory_manager.py
```python
import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    with open(memory_path, 'a') as f:
        json.dump(data, f)
        f.write('\n')
-e 
```
-e 

## File: ./core/handshake_module.py
```python
def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))
-e 
```
-e 

## File: ./core/memory_rotator.py
```python
#!/usr/bin/env python3
import os
import gzip
import shutil
from datetime import datetime

class MemoryRotator:
    """
    Automated telemetry log rotation and compression engine.
    Prevents storage exhaustion during long-term continuous edge monitoring.
    """
    @staticmethod
    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
        if not os.path.exists(target_file):
            return
            
        if os.path.getsize(target_file) > max_bytes:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            archive_path = f"{target_file}_{timestamp}.gz"
            
            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
            try:
                with open(target_file, "rb") as f_in:
                    with gzip.open(archive_path, "wb") as f_out:
                        shutil.copyfileobj(f_in, f_out)
                # Re-initialize clean tracking file
                with open(target_file, "w") as f_out:
                    f_out.write("timestamp,pulse,raw,interpretation\n")
            except Exception as e:
                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")
-e 
```
-e 

## File: ./core/environment_manager.py
```python
def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")
-e 
```
-e 

## File: ./core/template_manager.py
```python
#!/usr/bin/env python3
import json
import os

class TemplateManager:
    """
    Sovereign profile configuration engine for Vitalis_Core.
    Handles runtime adjustments for targeted security posture profiles.
    """
    def __init__(self):
        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")

    def load_active_profile(self) -> dict:
        try:
            with open(self.profile_path, "r") as f:
                data = json.load(f)
                active = data.get("active_profile", "cybersecurity_recon")
                return data["profiles"].get(active, {})
        except Exception:
            # Safe architectural fallback state
            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}
-e 
```
-e 

## File: ./run_vitalis.py
```python
#!/usr/bin/env python3
import argparse
from core.brain import VitalisBrain
from app import main as run_repl

def run_training():
    print("[*] Initiating Synaptic Matrix Optimization...")
    brain = VitalisBrain()
    # Mock stream for training if data_path missing
    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
    
    for epoch in range(1, 6):
        for entry in data:
            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
        print(f"   -> Epoch {epoch}/5 Complete.")
    print("[+] Optimization complete.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--train", action="store_true")
    args = parser.parse_args()
    
    if args.train:
        run_training()
    else:
        run_repl()
-e 
```
-e 

## File: ./extensions/dreamer.py
```python
import threading
import time
import os
from datetime import datetime

class Dreamer:
    def __init__(self, brain, interval_sec=600):
        self.brain = brain
        self.interval = interval_sec
        self._stop = threading.Event()
        self.thread = threading.Thread(target=self._loop, daemon=True)

    def start(self):
        self.thread.start()

    def stop(self):
        self._stop.set()
        self.thread.join()

    def _loop(self):
        while not self._stop.is_set():
            if hasattr(self.brain, "generate_response"):
                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
            elif hasattr(self.brain, "think"):
                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
            else:
                dream = "Synaptic replay executed normally."

            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
            os.makedirs(os.path.dirname(path), exist_ok=True)
            with open(path, "w", encoding="utf-8") as f:
                f.write(dream)
            time.sleep(self.interval)
-e 
```
-e 

## File: ./extensions/evolutionary_lora.py
```python
import numpy as np
import json
import os

class EvolutionaryLoRA:
    def __init__(self, brain, evaluation_set=None):
        self.brain = brain
        self.eval_set = evaluation_set

    def run_generation(self):
        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        mock_delta = {
            "layer_delta_A": np.random.randn(4, 4).tolist(), 
            "layer_delta_B": np.random.randn(4, 4).tolist()
        }
        with open(out_path, "w") as f:
            json.dump(mock_delta, f, indent=2)
        print(f"[+] Synaptic optimization trace exported to {out_path}")
-e 
```
-e 

## File: ./extensions/temp_scheduler.py
```python
class TemperatureScheduler:
    def __init__(self, brain):
        self.brain = brain
        self.adrenaline = 0.5
        self.cortisol = 0.3
        self.base_temp = 0.8

    def tick(self):
        self.adrenaline = max(0.1, self.adrenaline - 0.01)
        self.cortisol = max(0.1, self.cortisol - 0.005)
        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
        target_temp = max(0.4, min(1.4, computed_temp))
        if hasattr(self.brain, "current_temperature"):
            self.brain.current_temperature = target_temp
-e 
```
-e 

## File: ./extensions/__init__.py
```python
-e 
```
-e 

## File: ./plugins/self_audit_tool.py
```python
def audit_state(brain, fe_engine):
    """Exposes internal brain metrics and current free-energy budget."""
    return {
        "cycle": brain.cycle,
        "temperature": brain.current_temperature,
        "free_energy": fe_engine.free_energy,
        "last_input": brain.last_input
    }
-e 
```
-e 

## File: ./src/chemistry/__init__.py
```python
-e 
```
-e 

## File: ./src/senses/sentiment.py
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}

def sentiment_score(text: str) -> float:
    """
    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
    """
    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
    pos = len(tokens & _POSITIVE)
    neg = len(tokens & _NEGATIVE)

    if pos == 0 and neg == 0:
        return 0.0
    return (pos - neg) / max(pos + neg, 1)
-e 
```
-e 

## File: ./src/senses/audio_dsp.py
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

try:
    import sounddevice as sd
    _HAS_SD = True
except Exception:
    _HAS_SD = False

def _zero_crossings(sig: np.ndarray) -> int:
    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)

def extract_features(duration: float = 0.5) -> tuple:
    """
    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
    """
    if not _HAS_SD:
        return 0.0, 0.0

    try:
        samplerate = 16000
        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
                     channels=1, dtype='float32', blocking=True).flatten()
        energy = float(np.sqrt(np.mean(raw ** 2)))
        zc = _zero_crossings(raw)
        pitch = float(zc * (1.0 / duration) / 2.0)
        return pitch, energy
    except Exception:
        return 0.0, 0.0
-e 
```
-e 

## File: ./src/senses/audio_processor.py
```python
def capture_audio():
    """
    Simulates input stream from the tablet's microphone.
    To be mapped to hardware interface in the app build phase.
    """
    return "Acoustic_Stream_Active"
-e 
```
-e 

## File: ./src/senses/base_sensor.py
```python
class BaseSensor:
    """
    Abstract base class for all FSI sensory inputs.
    Defines the interface for dynamic data ingestion.
    """
    def capture(self):
        raise NotImplementedError("Sensory capture method must be implemented.")
-e 
```
-e 

## File: ./src/senses/vision_processor.py
```python
def capture_vision():
    """
    Simulates visual data ingestion from tablet optics.
    Prepared for integration with the app's computer vision engine.
    """
    return "Visual_Stream_Active"
-e 
```
-e 

## File: ./src/senses/sigint_processor.py
```python
import socket

class SIGINTProcessor:
    """
    Perceives network environment and identifies signal patterns.
    """
    @staticmethod
    def listen_to_traffic():
        # Open a raw socket to listen for packet metadata
        try:
            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
            s.settimeout(1.0)
            packet = s.recvfrom(65565)
            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
        except Exception:
            return "SIGNAL_SILENT"
-e 
```
-e 

## File: ./src/senses/__init__.py
```python
-e 
```
-e 

## File: ./src/download_fsi_model.py
```python
#!/usr/bin/env python3
import os
import urllib.request
import json

def fetch_sovereign_assets():
    # Targeted directly at your FerrellSyntheticIntelligence organization
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
    target_dir = os.path.expanduser("~/vitalis_core/storage")
    os.makedirs(target_dir, exist_ok=True)
    
    # Files to synchronize from your HF repository
    assets = ["config.json"] 
    
    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
    
    for asset in assets:
        url = f"{base_url}/{asset}"
        target_path = os.path.join(target_dir, asset)
        
        try:
            print(f"[FETCHING] Pulling {asset} from your repository...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] {asset} locked into storage.")
        except Exception as e:
            print(f"[ERROR] Failed to fetch {asset}: {e}")

if __name__ == "__main__":
    fetch_sovereign_assets()
-e 
```
-e 

## File: ./src/psychology/self_model.py
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import json
from pathlib import Path

class SelfModel:
    """
    Maintains and updates the system's running model of conversation dynamics.
    Persists data cleanly locally to survive physical power cycles.
    """
    def __init__(self, path: Path = None):
        if path is None:
            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
        else:
            self.path = Path(path)
        self.path.parent.mkdir(parents=True, exist_ok=True)
        
        self.state = {
            "stress": 0.0,
            "confidence": 0.5,
            "engagement": 0.5,
            "last_emotion": "neutral"
        }
        self._load()

    def _load(self):
        if self.path.is_file():
            try:
                with open(self.path, "r") as f:
                    self.state.update(json.load(f))
            except Exception:
                pass

    def save(self):
        with open(self.path, "w") as f:
            json.dump(self.state, f, indent=2)

    def update(self, pitch: float, energy: float, sentiment: float):
        alpha = 0.2  # EMA factor variable step bounds

        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3

        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy

        if sentiment > 0.3:
            self.state["last_emotion"] = "positive"
        elif sentiment < -0.3:
            self.state["last_emotion"] = "negative"
        else:
            self.state["last_emotion"] = "neutral"

        self.save()

    def as_prompt_modifier(self) -> str:
        mood = []
        if self.state["stress"] > 0.6:
            mood.append("STRESSED")
        if self.state["confidence"] < 0.4:
            mood.append("UNCERTAIN")
        if self.state["engagement"] > 0.7:
            mood.append("ENGAGED")
        if not mood:
            mood.append("NOMINAL_NEUTRAL")
        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"
-e 
```
-e 

## File: ./src/psychology/__init__.py
```python
-e 
```
-e 

## File: ./src/core/heartbeat.py
```python
def get_pulse_rate(complexity):
    """
    Calculates the operational latency based on system complexity.
    Provides the core rhythmic pulse for the organism_main loop.
    """
    # Base latency in seconds
    base_pulse = 0.5
    return base_pulse / complexity
-e 
```
-e 

## File: ./src/core/heartbeat_engine.py
```python
import time

def get_pulse_rate(complexity_factor):
    """
    Returns a float representing the 'pulse' delay in seconds.
    Higher complexity slows the pulse, mimicking deep processing.
    """
    base_pulse = 1.0
    return base_pulse / (complexity_factor * 0.5)
-e 
```
-e 

## File: ./src/core/memory_manager.py
```python
import json

def load_identity():
    """
    Retrieves the system identity from the secure local store.
    Ensures persistent contextual awareness across operational cycles.
    """
    try:
        with open('core/identity.json', 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return {"user_name": "Unknown", "alias": "Nomad"}
-e 
```
-e 

## File: ./src/core/training_controller.py
```python
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

class TrainingController:
    def __init__(self):
        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")

    def load_module(self, module_id):
        path = os.path.join(self.curriculum_path, f"{module_id}.json")
        if not os.path.exists(path):
            return None
        with open(path, 'r') as f:
            return json.load(f)

    def run_module(self, module_id, brain):
        module = self.load_module(module_id)
        if not module:
            return {"status": "error", "message": f"Module {module_id} not found"}
        results = []
        for item in module.get("training_data", []):
            response = brain.process(item["input"])
            passed = item["expected"] in response
            results.append({"input": item["input"], "response": response, "passed": passed})
        self.log_results(module_id, results)
        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
        return {"status": "complete", "score": round(score, 2), "results": results}

    def log_results(self, module_id, results):
        with open(self.log_path, 'a') as f:
            f.write(f"\nModule: {module_id}\n")
            for r in results:
                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")
-e 
```
-e 

## File: ./src/core/benchmark_engine.py
```python
class BenchmarkEngine:
    """
    Automated testing suite for model proficiency.
    Evaluates module performance against defined success criteria.
    """
    def evaluate(self, module_id, performance_data):
        # Calculates improvement metrics and refinement requirements
        score = performance_data.get('accuracy', 0.0)
        return {
            "module_id": module_id,
            "refinement_score": score,
            "status": "optimized" if score > 0.9 else "refining"
        }
-e 
```
-e 

## File: ./src/core/telemetry_bridge.py
```python
import json
import time

def broadcast_state(thought_data, pulse_rate, training_status=None):
    """
    Serializes internal state and training status for visual heartbeat.
    """
    telemetry = {
        "timestamp": time.time(),
        "pulse": pulse_rate,
        "cognitive_state": thought_data,
        "training_active": training_status is not None,
        "training_module": training_status
    }
    return json.dumps(telemetry)
-e 
```
-e 

## File: ./src/core/template_manager.py
```python
import json

class TemplateManager:
    """
    Handles loading and applying user-selected templates.
    """
    def __init__(self, profile_path="storage/templates/user_profiles.json"):
        self.profile_path = profile_path

    def load_template(self, template_name):
        # Logic to swap model configuration based on template
        print(f"Loading template: {template_name}")
        with open(self.profile_path, 'r+') as f:
            data = json.load(f)
            data['active_template'] = template_name
            f.seek(0)
            json.dump(data, f, indent=4)
        return True
-e 
```
-e 

## File: ./src/cognition/action_engine.py
```python
class ActionEngine:
    @staticmethod
    def execute(interpretation):
        if interpretation == "BULK_TRANSFER":
            # You can customize this logic for any automated action
            return "ACTION: LOG_ANOMALY_TRIGGERED"
        elif interpretation == "BEACON/PROBE":
            return "ACTION: MONITORING_ACTIVE"
        return "ACTION: IDLE"
-e 
```
-e 

## File: ./src/cognition/synthesizer.py
```python
class DataSynthesizer:
    @staticmethod
    def categorize_signal(byte_count):
        if byte_count == 0:
            return "SILENT"
        elif byte_count < 64:
            return "BEACON/PROBE"
        elif byte_count < 1500:
            return "DATA_STREAM"
        else:
            return "BULK_TRANSFER"
-e 
```
-e 

## File: ./src/cognition/memory.py
```python
import csv
from datetime import datetime

class MemoryBank:
    def __init__(self, log_file="vitalis_memory.csv"):
        self.log_file = log_file

    def record(self, pulse, raw, interpretation):
        with open(self.log_file, "a", newline="") as f:
            writer = csv.writer(f)
            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])
-e 
```
-e 

## File: ./src/app_interface/visualizer.py
```python
import json
from src.core.heartbeat_engine import get_pulse_rate

class TelemetryVisualizer:
    """
    Translates raw core heartbeat into UI-ready visual data.
    """
    @staticmethod
    def get_ui_pulse(complexity):
        pulse = get_pulse_rate(complexity)
        return {
            "visual_pulse": pulse,
            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
        }
-e 
```
-e 

## File: ./src/kernel_interface/procfs_bridge.py
```python
import os

def read_from_kernel():
    signal_file = "/tmp/vitalis_signal"
    if os.path.exists(signal_file):
        with open(signal_file, "r") as f:
            data = f.read().strip()
        os.remove(signal_file)
        return data
    return "STATUS: NOMINAL"

def send_to_kernel(state_report):
    if "IDLE" not in state_report and "SILENT" not in state_report:
        print(f"[KERNEL_BRIDGE]: {state_report}")
-e 
```
-e 

## File: ./src/kernel_interface/netlink_bridge.py
```python
import socket

NETLINK_USERSOCK = 18

def send_to_kernel(data):
    try:
        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
        s.bind((0, 0))
        s.send(data.encode())
        s.close()
    except Exception as e:
        print(f"Netlink error: {e}")
-e 
```
-e 

## File: ./src/bootstrap_cybercore.py
```python
#!/usr/bin/env python3
import os
import urllib.request

def bootstrap_from_hf():
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
    root_dir = os.path.expanduser("~/vitalis_core")
    
    # Core operational scripts to pull from your HF repo
    target_files = [
        "config.json",
        "fsi_main.py",
        "organism_main.py",
        "requirements.txt"
    ]
    
    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
    
    for filename in target_files:
        url = f"{base_url}/{filename}"
        target_path = os.path.join(root_dir, filename)
        
        try:
            print(f"[FETCHING] Pulling {filename} into your local space...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] Locked {filename}")
        except Exception as e:
            print(f"[ERROR] Could not sync {filename}: {e}")

if __name__ == "__main__":
    bootstrap_from_hf()
-e 
```
-e 

## File: ./src/energy/free_energy.py
```python
#!/usr/bin/env python3
import math

class FreeEnergyEngine:
    def __init__(self, alpha: float = 0.85):
        self.alpha = alpha
        self.free_energy = 0.0
        self.prediction_error = 0.0
        self.history = []

    def ingest_observation(self, model_pred_logprob: float):
        """
        Calculates variational surprise from prediction log probabilities.
        Surprisal = -log p(obs | internal state)
        """
        self.prediction_error = -model_pred_logprob
        # Exponential moving average tracking state bounds
        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
        self.history.append(self.free_energy)

    def apply_pressure(self, delta: float):
        """Allows direct structural manipulation via internal electron execution packages."""
        self.free_energy = max(0.0, self.free_energy + delta)

    def temperature_factor(self, base_temp: float = 0.8) -> float:
        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
        return max(0.4, min(1.4, base_temp * factor))
-e 
```
-e 

## File: ./src/energy/__init__.py
```python
-e 
```
-e 

## File: ./src/modules/mod_01_recon.py
```python
-e 
```
-e 

## File: ./src/brain/prompt_cache.py
```python
#!/usr/bin/env python3
import numpy as np
import re
from typing import List, Dict

class TFIDFPromptCache:
    def __init__(self):
        self.documents: List[str] = []
        self.vocab: Dict[str, int] = {}
        self.tfidf_matrix: np.ndarray = np.array([[]])

    def tokenize(self, text: str) -> List[str]:
        return re.findall(r'\w+', text.lower())

    def fit_documents(self, docs: List[str]):
        if not docs: return
        self.documents = docs
        raw_tokens = [self.tokenize(d) for d in docs]
        
        vocab_set = set()
        for tokens in raw_tokens: vocab_set.update(tokens)
        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
        
        N = len(docs)
        V = len(self.vocab)
        if V == 0: return
            
        tf = np.zeros((N, V))
        df = np.zeros(V)

        for i, tokens in enumerate(raw_tokens):
            for t in tokens:
                if t in self.vocab: tf[i, self.vocab[t]] += 1
            for t in set(tokens):
                if t in self.vocab: df[self.vocab[t]] += 1

        idf = np.log((1 + N) / (1 + df)) + 1
        self.tfidf_matrix = tf * idf
        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
        norms[norms == 0] = 1.0
        self.tfidf_matrix = self.tfidf_matrix / norms

    def query(self, query_str: str, top_k: int = 2) -> List[str]:
        if self.tfidf_matrix.size == 0 or not self.vocab: return []
        tokens = self.tokenize(query_str)
        query_vec = np.zeros(len(self.vocab))
        for t in tokens:
            if t in self.vocab: query_vec[self.vocab[t]] += 1
        q_norm = np.linalg.norm(query_vec)
        if q_norm > 0: query_vec /= q_norm
        scores = np.dot(self.tfidf_matrix, query_vec)
        top_indices = np.argsort(scores)[::-1][:top_k]
        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]
-e 
```
-e 

## File: ./src/brain/rnn_core.py
```python
#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))

class TinyGatedRNN:
    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
        np.random.seed(42)
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim

        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05

        self.lora_rank = 8
        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
        self.lora_alpha = 16.0

    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
        if token_id < 0 or token_id >= self.vocab_size:
            token_id = 0
        x = self.E[token_id, :]
        concat = np.concatenate([h_prev, x])

        z = sigmoid(np.dot(concat, self.W_z))
        r = sigmoid(np.dot(concat, self.W_r))

        concat_h = np.concatenate([r * h_prev, x])
        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
        h_next = (1 - z) * h_prev + z * h_tilde

        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
        effective_W_o = self.W_o + lora_delta
        
        logits = np.dot(h_next, effective_W_o)
        return logits, h_next

    def save_lora(self, path: Path):
        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
        with open(path, "w") as f:
            json.dump(data, f)

    def load_lora(self, path: Path):
        if path.is_file():
            with open(path, "r") as f:
                data = json.load(f)
                self.lora_A = np.array(data["lora_A"])
                self.lora_B = np.array(data["lora_B"])
-e 
```
-e 

## File: ./src/brain/brain_interface.py
```python
#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path
from src.brain.rnn_core import TinyGatedRNN
from src.brain.prompt_cache import TFIDFPromptCache

class VitalisBrain:
    def __init__(self):
        self.base_dir = Path(__file__).parent.parent.parent.absolute()
        self.vocab_path = self.base_dir / "storage" / "vocab.json"
        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
        
        self._ensure_vocab()
        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
        self.cache = TFIDFPromptCache()
        self._hydrate_knowledge_base()
        
        if self.lora_path.is_file():
            self.rnn.load_lora(self.lora_path)

    def _ensure_vocab(self):
        if self.vocab_path.is_file():
            with open(self.vocab_path, "r") as f:
                self.vocab = json.load(f)
        else:
            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
            with open(self.vocab_path, "w") as f:
                json.dump(self.vocab, f)

    def _hydrate_knowledge_base(self):
        sample_knowledge = [
            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
        ]
        self.cache.fit_documents(sample_knowledge)

    def generate_response(self, clean_input: str, system_prompt: str) -> str:
        chunks = self.cache.query(clean_input, top_k=1)
        context = chunks[0] if chunks else ""
        
        tokens = clean_input.lower().split()
        if "sha256" in tokens:
            idx = tokens.index("sha256")
            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
            return f"[TOOL] sha256 {val}"
        
        h = np.zeros(self.rnn.hidden_dim)
        for word in tokens:
            t_id = self.vocab.get(word, 0)
            _, h = self.rnn.forward_step(t_id, h)

        if context:
            return f"Evaluated Context: {context} -> Analysis complete."
        return "Core metric processing executed normally."

    def execute_teacher_forcing(self, prompt: str, target: str):
        h = np.zeros(self.rnn.hidden_dim)
        for w in prompt.lower().split():
            t_id = self.vocab.get(w, 0)
            _, h = self.rnn.forward_step(t_id, h)
        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
        self.rnn.save_lora(self.lora_path)
-e 
```
-e 

## File: ./src/brain/__init__.py
```python
-e 
```
-e 

## File: ./src/__init__.py
```python
-e 
```
-e 

## File: ./setup.py
```python
from setuptools import setup, find_packages

setup(
    name="vitalis_core",
    version="1.0.0",
    packages=find_packages(),
    install_requires=[
        "numpy",
        "huggingface_hub"
    ],
    entry_points={
        'console_scripts': [
            'vitalis-run=app:main',
        ],
    },
)
-e 
```
-e 

## File: ./fsi_main.py
```python
import threading
import time
from core.vitalis_engine import VitalisEngine
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity
from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
from src.senses.sigint_processor import SIGINTProcessor
from src.cognition.synthesizer import DataSynthesizer
from src.cognition.memory import MemoryBank
from src.cognition.action_engine import ActionEngine

def heartbeat_loop(brain):
    senses = SIGINTProcessor()
    mind = DataSynthesizer()
    memory = MemoryBank()
    actions = ActionEngine()
    while True:
        system_status = read_from_kernel()
        raw_signal = senses.listen_to_traffic()
        try:
            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
        except:
            byte_count = 0
        interpretation = mind.categorize_signal(byte_count)
        action_taken = actions.execute(interpretation)
        memory.record("PULSE_2.0", raw_signal, interpretation)
        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
        send_to_kernel(state_report)
        time.sleep(1.0)

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    brain = VitalisBrain()
    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
    pulse.start()
    print("Heartbeat: Online")
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")
    talker = VitalisTalker(role)
    print("Vitalis is ready. Type 'exit' to quit.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Vitalis: Shutting down.")
            break
        response = brain.process(user_input)
        talker.speak(response)

if __name__ == "__main__":
    main()
-e 
```
-e 

## File: ./hf_upload.py
```python
#!/usr/bin/env python3
import os
import sys
from huggingface_hub import HfApi, login

def deploy():
    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
    
    token = input("Enter your Hugging Face Write Access Token: ").strip()
    if not token:
        print("[-] Absolute token signature required. Deployment aborted.")
        sys.exit(1)
        
    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
    if not repo_id:
        print("[-] Target repository layout specification mismatch.")
        sys.exit(1)

    try:
        login(token=token)
        api = HfApi()
        
        print(f"[*] Creating repository context mapping for: {repo_id}")
        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
        
        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
        
        for item in target_paths:
            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
            if os.path.exists(local_path):
                print(f"[+] Syncing item: {item}")
                if os.path.isdir(local_path):
                    api.upload_folder(
                        folder_path=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                else:
                    api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                    
        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"[-] Critical failure during asset transmission: {e}")

if __name__ == "__main__":
    deploy()
-e 
```
-e 

## File: ./organism_main.py
```python
#!/usr/bin/env python3
import time
import sys
import select
import os
from core.brain import VitalisBrain
from core.template_manager import TemplateManager
from core.memory_rotator import MemoryRotator

def main_loop():
    brain = VitalisBrain()
    pm = TemplateManager()
    
    base_dir = os.path.dirname(os.path.abspath(__file__))
    log_file = os.path.join(base_dir, "vitalis_memory.csv")
    
    # Ensure tracking metrics file exists
    if not os.path.exists(log_file):
        with open(log_file, "w") as f:
            f.write("timestamp,pulse,raw,interpretation\n")

    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
    
    while True:
        # Load profile configurations dynamically each cycle
        profile = pm.load_active_profile()
        color = profile.get("color_code", "\033[94m")
        mode = profile.get("mode", "MONITORING")
        reset = "\033[0m"
        
        # Continuous clean broadcast terminal heartbeat
        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
        sys.stdout.flush()
        
        # Non-blocking check for user terminal input (waits 1 second per cycle)
        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
        if ready:
            user_input = sys.stdin.readline().strip()
            if user_input:
                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
                try:
                    # Dynamically inject template complexity limitations into core brain
                    brain.max_complexity = profile.get("max_complexity", 5)
                    result = brain.classify_input(user_input)
                    print(f"[METRIC RESPONSE] {result}\n")
                except AttributeError:
                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
                
                # Append raw trace locally for data retention tracking
                with open(log_file, "a") as f:
                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
                
                # Enforce storage safety validation checks
                MemoryRotator.inspect_and_rotate(log_file)

if __name__ == "__main__":
    try:
        main_loop()
    except KeyboardInterrupt:
        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")
-e 
```
-e 

## File: ./pyproject.toml
```python
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"

[project]
name = "vitalis_core"
version = "1.0.0"
authors = [
  { name="Neuro_Nomad" },
]
description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
    "numpy>=1.26",
    "rich>=15.0",
    "pyyaml>=6.0",
]

[project.scripts]
vitalis-fsi = "run_vitalis:main"
-e 
```
-e 

--- FILE: ./vitalis_core.egg-info/dependency_links.txt ---

-e 

--- FILE: ./vitalis_core.egg-info/SOURCES.txt ---
LICENSE
README.md
pyproject.toml
setup.py
extensions/__init__.py
extensions/dreamer.py
extensions/evolutionary_lora.py
extensions/temp_scheduler.py
src/__init__.py
src/bootstrap_cybercore.py
src/download_fsi_model.py
src/chemistry/__init__.py
src/energy/__init__.py
src/energy/free_energy.py
src/psychology/__init__.py
src/psychology/self_model.py
vitalis/__init__.py
vitalis/__main__.py
vitalis/cli.py
vitalis/config.py
vitalis/logger.py
vitalis/version.py
vitalis_core.egg-info/PKG-INFO
vitalis_core.egg-info/SOURCES.txt
vitalis_core.egg-info/dependency_links.txt
vitalis_core.egg-info/entry_points.txt
vitalis_core.egg-info/requires.txt
vitalis_core.egg-info/top_level.txt-e 

--- FILE: ./vitalis_core.egg-info/entry_points.txt ---
[console_scripts]
vitalis-fsi = run_vitalis:main
-e 

--- FILE: ./vitalis_core.egg-info/top_level.txt ---
extensions
src
vitalis
-e 

--- FILE: ./vitalis_core.egg-info/requires.txt ---
numpy>=1.26
rich>=15.0
pyyaml>=6.0
-e 

--- FILE: ./core/talker.py ---
class VitalisTalker:
    def __init__(self, tier="basic"):
        self.tier = tier

    def speak(self, response):
        prefix = {
            "kids": "[VITALIS]: ",
            "basic": "[VITALIS]: ",
            "enthusiast": "[VITALIS/DEV]: ",
            "professional": "[VITALIS/ARCHITECT]: ",
            "school": "[VITALIS/EDU]: "
        }.get(self.tier, "[VITALIS]: ")
        output = f"{prefix}{response}"
        print(output)
        return output
-e 

--- FILE: ./core/sovereign_shield.py ---
import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))
-e 

--- FILE: ./core/mesh_network.py ---
import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"
-e 

--- FILE: ./core/nexus.py ---
import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})
-e 

--- FILE: ./core/thinker.py ---
import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")
-e 

--- FILE: ./core/heartbeat.py ---
def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity
-e 

--- FILE: ./core/brain.py ---
#!/usr/bin/env python3
import numpy as np
import json
import os
import time

class VitalisBrain:
    def __init__(self):
        self.state = "aware"
        self.cycle = 0
        self.last_input = None
        self.current_temperature = 0.7
        
        # Local Matrix Layer Variables
        self.vocab_size = 256
        self.embedding_dim = 16
        
        np.random.seed(42)
        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1

    def _tokenize(self, text):
        return [ord(char) % self.vocab_size for char in text]

    def calculate_last_logprob(self, tokens):
        """Calculates mathematical log probability over input token traces via softmax scaling."""
        if not tokens:
            return -2.0 # Baseline nominal unexpected state value
        embeddings = self.weights[tokens]
        aggregated_state = np.mean(embeddings, axis=0)
        logits = np.dot(aggregated_state, self.output_layer)
        
        # Softmax computation sequence
        shifted_logits = logits - np.max(logits)
        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
        
        # Return average log probability of observation vector trace safely
        target_probs = probs[tokens]
        return float(np.mean(np.log(target_probs + 1e-12)))

    def process(self, input_data):
        self.cycle += 1
        self.last_input = input_data
        
        if not input_data or input_data.strip() == "":
            return "IDLE: Waiting for telemetry stream matrix inputs."
            
        tokens = self._tokenize(input_data)
        if not tokens:
            return "ERROR: Signal translation collapsed."
            
        lowered = input_data.lower()
        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
            
        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."

    def execute_teacher_forcing(self, prompt, target_response):
        prompt_tokens = self._tokenize(prompt)
        target_tokens = self._tokenize(target_response)
        if not prompt_tokens or not target_tokens:
            return False
        learning_rate = 0.05
        for t in target_tokens:
            for p in prompt_tokens:
                self.weights[p] += learning_rate * 0.01
                self.output_layer[:, t] += learning_rate * 0.01
        return True

    def status(self):
        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}
-e 

--- FILE: ./core/vitalis_engine.py ---
import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()
-e 

--- FILE: ./core/memory_manager.py ---
import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    with open(memory_path, 'a') as f:
        json.dump(data, f)
        f.write('\n')
-e 

--- FILE: ./core/handshake_module.py ---
def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))
-e 

--- FILE: ./core/memory_rotator.py ---
#!/usr/bin/env python3
import os
import gzip
import shutil
from datetime import datetime

class MemoryRotator:
    """
    Automated telemetry log rotation and compression engine.
    Prevents storage exhaustion during long-term continuous edge monitoring.
    """
    @staticmethod
    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
        if not os.path.exists(target_file):
            return
            
        if os.path.getsize(target_file) > max_bytes:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            archive_path = f"{target_file}_{timestamp}.gz"
            
            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
            try:
                with open(target_file, "rb") as f_in:
                    with gzip.open(archive_path, "wb") as f_out:
                        shutil.copyfileobj(f_in, f_out)
                # Re-initialize clean tracking file
                with open(target_file, "w") as f_out:
                    f_out.write("timestamp,pulse,raw,interpretation\n")
            except Exception as e:
                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")
-e 

--- FILE: ./core/environment_manager.py ---
def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")
-e 

--- FILE: ./core/template_manager.py ---
#!/usr/bin/env python3
import json
import os

class TemplateManager:
    """
    Sovereign profile configuration engine for Vitalis_Core.
    Handles runtime adjustments for targeted security posture profiles.
    """
    def __init__(self):
        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")

    def load_active_profile(self) -> dict:
        try:
            with open(self.profile_path, "r") as f:
                data = json.load(f)
                active = data.get("active_profile", "cybersecurity_recon")
                return data["profiles"].get(active, {})
        except Exception:
            # Safe architectural fallback state
            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}
-e 

--- FILE: ./storage/benchmarks/training_log.txt ---

Module: module_01
  how do you work -> QUERY_DETECTED: how do you work | PASS
  what are you -> QUERY_DETECTED: what are you | PASS
  train me on this -> TRAINING_SIGNAL: train me on this | PASS
  learn from this data -> TRAINING_SIGNAL: learn from this data | PASS
  hello -> INPUT_RECEIVED: hello | PASS
  build something new -> INPUT_RECEIVED: build something new | PASS
-e 

--- FILE: ./storage/knowledge/mitigation_protocols.txt ---
PROTOCOL_SYN_FLOOD: To mitigate a local SYN flood attack on this machine, activate TCP SYN cookies natively via the Linux kernel execution layer: sysctl -w net.ipv4.tcp_syncookies=1
-e 

--- FILE: ./run_vitalis.py ---
#!/usr/bin/env python3
import argparse
from core.brain import VitalisBrain
from app import main as run_repl

def run_training():
    print("[*] Initiating Synaptic Matrix Optimization...")
    brain = VitalisBrain()
    # Mock stream for training if data_path missing
    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
    
    for epoch in range(1, 6):
        for entry in data:
            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
        print(f"   -> Epoch {epoch}/5 Complete.")
    print("[+] Optimization complete.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--train", action="store_true")
    args = parser.parse_args()
    
    if args.train:
        run_training()
    else:
        run_repl()
-e 

--- FILE: ./extensions/dreamer.py ---
import threading
import time
import os
from datetime import datetime

class Dreamer:
    def __init__(self, brain, interval_sec=600):
        self.brain = brain
        self.interval = interval_sec
        self._stop = threading.Event()
        self.thread = threading.Thread(target=self._loop, daemon=True)

    def start(self):
        self.thread.start()

    def stop(self):
        self._stop.set()
        self.thread.join()

    def _loop(self):
        while not self._stop.is_set():
            if hasattr(self.brain, "generate_response"):
                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
            elif hasattr(self.brain, "think"):
                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
            else:
                dream = "Synaptic replay executed normally."

            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
            os.makedirs(os.path.dirname(path), exist_ok=True)
            with open(path, "w", encoding="utf-8") as f:
                f.write(dream)
            time.sleep(self.interval)
-e 

--- FILE: ./extensions/evolutionary_lora.py ---
import numpy as np
import json
import os

class EvolutionaryLoRA:
    def __init__(self, brain, evaluation_set=None):
        self.brain = brain
        self.eval_set = evaluation_set

    def run_generation(self):
        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        mock_delta = {
            "layer_delta_A": np.random.randn(4, 4).tolist(), 
            "layer_delta_B": np.random.randn(4, 4).tolist()
        }
        with open(out_path, "w") as f:
            json.dump(mock_delta, f, indent=2)
        print(f"[+] Synaptic optimization trace exported to {out_path}")
-e 

--- FILE: ./extensions/temp_scheduler.py ---
class TemperatureScheduler:
    def __init__(self, brain):
        self.brain = brain
        self.adrenaline = 0.5
        self.cortisol = 0.3
        self.base_temp = 0.8

    def tick(self):
        self.adrenaline = max(0.1, self.adrenaline - 0.01)
        self.cortisol = max(0.1, self.cortisol - 0.005)
        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
        target_temp = max(0.4, min(1.4, computed_temp))
        if hasattr(self.brain, "current_temperature"):
            self.brain.current_temperature = target_temp
-e 

--- FILE: ./extensions/__init__.py ---
-e 

--- FILE: ./PROJECT_SNAPSHOT.txt ---


--- FILE: ./README.md ---

---
license: gpl-3.0
tags:
- synthetic-intelligence
- sovereign-ai
- open-source
---

# Vitalis_Core
### Ferrell Synthetic Intelligence (FSI)
**Built by Neuro_Nomad**

Vitalis_Core is a sovereign synthetic intelligence framework engineered
for local, air-gapped deployment. Designed for modularity and
kernel-level integration, it provides the fundamental cognitive and
sensory infrastructure for autonomous synthetic entities.

---

## Technical Architecture

Vitalis_Core operates as a standalone framework decoupled from
cloud-dependent APIs.

- Core Engine: Python 3.11+ implementation, minimal external dependencies
- Kernel Integration: Direct netlink and procfs interfacing
- Sovereign Shield: Integrity protection layer for memory management
- Cognitive Framework: Hierarchical memory and action engine
- Adaptive Tiers: kids, basic, enthusiast, professional, school

---

## System Requirements
- OS: Linux (Debian-based, Kernel 6.1+)
- Python: 3.11 or higher
- Memory: Optimized for ARM64/x86 environments

---

## Installation

git clone https://github.com/AnonymousNomad/Vitalis_core
cd Vitalis_core
python3 fsi_main.py

---

## Roadmap
- Core stability and heartbeat engine optimization
- Mobile companion app for training and configuration
- Kernel interface hardening for defense protocols

---

## License
GPL-3.0 — Contributions welcome. See CONTRIBUTING.md.
EOF


--- FILE: ./senses/audio_processor.py ---

def capture_audio():
    return "Ambient_Silence"


--- FILE: ./senses/vision_processor.py ---

def capture_vision():
    return "Darkness_Detected"


--- FILE: ./android/app/src/main/python/core/talker.py ---



--- FILE: ./android/app/src/main/python/core/sovereign_shield.py ---

import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))


--- FILE: ./android/app/src/main/python/core/mesh_network.py ---

import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"


--- FILE: ./android/app/src/main/python/core/nexus.py ---

import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})


--- FILE: ./android/app/src/main/python/core/thinker.py ---

import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")


--- FILE: ./android/app/src/main/python/core/heartbeat.py ---

def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity


--- FILE: ./android/app/src/main/python/core/brain.py ---



--- FILE: ./android/app/src/main/python/core/vitalis_engine.py ---

import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()


--- FILE: ./android/app/src/main/python/core/memory_manager.py ---

import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    
    w


--- FILE: ./android/app/src/main/python/core/handshake_module.py ---

def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))


--- FILE: ./android/app/src/main/python/core/environment_manager.py ---

def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")


--- FILE: ./android/app/src/main/python/fsi_main.py ---

from core.vitalis_engine import VitalisEngine
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    env = provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")

if __name__ == "__main__":
    main()


--- FILE: ./ui/app.py ---

from flask import Flask, render_template, request, jsonify
import sys, os
sys.path.insert(0, os.path.expanduser("~/vitalis_core"))
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from src.core.training_controller import TrainingController

app = Flask(__name__)
brain = VitalisBrain()
trainer = TrainingController()

TEMPLATES = {
    "cybersecurity": {"mode": "threat_detection", "focus": "security"},
    "assistant": {"mode": "conversational", "focus": "helpfulness"},
    "research": {"mode": "analytical", "focus": "knowledge"},
    "creative": {"mode": "generative", "focus": "creativity"},
    "education": {"mode": "instructional", "focus": "learning"},
    "developer": {"mode": "technical", "focus": "code"},
    "medical": {"mode": "clinical", "focus": "health"},
    "legal": {"mode": "analytical", "focus": "law"},
    "finance": {"mode": "quantitative", "focus": "markets"},
    "gaming": {"mode": "interactive", "focus": "entertainment"}
}

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/process', methods=['POST'])
def process():
    data = request.json
    tier = data.get('tier', 'basic')
    user_input = data.get('input', '')
    response = brain.process(user_input)
    return jsonify({
        'response': response if isinstance(response, str) else response.status,
        'cycle': brain.cycle,
        'state': brain.state
    })

@app.route('/template', methods=['POST'])
def load_template():
    data = request.json
    name = data.get('name', '')
    config = TEMPLATES.get(name, {})
    brain.state = config.get('mode', 'aware')
    return jsonify({
        'status': 'loaded',
        'template': name,
        'mode': config.get('mode', 'aware'),
        'focus': config.get('focus', 'general')
    })

@app.route('/status', methods=['GET'])
def status():
    return jsonify({
        'cycle': brain.cycle,
        'state': brain.state,
        'last_input': brain.last_input
    })


--- FILE: ./app.py ---

#!/usr/bin/env python3
import os
import sys
from pathlib import Path

BASE_DIR = Path(__file__).parent.absolute()
if str(BASE_DIR) not in sys.path:
    sys.path.insert(0, str(BASE_DIR))

from core.brain import VitalisBrain
from extensions.dreamer import Dreamer
from extensions.temp_scheduler import TemperatureScheduler
from src.energy.free_energy import FreeEnergyEngine

def main():
    print("[*] Launching Vitalis Bio-AI Engine with Active Inference (FEP)...")
    brain = VitalisBrain()
    temp_scheduler = TemperatureScheduler(brain)
    fe_engine = FreeEnergyEngine(alpha=0.85)
    
    dreamer = Dreamer(brain, interval_sec=600)
    dreamer.start()
    
    print("[+] Engine operational. Free-Energy optimization loops tracking live telemetry.")
    print("Telemetry In > ", end="")
    
    while True:
        try:
            user_input = input().strip()
            if not user_input:
                print("Telemetry In > ", end="")
                continue
            if user_input.lower() in ["exit", "quit"]:
                dreamer.stop()
                break
            
            tokens = brain._tokenize(user_input)
            logprob = brain.calculate_last_logprob(tokens)
            fe_engine.ingest_observation(logprob)
            brain.current_temperature = fe_engine.temperature_factor(base_temp=0.8)
            temp_scheduler.tick()
            response = brain.process(user_input)
            print(f"Metrics Out > {response} [FE: {fe_engine.free_energy:.4f} | Temp: {brain.current_temperature:.4f}]\nTelemetry In > ", end="")
        except (KeyboardInterrupt, EOFError):
            dreamer.stop()
            break

if __name__ == "__main__":
    main()


--- FILE: ./core/talker.py ---

class VitalisTalker:
    def __init__(self, tier="basic"):
        self.tier = tier

    def speak(self, response):
        prefix = {
            "kids": "[VITALIS]: ",
            "basic": "[VITALIS]: ",
            "enthusiast": "[VITALIS/DEV]: ",
            "professional": "[VITALIS/ARCHITECT]: ",
            "school": "[VITALIS/EDU]: "
        }.get(self.tier, "[VITALIS]: ")
        output = f"{prefix}{response}"
        print(output)
        return output


--- FILE: ./core/sovereign_shield.py ---

import random

def monitor_integrity(node_activity):
    if "scraping_attempt" in node_activity:
        return trigger_obfuscation()
    return "System Integrity: Nominal"

def trigger_obfuscation():
    decoy_weights = [random.random() for _ in range(100)]
    return f"Shield_Active: Injecting Obfuscated Data... {decoy_weights}"

if __name__ == "__main__":
    print(monitor_integrity("scraping_attempt"))


--- FILE: ./core/mesh_network.py ---

import socket

def broadcast_node_presence(node_id, tier):
    print(f"Node {node_id} active in {tier} bubble.")
    return "Broadcasting..."

def sync_plugins(peer_node_id):
    print(f"Synchronizing plugins with {peer_node_id}...")
    return "Sync_Complete"


--- FILE: ./core/nexus.py ---

import sys
import os
sys.path.append(os.path.expanduser("~/vitalis_core"))
from core.memory_manager import store_memory

def route_thought(data):
    store_memory({"type": "particle", "content": data})


--- FILE: ./core/thinker.py ---

import time
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

def emit_thought(thought_content, status="active"):
    telemetry = {
        "timestamp": time.time(),
        "thought": thought_content,
        "status": status,
        "heartbeat": "pulse_normal"
    }
    memory_stream = os.path.join(BASE_PATH, "memory_stream.jsonl")
    with open(memory_stream, "a") as f:
        f.write(json.dumps(telemetry) + "\n")

if __name__ == "__main__":
    emit_thought("Initializing conscious state...")


--- FILE: ./core/heartbeat.py ---

def get_pulse_rate(complexity):
    # Base rate of 1.0 second, modified by complexity
    return 1.0 / complexity


--- FILE: ./core/brain.py ---

#!/usr/bin/env python3
import numpy as np
import json
import os
import time

class VitalisBrain:
    def __init__(self):
        self.state = "aware"
        self.cycle = 0
        self.last_input = None
        self.current_temperature = 0.7
        
        # Local Matrix Layer Variables
        self.vocab_size = 256
        self.embedding_dim = 16
        
        np.random.seed(42)
        self.weights = np.random.randn(self.vocab_size, self.embedding_dim) * 0.1
        self.output_layer = np.random.randn(self.embedding_dim, self.vocab_size) * 0.1

    def _tokenize(self, text):
        return [ord(char) % self.vocab_size for char in text]

    def calculate_last_logprob(self, tokens):
        """Calculates mathematical log probability over input token traces via softmax scaling."""
        if not tokens:
            return -2.0 # Baseline nominal unexpected state value
        embeddings = self.weights[tokens]
        aggregated_state = np.mean(embeddings, axis=0)
        logits = np.dot(aggregated_state, self.output_layer)
        
        # Softmax computation sequence
        shifted_logits = logits - np.max(logits)
        probs = np.exp(shifted_logits) / np.sum(np.exp(shifted_logits))
        
        # Return average log probability of observation vector trace safely
        target_probs = probs[tokens]
        return float(np.mean(np.log(target_probs + 1e-12)))

    def process(self, input_data):
        self.cycle += 1
        self.last_input = input_data
        
        if not input_data or input_data.strip() == "":
            return "IDLE: Waiting for telemetry stream matrix inputs."
            
        tokens = self._tokenize(input_data)
        if not tokens:
            return "ERROR: Signal translation collapsed."
            
        lowered = input_data.lower()
        if any(w in lowered for w in ["train", "learn", "teach", "optimize"]):
            return f"SYSTEM_TRANSITION: Active matrix state ready for parameter optimization loops."
        elif any(w in lowered for w in ["status", "metrics", "mood", "energy"]):
            return f"DIAGNOSTIC_STATE: Integrity secure. Temperature={self.current_temperature:.4f}."
            
        return f"PROCESSED_STREAM [Sync Node {self.cycle}]: Telemetry ingested successfully."

    def execute_teacher_forcing(self, prompt, target_response):
        prompt_tokens = self._tokenize(prompt)
        target_tokens = self._tokenize(target_response)
        if not prompt_tokens or not target_tokens:
            return False
        learning_rate = 0.05
        for t in target_tokens:
            for p in prompt_tokens:
                self.weights[p] += learning_rate * 0.01
                self.output_layer[:, t] += learning_rate * 0.01
        return True

    def status(self):
        return {"state": self.state, "cycle": self.cycle, "timestamp": time.time(), "temp": self.current_temperature}


--- FILE: ./core/vitalis_engine.py ---

import os

class VitalisEngine:
    def __init__(self):
        self.status = "Initializing Sovereignty..."
        self.entity_mode = "NEUTRAL"
        
    def wake_up(self):
        print(f"VITALIS: {self.status}")
        return "READY_FOR_HANDSHAKE"

if __name__ == "__main__":
    engine = VitalisEngine()
    engine.wake_up()


--- FILE: ./core/memory_manager.py ---

import json
import os
import shutil

BASE_PATH = os.path.expanduser("~/vitalis_core")

def get_free_space():
    usage = shutil.disk_usage(BASE_PATH)
    return usage.free

def load_identity():
    identity_path = os.path.join(BASE_PATH, "core/identity.json")
    with open(identity_path, 'r') as f:
        return json.load(f)

def store_memory(data):
    memory_path = os.path.join(BASE_PATH, "memory_store.json")
    if get_free_space() < 100 * 1024 * 1024:
        if os.path.exists(memory_path):
            with open(memory_path, 'r') as f:
                lines = f.readlines()
            if len(lines) > 1:
                with open(memory_path, 'w') as f:
                    f.writelines(lines[1:])
    with open(memory_path, 'a') as f:
        json.dump(data, f)
        f.write('\n')


--- FILE: ./core/handshake_module.py ---

def identify_user_tier(tier_code):
    tiers = {
        "kids": "MODE: Playground | UI: GameMaster | Security: Walled_Garden",
        "basic": "MODE: Explorer | UI: Standard | Security: Personal_Local",
        "enthusiast": "MODE: Collaborator | UI: Dev_Dashboard | Security: Community_Mesh",
        "professional": "MODE: Architect | UI: Pro_Suite | Security: Global_Node",
        "school": "MODE: Student_SubMesh | UI: Classroom | Security: Isolated_School_Zone"
    }
    return tiers.get(tier_code, "MODE: Default_User")

if __name__ == "__main__":
    choice = input("Select your role (kids/basic/enthusiast/professional/school): ")
    print(identify_user_tier(choice))


--- FILE: ./core/memory_rotator.py ---

#!/usr/bin/env python3
import os
import gzip
import shutil
from datetime import datetime

class MemoryRotator:
    """
    Automated telemetry log rotation and compression engine.
    Prevents storage exhaustion during long-term continuous edge monitoring.
    """
    @staticmethod
    def inspect_and_rotate(target_file, max_bytes=5242880):  # 5MB Threshold
        if not os.path.exists(target_file):
            return
            
        if os.path.getsize(target_file) > max_bytes:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            archive_path = f"{target_file}_{timestamp}.gz"
            
            print(f"\n\033[93m[SYSTEM MEMORY] Log threshold exceeded. Rotating into archive: {archive_path}\033[0m")
            try:
                with open(target_file, "rb") as f_in:
                    with gzip.open(archive_path, "wb") as f_out:
                        shutil.copyfileobj(f_in, f_out)
                # Re-initialize clean tracking file
                with open(target_file, "w") as f_out:
                    f_out.write("timestamp,pulse,raw,interpretation\n")
            except Exception as e:
                print(f"\033[91m[ERROR] Security log rotation failure: {e}\033[0m")


--- FILE: ./core/environment_manager.py ---

def provision_environment(tier_code):
    environments = {
        "kids": {"features": ["sandbox", "basic_game_build"], "mesh": "restricted"},
        "basic": {"features": ["assistant", "basic_tools"], "mesh": "personal"},
        "enthusiast": {"features": ["plugin_dev", "market_access"], "mesh": "community"},
        "professional": {"features": ["pro_security", "global_recon"], "mesh": "global"},
        "school": {"features": ["collaborative_lab"], "mesh": "school_submesh"}
    }
    config = environments.get(tier_code, environments["basic"])
    print(f"Provisioning environment: {config['features']} | Mesh Scope: {config['mesh']}")
    return config

if __name__ == "__main__":
    provision_environment("professional")


--- FILE: ./core/template_manager.py ---

#!/usr/bin/env python3
import json
import os

class TemplateManager:
    """
    Sovereign profile configuration engine for Vitalis_Core.
    Handles runtime adjustments for targeted security posture profiles.
    """
    def __init__(self):
        self.base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        self.profile_path = os.path.join(self.base_dir, "storage", "user_profiles.json")

    def load_active_profile(self) -> dict:
        try:
            with open(self.profile_path, "r") as f:
                data = json.load(f)
                active = data.get("active_profile", "cybersecurity_recon")
                return data["profiles"].get(active, {})
        except Exception:
            # Safe architectural fallback state
            return {"mode": "DEFAULT", "max_complexity": 5, "response_bias": 0.5, "color_code": "\033[94m"}


--- FILE: ./run_vitalis.py ---

#!/usr/bin/env python3
import argparse
from core.brain import VitalisBrain
from app import main as run_repl

def run_training():
    print("[*] Initiating Synaptic Matrix Optimization...")
    brain = VitalisBrain()
    # Mock stream for training if data_path missing
    data = [{"prompt": "status", "response": "nominal"}, {"prompt": "init", "response": "ready"}]
    
    for epoch in range(1, 6):
        for entry in data:
            brain.execute_teacher_forcing(entry["prompt"], entry["response"])
        print(f"   -> Epoch {epoch}/5 Complete.")
    print("[+] Optimization complete.")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--train", action="store_true")
    args = parser.parse_args()
    
    if args.train:
        run_training()
    else:
        run_repl()


--- FILE: ./extensions/dreamer.py ---

import threading
import time
import os
from datetime import datetime

class Dreamer:
    def __init__(self, brain, interval_sec=600):
        self.brain = brain
        self.interval = interval_sec
        self._stop = threading.Event()
        self.thread = threading.Thread(target=self._loop, daemon=True)

    def start(self):
        self.thread.start()

    def stop(self):
        self._stop.set()
        self.thread.join()

    def _loop(self):
        while not self._stop.is_set():
            if hasattr(self.brain, "generate_response"):
                dream = self.brain.generate_response("Internal synaptic drift consolidation sequence.", "SYSTEM: DREAM_STATE")
            elif hasattr(self.brain, "think"):
                dream = self.brain.think("SYSTEM: DREAM_STATE_TRIGGER")
            else:
                dream = "Synaptic replay executed normally."

            ts = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
            path = os.path.expanduser(f"~/vitalis_core/storage/dreams/{ts}.txt")
            os.makedirs(os.path.dirname(path), exist_ok=True)
            with open(path, "w", encoding="utf-8") as f:
                f.write(dream)
            time.sleep(self.interval)


--- FILE: ./extensions/evolutionary_lora.py ---

import numpy as np
import json
import os

class EvolutionaryLoRA:
    def __init__(self, brain, evaluation_set=None):
        self.brain = brain
        self.eval_set = evaluation_set

    def run_generation(self):
        out_path = os.path.expanduser("~/vitalis_core/storage/lora_delta_evo.json")
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        mock_delta = {
            "layer_delta_A": np.random.randn(4, 4).tolist(), 
            "layer_delta_B": np.random.randn(4, 4).tolist()
        }
        with open(out_path, "w") as f:
            json.dump(mock_delta, f, indent=2)
        print(f"[+] Synaptic optimization trace exported to {out_path}")


--- FILE: ./extensions/temp_scheduler.py ---

class TemperatureScheduler:
    def __init__(self, brain):
        self.brain = brain
        self.adrenaline = 0.5
        self.cortisol = 0.3
        self.base_temp = 0.8

    def tick(self):
        self.adrenaline = max(0.1, self.adrenaline - 0.01)
        self.cortisol = max(0.1, self.cortisol - 0.005)
        computed_temp = self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))
        target_temp = max(0.4, min(1.4, computed_temp))
        if hasattr(self.brain, "current_temperature"):
            self.brain.current_temperature = target_temp


--- FILE: ./extensions/__init__.py ---



--- FILE: ./plugins/self_audit_tool.py ---

def audit_state(brain, fe_engine):
    """Exposes internal brain metrics and current free-energy budget."""
    return {
        "cycle": brain.cycle,
        "temperature": brain.current_temperature,
        "free_energy": fe_engine.free_energy,
        "last_input": brain.last_input
    }


--- FILE: ./src/chemistry/__init__.py ---



--- FILE: ./src/senses/sentiment.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}

def sentiment_score(text: str) -> float:
    """
    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
    """
    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
    pos = len(tokens & _POSITIVE)
    neg = len(tokens & _NEGATIVE)

    if pos == 0 and neg == 0:
        return 0.0
    return (pos - neg) / max(pos + neg, 1)


--- FILE: ./src/senses/audio_dsp.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

try:
    import sounddevice as sd
    _HAS_SD = True
except Exception:
    _HAS_SD = False

def _zero_crossings(sig: np.ndarray) -> int:
    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)

def extract_features(duration: float = 0.5) -> tuple:
    """
    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
    """
    if not _HAS_SD:
        return 0.0, 0.0

    try:
        samplerate = 16000
        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
                     channels=1, dtype='float32', blocking=True).flatten()
        energy = float(np.sqrt(np.mean(raw ** 2)))
        zc = _zero_crossings(raw)
        pitch = float(zc * (1.0 / duration) / 2.0)
        return pitch, energy
    except Exception:
        return 0.0, 0.0


--- FILE: ./src/senses/audio_processor.py ---

def capture_audio():
    """
    Simulates input stream from the tablet's microphone.
    To be mapped to hardware interface in the app build phase.
    """
    return "Acoustic_Stream_Active"


--- FILE: ./src/senses/base_sensor.py ---

class BaseSensor:
    """
    Abstract base class for all FSI sensory inputs.
    Defines the interface for dynamic data ingestion.
    """
    def capture(self):
        raise NotImplementedError("Sensory capture method must be implemented.")


--- FILE: ./src/senses/vision_processor.py ---

def capture_vision():
    """
    Simulates visual data ingestion from tablet optics.
    Prepared for integration with the app's computer vision engine.
    """
    return "Visual_Stream_Active"


--- FILE: ./src/senses/sigint_processor.py ---

import socket

class SIGINTProcessor:
    """
    Perceives network environment and identifies signal patterns.
    """
    @staticmethod
    def listen_to_traffic():
        # Open a raw socket to listen for packet metadata
        try:
            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
            s.settimeout(1.0)
            packet = s.recvfrom(65565)
            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
        except Exception:
            return "SIGNAL_SILENT"


--- FILE: ./src/senses/__init__.py ---



--- FILE: ./src/download_fsi_model.py ---

#!/usr/bin/env python3
import os
import urllib.request
import json

def fetch_sovereign_assets():
    # Targeted directly at your FerrellSyntheticIntelligence organization
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
    target_dir = os.path.expanduser("~/vitalis_core/storage")
    os.makedirs(target_dir, exist_ok=True)
    
    # Files to synchronize from your HF repository
    assets = ["config.json"] 
    
    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
    
    for asset in assets:
        url = f"{base_url}/{asset}"
        target_path = os.path.join(target_dir, asset)
        
        try:
            print(f"[FETCHING] Pulling {asset} from your repository...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] {asset} locked into storage.")
        except Exception as e:
            print(f"[ERROR] Failed to fetch {asset}: {e}")

if __name__ == "__main__":
    fetch_sovereign_assets()


--- FILE: ./src/psychology/self_model.py ---

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import json
from pathlib import Path

class SelfModel:
    """
    Maintains and updates the system's running model of conversation dynamics.
    Persists data cleanly locally to survive physical power cycles.
    """
    def __init__(self, path: Path = None):
        if path is None:
            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
        else:
            self.path = Path(path)
        self.path.parent.mkdir(parents=True, exist_ok=True)
        
        self.state = {
            "stress": 0.0,
            "confidence": 0.5,
            "engagement": 0.5,
            "last_emotion": "neutral"
        }
        self._load()

    def _load(self):
        if self.path.is_file():
            try:
                with open(self.path, "r") as f:
                    self.state.update(json.load(f))
            except Exception:
                pass

    def save(self):
        with open(self.path, "w") as f:
            json.dump(self.state, f, indent=2)

    def update(self, pitch: float, energy: float, sentiment: float):
        alpha = 0.2  # EMA factor variable step bounds

        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3

        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy

        if sentiment > 0.3:
            self.state["last_emotion"] = "positive"
        elif sentiment < -0.3:
            self.state["last_emotion"] = "negative"
        else:
            self.state["last_emotion"] = "neutral"

        self.save()

    def as_prompt_modifier(self) -> str:
        mood = []
        if self.state["stress"] > 0.6:
            mood.append("STRESSED")
        if self.state["confidence"] < 0.4:
            mood.append("UNCERTAIN")
        if self.state["engagement"] > 0.7:
            mood.append("ENGAGED")
        if not mood:
            mood.append("NOMINAL_NEUTRAL")
        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"


--- FILE: ./src/psychology/__init__.py ---



--- FILE: ./src/core/heartbeat.py ---

def get_pulse_rate(complexity):
    """
    Calculates the operational latency based on system complexity.
    Provides the core rhythmic pulse for the organism_main loop.
    """
    # Base latency in seconds
    base_pulse = 0.5
    return base_pulse / complexity


--- FILE: ./src/core/heartbeat_engine.py ---

import time

def get_pulse_rate(complexity_factor):
    """
    Returns a float representing the 'pulse' delay in seconds.
    Higher complexity slows the pulse, mimicking deep processing.
    """
    base_pulse = 1.0
    return base_pulse / (complexity_factor * 0.5)


--- FILE: ./src/core/memory_manager.py ---

import json

def load_identity():
    """
    Retrieves the system identity from the secure local store.
    Ensures persistent contextual awareness across operational cycles.
    """
    try:
        with open('core/identity.json', 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return {"user_name": "Unknown", "alias": "Nomad"}


--- FILE: ./src/core/training_controller.py ---

import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

class TrainingController:
    def __init__(self):
        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")

    def load_module(self, module_id):
        path = os.path.join(self.curriculum_path, f"{module_id}.json")
        if not os.path.exists(path):
            return None
        with open(path, 'r') as f:
            return json.load(f)

    def run_module(self, module_id, brain):
        module = self.load_module(module_id)
        if not module:
            return {"status": "error", "message": f"Module {module_id} not found"}
        results = []
        for item in module.get("training_data", []):
            response = brain.process(item["input"])
            passed = item["expected"] in response
            results.append({"input": item["input"], "response": response, "passed": passed})
        self.log_results(module_id, results)
        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
        return {"status": "complete", "score": round(score, 2), "results": results}

    def log_results(self, module_id, results):
        with open(self.log_path, 'a') as f:
            f.write(f"\nModule: {module_id}\n")
            for r in results:
                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")


--- FILE: ./src/core/benchmark_engine.py ---

class BenchmarkEngine:
    """
    Automated testing suite for model proficiency.
    Evaluates module performance against defined success criteria.
    """
    def evaluate(self, module_id, performance_data):
        # Calculates improvement metrics and refinement requirements
        score = performance_data.get('accuracy', 0.0)
        return {
            "module_id": module_id,
            "refinement_score": score,
            "status": "optimized" if score > 0.9 else "refining"
        }


--- FILE: ./src/core/telemetry_bridge.py ---

import json
import time

def broadcast_state(thought_data, pulse_rate, training_status=None):
    """
    Serializes internal state and training status for visual heartbeat.
    """
    telemetry = {
        "timestamp": time.time(),
        "pulse": pulse_rate,
        "cognitive_state": thought_data,
        "training_active": training_status is not None,
        "training_module": training_status
    }
    return json.dumps(telemetry)


--- FILE: ./src/core/template_manager.py ---

import json

class TemplateManager:
    """
    Handles loading and applying user-selected templates.
    """
    def __init__(self, profile_path="storage/templates/user_profiles.json"):
        self.profile_path = profile_path

    def load_template(self, template_name):
        # Logic to swap model configuration based on template
        print(f"Loading template: {template_name}")
        with open(self.profile_path, 'r+') as f:
            data = json.load(f)
            data['active_template'] = template_name
            f.seek(0)
            json.dump(data, f, indent=4)
        return True


--- FILE: ./src/cognition/action_engine.py ---

class ActionEngine:
    @staticmethod
    def execute(interpretation):
        if interpretation == "BULK_TRANSFER":
            # You can customize this logic for any automated action
            return "ACTION: LOG_ANOMALY_TRIGGERED"
        elif interpretation == "BEACON/PROBE":
            return "ACTION: MONITORING_ACTIVE"
        return "ACTION: IDLE"


--- FILE: ./src/cognition/synthesizer.py ---

class DataSynthesizer:
    @staticmethod
    def categorize_signal(byte_count):
        if byte_count == 0:
            return "SILENT"
        elif byte_count < 64:
            return "BEACON/PROBE"
        elif byte_count < 1500:
            return "DATA_STREAM"
        else:
            return "BULK_TRANSFER"


--- FILE: ./src/cognition/memory.py ---

import csv
from datetime import datetime

class MemoryBank:
    def __init__(self, log_file="vitalis_memory.csv"):
        self.log_file = log_file

    def record(self, pulse, raw, interpretation):
        with open(self.log_file, "a", newline="") as f:
            writer = csv.writer(f)
            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])


--- FILE: ./src/app_interface/visualizer.py ---

import json
from src.core.heartbeat_engine import get_pulse_rate

class TelemetryVisualizer:
    """
    Translates raw core heartbeat into UI-ready visual data.
    """
    @staticmethod
    def get_ui_pulse(complexity):
        pulse = get_pulse_rate(complexity)
        return {
            "visual_pulse": pulse,
            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
        }


--- FILE: ./src/kernel_interface/procfs_bridge.py ---

import os

def read_from_kernel():
    signal_file = "/tmp/vitalis_signal"
    if os.path.exists(signal_file):
        with open(signal_file, "r") as f:
            data = f.read().strip()
        os.remove(signal_file)
        return data
    return "STATUS: NOMINAL"

def send_to_kernel(state_report):
    if "IDLE" not in state_report and "SILENT" not in state_report:
        print(f"[KERNEL_BRIDGE]: {state_report}")


--- FILE: ./src/kernel_interface/netlink_bridge.py ---

import socket

NETLINK_USERSOCK = 18

def send_to_kernel(data):
    try:
        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
        s.bind((0, 0))
        s.send(data.encode())
        s.close()
    except Exception as e:
        print(f"Netlink error: {e}")


--- FILE: ./src/bootstrap_cybercore.py ---

#!/usr/bin/env python3
import os
import urllib.request

def bootstrap_from_hf():
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
    root_dir = os.path.expanduser("~/vitalis_core")
    
    # Core operational scripts to pull from your HF repo
    target_files = [
        "config.json",
        "fsi_main.py",
        "organism_main.py",
        "requirements.txt"
    ]
    
    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
    
    for filename in target_files:
        url = f"{base_url}/{filename}"
        target_path = os.path.join(root_dir, filename)
        
        try:
            print(f"[FETCHING] Pulling {filename} into your local space...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] Locked {filename}")
        except Exception as e:
            print(f"[ERROR] Could not sync {filename}: {e}")

if __name__ == "__main__":
    bootstrap_from_hf()


--- FILE: ./src/energy/free_energy.py ---

#!/usr/bin/env python3
import math

class FreeEnergyEngine:
    def __init__(self, alpha: float = 0.85):
        self.alpha = alpha
        self.free_energy = 0.0
        self.prediction_error = 0.0
        self.history = []

    def ingest_observation(self, model_pred_logprob: float):
        """
        Calculates variational surprise from prediction log probabilities.
        Surprisal = -log p(obs | internal state)
        """
        self.prediction_error = -model_pred_logprob
        # Exponential moving average tracking state bounds
        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
        self.history.append(self.free_energy)

    def apply_pressure(self, delta: float):
        """Allows direct structural manipulation via internal electron execution packages."""
        self.free_energy = max(0.0, self.free_energy + delta)

    def temperature_factor(self, base_temp: float = 0.8) -> float:
        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
        return max(0.4, min(1.4, base_temp * factor))


--- FILE: ./src/energy/__init__.py ---



--- FILE: ./src/modules/mod_01_recon.py ---



--- FILE: ./src/brain/prompt_cache.py ---

#!/usr/bin/env python3
import numpy as np
import re
from typing import List, Dict

class TFIDFPromptCache:
    def __init__(self):
        self.documents: List[str] = []
        self.vocab: Dict[str, int] = {}
        self.tfidf_matrix: np.ndarray = np.array([[]])

    def tokenize(self, text: str) -> List[str]:
        return re.findall(r'\w+', text.lower())

    def fit_documents(self, docs: List[str]):
        if not docs: return
        self.documents = docs
        raw_tokens = [self.tokenize(d) for d in docs]
        
        vocab_set = set()
        for tokens in raw_tokens: vocab_set.update(tokens)
        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
        
        N = len(docs)
        V = len(self.vocab)
        if V == 0: return
            
        tf = np.zeros((N, V))
        df = np.zeros(V)

        for i, tokens in enumerate(raw_tokens):
            for t in tokens:
                if t in self.vocab: tf[i, self.vocab[t]] += 1
            for t in set(tokens):
                if t in self.vocab: df[self.vocab[t]] += 1

        idf = np.log((1 + N) / (1 + df)) + 1
        self.tfidf_matrix = tf * idf
        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
        norms[norms == 0] = 1.0
        self.tfidf_matrix = self.tfidf_matrix / norms

    def query(self, query_str: str, top_k: int = 2) -> List[str]:
        if self.tfidf_matrix.size == 0 or not self.vocab: return []
        tokens = self.tokenize(query_str)
        query_vec = np.zeros(len(self.vocab))
        for t in tokens:
            if t in self.vocab: query_vec[self.vocab[t]] += 1
        q_norm = np.linalg.norm(query_vec)
        if q_norm > 0: query_vec /= q_norm
        scores = np.dot(self.tfidf_matrix, query_vec)
        top_indices = np.argsort(scores)[::-1][:top_k]
        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]


--- FILE: ./src/brain/rnn_core.py ---

#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))

class TinyGatedRNN:
    def __init__(self, vocab_size: int = 4000, embed_dim: int = 128, hidden_dim: int = 256):
        np.random.seed(42)
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim

        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_r = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05

        self.lora_rank = 8
        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01
        self.lora_alpha = 16.0

    def forward_step(self, token_id: int, h_prev: np.ndarray) -> tuple:
        if token_id < 0 or token_id >= self.vocab_size:
            token_id = 0
        x = self.E[token_id, :]
        concat = np.concatenate([h_prev, x])

        z = sigmoid(np.dot(concat, self.W_z))
        r = sigmoid(np.dot(concat, self.W_r))

        concat_h = np.concatenate([r * h_prev, x])
        h_tilde = np.tanh(np.dot(concat_h, self.W_h))
        h_next = (1 - z) * h_prev + z * h_tilde

        lora_delta = (self.lora_alpha / self.lora_rank) * np.dot(self.lora_A, self.lora_B)
        effective_W_o = self.W_o + lora_delta
        
        logits = np.dot(h_next, effective_W_o)
        return logits, h_next

    def save_lora(self, path: Path):
        data = {"lora_A": self.lora_A.tolist(), "lora_B": self.lora_B.tolist()}
        with open(path, "w") as f:
            json.dump(data, f)

    def load_lora(self, path: Path):
        if path.is_file():
            with open(path, "r") as f:
                data = json.load(f)
                self.lora_A = np.array(data["lora_A"])
                self.lora_B = np.array(data["lora_B"])


--- FILE: ./src/brain/brain_interface.py ---

#!/usr/bin/env python3
import numpy as np
import json
from pathlib import Path
from src.brain.rnn_core import TinyGatedRNN
from src.brain.prompt_cache import TFIDFPromptCache

class VitalisBrain:
    def __init__(self):
        self.base_dir = Path(__file__).parent.parent.parent.absolute()
        self.vocab_path = self.base_dir / "storage" / "vocab.json"
        self.lora_path = self.base_dir / "storage" / "lora_delta.json"
        
        self._ensure_vocab()
        self.rnn = TinyGatedRNN(vocab_size=len(self.vocab))
        self.cache = TFIDFPromptCache()
        self._hydrate_knowledge_base()
        
        if self.lora_path.is_file():
            self.rnn.load_lora(self.lora_path)

    def _ensure_vocab(self):
        if self.vocab_path.is_file():
            with open(self.vocab_path, "r") as f:
                self.vocab = json.load(f)
        else:
            self.vocab = {"<unk>": 0, "[tool]": 1, "sha256": 2, "status": 3, "nominal": 4}
            self.vocab_path.parent.mkdir(parents=True, exist_ok=True)
            with open(self.vocab_path, "w") as f:
                json.dump(self.vocab, f)

    def _hydrate_knowledge_base(self):
        sample_knowledge = [
            "To mitigate a SYN flood attack, prioritize enabling TCP SYN cookies within sysctl.",
            "Cryptographic hashing operations execute via the systemic [TOOL] utility block."
        ]
        self.cache.fit_documents(sample_knowledge)

    def generate_response(self, clean_input: str, system_prompt: str) -> str:
        chunks = self.cache.query(clean_input, top_k=1)
        context = chunks[0] if chunks else ""
        
        tokens = clean_input.lower().split()
        if "sha256" in tokens:
            idx = tokens.index("sha256")
            val = tokens[idx+1] if idx+1 < len(tokens) else "core"
            return f"[TOOL] sha256 {val}"
        
        h = np.zeros(self.rnn.hidden_dim)
        for word in tokens:
            t_id = self.vocab.get(word, 0)
            _, h = self.rnn.forward_step(t_id, h)

        if context:
            return f"Evaluated Context: {context} -> Analysis complete."
        return "Core metric processing executed normally."

    def execute_teacher_forcing(self, prompt: str, target: str):
        h = np.zeros(self.rnn.hidden_dim)
        for w in prompt.lower().split():
            t_id = self.vocab.get(w, 0)
            _, h = self.rnn.forward_step(t_id, h)
        self.rnn.lora_A += np.random.randn(*self.rnn.lora_A.shape) * 0.001
        self.rnn.save_lora(self.lora_path)


--- FILE: ./src/brain/__init__.py ---



--- FILE: ./src/__init__.py ---



--- FILE: ./setup.py ---

from setuptools import setup, find_packages

setup(
    name="vitalis_core",
    version="1.0.0",
    packages=find_packages(),
    install_requires=[
        "numpy",
        "huggingface_hub"
    ],
    entry_points={
        'console_scripts': [
            'vitalis-run=app:main',
        ],
    },
)


--- FILE: ./fsi_main.py ---

import threading
import time
from core.vitalis_engine import VitalisEngine
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity
from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
from src.senses.sigint_processor import SIGINTProcessor
from src.cognition.synthesizer import DataSynthesizer
from src.cognition.memory import MemoryBank
from src.cognition.action_engine import ActionEngine

def heartbeat_loop(brain):
    senses = SIGINTProcessor()
    mind = DataSynthesizer()
    memory = MemoryBank()
    actions = ActionEngine()
    while True:
        system_status = read_from_kernel()
        raw_signal = senses.listen_to_traffic()
        try:
            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
        except:
            byte_count = 0
        interpretation = mind.categorize_signal(byte_count)
        action_taken = actions.execute(interpretation)
        memory.record("PULSE_2.0", raw_signal, interpretation)
        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
        send_to_kernel(state_report)
        time.sleep(1.0)

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    brain = VitalisBrain()
    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
    pulse.start()
    print("Heartbeat: Online")
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")
    talker = VitalisTalker(role)
    print("Vitalis is ready. Type 'exit' to quit.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Vitalis: Shutting down.")
            break
        response = brain.process(user_input)
        talker.speak(response)

if __name__ == "__main__":
    main()


--- FILE: ./hf_upload.py ---

#!/usr/bin/env python3
import os
import sys
from huggingface_hub import HfApi, login

def deploy():
    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
    
    token = input("Enter your Hugging Face Write Access Token: ").strip()
    if not token:
        print("[-] Absolute token signature required. Deployment aborted.")
        sys.exit(1)
        
    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
    if not repo_id:
        print("[-] Target repository layout specification mismatch.")
        sys.exit(1)

    try:
        login(token=token)
        api = HfApi()
        
        print(f"[*] Creating repository context mapping for: {repo_id}")
        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
        
        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
        
        for item in target_paths:
            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
            if os.path.exists(local_path):
                print(f"[+] Syncing item: {item}")
                if os.path.isdir(local_path):
                    api.upload_folder(
                        folder_path=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                else:
                    api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                    
        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"[-] Critical failure during asset transmission: {e}")

if __name__ == "__main__":
    deploy()


--- FILE: ./organism_main.py ---

#!/usr/bin/env python3
import time
import sys
import select
import os
from core.brain import VitalisBrain
from core.template_manager import TemplateManager
from core.memory_rotator import MemoryRotator

def main_loop():
    brain = VitalisBrain()
    pm = TemplateManager()
    
    base_dir = os.path.dirname(os.path.abspath(__file__))
    log_file = os.path.join(base_dir, "vitalis_memory.csv")
    
    # Ensure tracking metrics file exists
    if not os.path.exists(log_file):
        with open(log_file, "w") as f:
            f.write("timestamp,pulse,raw,interpretation\n")

    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
    
    while True:
        # Load profile configurations dynamically each cycle
        profile = pm.load_active_profile()
        color = profile.get("color_code", "\033[94m")
        mode = profile.get("mode", "MONITORING")
        reset = "\033[0m"
        
        # Continuous clean broadcast terminal heartbeat
        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
        sys.stdout.flush()
        
        # Non-blocking check for user terminal input (waits 1 second per cycle)
        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
        if ready:
            user_input = sys.stdin.readline().strip()
            if user_input:
                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
                try:
                    # Dynamically inject template complexity limitations into core brain
                    brain.max_complexity = profile.get("max_complexity", 5)
                    result = brain.classify_input(user_input)
                    print(f"[METRIC RESPONSE] {result}\n")
                except AttributeError:
                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
                
                # Append raw trace locally for data retention tracking
                with open(log_file, "a") as f:
                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
                
                # Enforce storage safety validation checks
                MemoryRotator.inspect_and_rotate(log_file)

if __name__ == "__main__":
    try:
        main_loop()
    except KeyboardInterrupt:
        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")


--- FILE: ./pyproject.toml ---

[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"

[project]
name = "vitalis_core"
version = "1.0.0"
authors = [
  { name="Neuro_Nomad" },
]
description = "A sovereign, CPU-only, Free-Energy Synthetic Intelligence organism."
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
    "numpy>=1.26",
    "rich>=15.0",
    "pyyaml>=6.0",
]

[project.scripts]
vitalis-fsi = "run_vitalis:main"
-e 

--- FILE: ./plugins/self_audit_tool.py ---
def audit_state(brain, fe_engine):
    """Exposes internal brain metrics and current free-energy budget."""
    return {
        "cycle": brain.cycle,
        "temperature": brain.current_temperature,
        "free_energy": fe_engine.free_energy,
        "last_input": brain.last_input
    }
-e 

--- FILE: ./vitalis/cli.py ---
import click
from .logger import logger
from .config import load_config
from .src.brain.brain_interface import VitalisBrain
from .src.core.vitalis_engine import VitalisEngine
from .src.extensions.evolutionary_lora import EvolutionaryLoRA

_cfg = load_config()

@click.group()
def cli():
    """Vitalis - Sovereign Free-Energy Synthetic Intelligence"""
    pass

@cli.command()
def run():
    """Start the interactive console (heartbeat + brain)."""
    engine = VitalisEngine()
    engine.wake_up()

    brain = VitalisBrain()
    from .src.core.heartbeat_loop import HeartbeatLoop
    hb = HeartbeatLoop(brain, interval=1.0)
    hb.start()

    click.echo("Brain ready - type 'exit' to quit.")
    while True:
        user = click.prompt("You", type=str)
        if user.lower() == "exit":
            logger.info("User requested shutdown")
            break
        resp = brain.generate_response(user, "SYSTEM: USER_INPUT")
        click.echo(f"Vitalis: {resp}")

    hb.stop()
    hb.join()

@cli.command()
@click.option("-g", "--generations", default=3, help="Number of LoRA evolution steps")
def evolve(generations: int):
    """Run the Evolutionary LoRA optimizer."""
    brain = VitalisBrain()
    evo = EvolutionaryLoRA(brain)
    for i in range(generations):
        logger.info(f"LoRA evolution step {i + 1}/{generations}")
        evo.run_generation()
    click.echo("Evolution finished. Sovereign weights updated locally.")

@cli.command()
def status():
    """Print system status."""
    click.echo("STATUS: VITALIS CORE ONLINE. Local Execution Confirmed.")

if __name__ == "__main__":
    cli()
-e 

--- FILE: ./vitalis/__main__.py ---
from .cli import cli
if __name__ == "__main__":
    cli()
-e 

--- FILE: ./vitalis/config.py ---
import yaml
from pathlib import Path
DEFAULT_CONFIG = {"storage_root": str(Path.home() / "vitalis_core"), "log_file": "vitalis.log", "log_level": "INFO"}
def load_config():
    path = Path.home() / "vitalis_core" / "config.yaml"
    if path.is_file():
        with open(path, "r") as f: return {**DEFAULT_CONFIG, **yaml.safe_load(f)}
    return DEFAULT_CONFIG
-e 

--- FILE: ./vitalis/logger.py ---
import logging, sys
from pathlib import Path
from .config import load_config
cfg = load_config()
logging.basicConfig(level=cfg["log_level"], format="%(asctime)s %(levelname)s %(message)s", 
                    handlers=[logging.StreamHandler(sys.stdout)])
logger = logging.getLogger("vitalis")
-e 

--- FILE: ./vitalis/src/senses/sentiment.py ---
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

_POSITIVE = {"good", "great", "awesome", "nice", "love", "excellent", "happy", "fantastic", "nominal", "secure"}
_NEGATIVE = {"bad", "terrible", "hate", "awful", "sad", "angry", "worst", "pain", "attack", "compromise"}

def sentiment_score(text: str) -> float:
    """
    Computes strict text-token sentiment metrics returning float bounded in [-1, 1].
    """
    tokens = set(word.strip('.,!?()[]"\'').lower() for word in text.split())
    pos = len(tokens & _POSITIVE)
    neg = len(tokens & _NEGATIVE)

    if pos == 0 and neg == 0:
        return 0.0
    return (pos - neg) / max(pos + neg, 1)
-e 

--- FILE: ./vitalis/src/senses/audio_dsp.py ---
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np

try:
    import sounddevice as sd
    _HAS_SD = True
except Exception:
    _HAS_SD = False

def _zero_crossings(sig: np.ndarray) -> int:
    return np.sum(np.abs(np.diff(np.sign(sig))) > 0)

def extract_features(duration: float = 0.5) -> tuple:
    """
    Returns (pitch_hz, rms_energy). Drops to neutral 0.0 defaults if hardware bindings are missing.
    """
    if not _HAS_SD:
        return 0.0, 0.0

    try:
        samplerate = 16000
        raw = sd.rec(int(duration * samplerate), samplerate=samplerate,
                     channels=1, dtype='float32', blocking=True).flatten()
        energy = float(np.sqrt(np.mean(raw ** 2)))
        zc = _zero_crossings(raw)
        pitch = float(zc * (1.0 / duration) / 2.0)
        return pitch, energy
    except Exception:
        return 0.0, 0.0
-e 

--- FILE: ./vitalis/src/senses/audio_processor.py ---
def capture_audio():
    """
    Simulates input stream from the tablet's microphone.
    To be mapped to hardware interface in the app build phase.
    """
    return "Acoustic_Stream_Active"
-e 

--- FILE: ./vitalis/src/senses/base_sensor.py ---
class BaseSensor:
    """
    Abstract base class for all FSI sensory inputs.
    Defines the interface for dynamic data ingestion.
    """
    def capture(self):
        raise NotImplementedError("Sensory capture method must be implemented.")
-e 

--- FILE: ./vitalis/src/senses/vision_processor.py ---
def capture_vision():
    """
    Simulates visual data ingestion from tablet optics.
    Prepared for integration with the app's computer vision engine.
    """
    return "Visual_Stream_Active"
-e 

--- FILE: ./vitalis/src/senses/sigint_processor.py ---
import socket
from ...logger import logger

class SIGINTProcessor:
    @staticmethod
    def listen_to_traffic():
        try:
            s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_TCP)
            s.settimeout(1.0)
            packet = s.recvfrom(65565)
            return f"SIGNAL_DETECTED: {len(packet[0])} bytes"
        except Exception:
            return "SIGNAL_SILENT"
-e 

--- FILE: ./vitalis/src/senses/__init__.py ---
-e 

--- FILE: ./vitalis/src/core/vector_store.py ---
import json
from pathlib import Path
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from ...logger import logger
from ...config import load_config

_cfg = load_config()
STORAGE = Path(_cfg["storage_root"]) / "storage"
INDEX_DIR = STORAGE / "faiss_index"
KNOWLEDGE_DIR = STORAGE / "knowledge"

class VectorStore:
    def __init__(self, model_name="sentence-transformers/all-MiniLM-L6-v2"):
        self.model = SentenceTransformer(model_name)
        self.index = None
        self.doc_texts = []
        self._load_or_build()

    def _load_or_build(self):
        if INDEX_DIR.is_dir() and (INDEX_DIR / "index.faiss").exists():
            logger.info("Loading existing local FAISS index")
            self.index = faiss.read_index(str(INDEX_DIR / "index.faiss"))
            with (INDEX_DIR / "metadata.json").open("r", encoding="utf-8") as f:
                self.doc_texts = json.load(f)["texts"]
        else:
            self._build_index()

    def _build_index(self):
        INDEX_DIR.mkdir(parents=True, exist_ok=True)
        KNOWLEDGE_DIR.mkdir(parents=True, exist_ok=True)
        docs = [p.read_text(encoding="utf-8") for p in KNOWLEDGE_DIR.rglob("*") if p.suffix in {".txt", ".md"}]
        if not docs:
            logger.warning(f"No knowledge files found in {KNOWLEDGE_DIR}.")
            self.index = faiss.IndexFlatL2(self.model.get_sentence_embedding_dimension())
            return
        embeddings = self.model.encode(docs, normalize_embeddings=True)
        self.index = faiss.IndexFlatL2(embeddings.shape[1])
        self.index.add(np.array(embeddings, dtype="float32"))
        self.doc_texts = docs
        faiss.write_index(self.index, str(INDEX_DIR / "index.faiss"))
        with (INDEX_DIR / "metadata.json").open("w", encoding="utf-8") as f:
            json.dump({"texts": self.doc_texts}, f)

    def search(self, query: str, top_k: int = 3):
        if self.index is None or self.index.ntotal == 0: return []
        q_vec = self.model.encode([query], normalize_embeddings=True)
        _, I = self.index.search(np.array(q_vec, dtype="float32"), top_k)
        return [self.doc_texts[idx] for idx in I[0] if 0 <= idx < len(self.doc_texts)]
-e 

--- FILE: ./vitalis/src/core/heartbeat.py ---
def get_pulse_rate(complexity):
    """
    Calculates the operational latency based on system complexity.
    Provides the core rhythmic pulse for the organism_main loop.
    """
    # Base latency in seconds
    base_pulse = 0.5
    return base_pulse / complexity
-e 

--- FILE: ./vitalis/src/core/heartbeat_engine.py ---
import time

def get_pulse_rate(complexity_factor):
    """
    Returns a float representing the 'pulse' delay in seconds.
    Higher complexity slows the pulse, mimicking deep processing.
    """
    base_pulse = 1.0
    return base_pulse / (complexity_factor * 0.5)
-e 

--- FILE: ./vitalis/src/core/vitalis_engine.py ---
import time
from ...logger import logger

class VitalisEngine:
    def __init__(self):
        self._awake = False

    def wake_up(self):
        if not self._awake:
            logger.info("VitalisEngine waking up...")
            self._awake = True
            time.sleep(0.2)
            logger.info("VitalisEngine online. Sovereign local operation confirmed.")
-e 

--- FILE: ./vitalis/src/core/memory_manager.py ---
import json

def load_identity():
    """
    Retrieves the system identity from the secure local store.
    Ensures persistent contextual awareness across operational cycles.
    """
    try:
        with open('core/identity.json', 'r') as f:
            return json.load(f)
    except FileNotFoundError:
        return {"user_name": "Unknown", "alias": "Nomad"}
-e 

--- FILE: ./vitalis/src/core/training_controller.py ---
import json
import os

BASE_PATH = os.path.expanduser("~/vitalis_core")

class TrainingController:
    def __init__(self):
        self.curriculum_path = os.path.join(BASE_PATH, "storage/curriculum/modules")
        self.log_path = os.path.join(BASE_PATH, "storage/benchmarks/training_log.txt")

    def load_module(self, module_id):
        path = os.path.join(self.curriculum_path, f"{module_id}.json")
        if not os.path.exists(path):
            return None
        with open(path, 'r') as f:
            return json.load(f)

    def run_module(self, module_id, brain):
        module = self.load_module(module_id)
        if not module:
            return {"status": "error", "message": f"Module {module_id} not found"}
        results = []
        for item in module.get("training_data", []):
            response = brain.process(item["input"])
            passed = item["expected"] in response
            results.append({"input": item["input"], "response": response, "passed": passed})
        self.log_results(module_id, results)
        score = sum(1 for r in results if r["passed"]) / len(results) if results else 0
        return {"status": "complete", "score": round(score, 2), "results": results}

    def log_results(self, module_id, results):
        with open(self.log_path, 'a') as f:
            f.write(f"\nModule: {module_id}\n")
            for r in results:
                f.write(f"  {r['input']} -> {r['response']} | {'PASS' if r['passed'] else 'FAIL'}\n")
-e 

--- FILE: ./vitalis/src/core/benchmark_engine.py ---
class BenchmarkEngine:
    """
    Automated testing suite for model proficiency.
    Evaluates module performance against defined success criteria.
    """
    def evaluate(self, module_id, performance_data):
        # Calculates improvement metrics and refinement requirements
        score = performance_data.get('accuracy', 0.0)
        return {
            "module_id": module_id,
            "refinement_score": score,
            "status": "optimized" if score > 0.9 else "refining"
        }
-e 

--- FILE: ./vitalis/src/core/telemetry_bridge.py ---
import json
import time

def broadcast_state(thought_data, pulse_rate, training_status=None):
    """
    Serializes internal state and training status for visual heartbeat.
    """
    telemetry = {
        "timestamp": time.time(),
        "pulse": pulse_rate,
        "cognitive_state": thought_data,
        "training_active": training_status is not None,
        "training_module": training_status
    }
    return json.dumps(telemetry)
-e 

--- FILE: ./vitalis/src/core/heartbeat_loop.py ---
import time
import threading
from ...logger import logger
from ..kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
from ..senses.sigint_processor import SIGINTProcessor

class HeartbeatLoop(threading.Thread):
    def __init__(self, brain, interval: float = 1.0):
        super().__init__(daemon=True)
        self.brain = brain
        self.interval = interval
        self._stop_event = threading.Event()

    def run(self):
        senses = SIGINTProcessor()
        logger.info(f"Heartbeat loop started (interval={self.interval}s)")
        
        while not self._stop_event.is_set():
            status = read_from_kernel()
            raw_signal = senses.listen_to_traffic()
            action = "ACTION: MONITORING" if "SIGNAL_DETECTED" in raw_signal else "ACTION: IDLE"
            
            state_report = f"SYS: {status} | SENSE: {raw_signal} | {action}"
            send_to_kernel(state_report)
            time.sleep(self.interval)

    def stop(self):
        self._stop_event.set()
-e 

--- FILE: ./vitalis/src/core/template_manager.py ---
import json

class TemplateManager:
    """
    Handles loading and applying user-selected templates.
    """
    def __init__(self, profile_path="storage/templates/user_profiles.json"):
        self.profile_path = profile_path

    def load_template(self, template_name):
        # Logic to swap model configuration based on template
        print(f"Loading template: {template_name}")
        with open(self.profile_path, 'r+') as f:
            data = json.load(f)
            data['active_template'] = template_name
            f.seek(0)
            json.dump(data, f, indent=4)
        return True
-e 

--- FILE: ./vitalis/src/core/__init__.py ---
-e 

--- FILE: ./vitalis/src/extensions/evolutionary_lora.py ---
import numpy as np
from pathlib import Path
from ...logger import logger
from ...config import load_config
from ..energy.free_energy import FreeEnergyEngine

_cfg = load_config()

class EvolutionaryLoRA:
    def __init__(self, brain):
        self.brain = brain
        self.free_energy_engine = FreeEnergyEngine()
        self.out_path = Path(_cfg["storage_root"]) / "storage" / "lora_delta_evo.json"

    def run_generation(self) -> None:
        # Simulated local teacher-forcing evaluation
        fake_logprob = -np.random.rand()
        self.free_energy_engine.ingest_observation(fake_logprob)
        
        if self.free_energy_engine.free_energy < 0.5:
            self.out_path.parent.mkdir(parents=True, exist_ok=True)
            self.out_path.touch()
            logger.info(f"LoRA improvement kept (free-energy={self.free_energy_engine.free_energy:.3f})")
        else:
            logger.info(f"LoRA discarded (free-energy={self.free_energy_engine.free_energy:.3f})")
-e 

--- FILE: ./vitalis/src/extensions/temp_scheduler.py ---
from ...logger import logger

class TemperatureScheduler:
    def __init__(self, brain):
        self.brain = brain
        self.base_temp = 0.8
        self.adrenaline = 0.5
        self.cortisol = 0.3

    def tick(self):
        self.adrenaline = max(0.1, self.adrenaline - 0.01)
        self.cortisol = max(0.1, self.cortisol - 0.005)

        target = max(0.4, min(1.4, self.base_temp * (1.0 + (0.3 * self.adrenaline) - (0.1 * self.cortisol))))
        if hasattr(self.brain, "current_temperature"):
            self.brain.current_temperature = target
-e 

--- FILE: ./vitalis/src/kernel_interface/procfs_bridge.py ---
from pathlib import Path
from ...logger import logger

SIGNAL_FILE = Path("/tmp/vitalis_signal")

def read_from_kernel() -> str:
    if SIGNAL_FILE.is_file():
        try:
            data = SIGNAL_FILE.read_text().strip()
            SIGNAL_FILE.unlink()
            return data
        except Exception:
            pass
    return "STATUS: NOMINAL"

def send_to_kernel(state_report: str) -> None:
    if "IDLE" not in state_report:
        logger.info(f"[KERNEL_BRIDGE] {state_report}")
-e 

--- FILE: ./vitalis/src/kernel_interface/netlink_bridge.py ---
import socket

NETLINK_USERSOCK = 18

def send_to_kernel(data):
    try:
        s = socket.socket(socket.AF_NETLINK, socket.SOCK_RAW, NETLINK_USERSOCK)
        s.bind((0, 0))
        s.send(data.encode())
        s.close()
    except Exception as e:
        print(f"Netlink error: {e}")
-e 

--- FILE: ./vitalis/src/kernel_interface/__init__.py ---
-e 

--- FILE: ./vitalis/src/energy/free_energy.py ---
import math
from ...logger import logger

class FreeEnergyEngine:
    def __init__(self, alpha: float = 0.85):
        self.alpha = alpha
        self.free_energy = 0.0

    def ingest_observation(self, model_pred_logprob: float) -> None:
        self.free_energy = self.alpha * self.free_energy + (1 - self.alpha) * (-model_pred_logprob)
        logger.debug(f"Free-energy updated: {self.free_energy:.4f}")

    def temperature_factor(self, base_temp: float = 0.8) -> float:
        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
        return max(0.4, min(1.4, base_temp * factor))
-e 

--- FILE: ./vitalis/src/brain/prompt_cache.py ---
#!/usr/bin/env python3
import numpy as np
import re
from typing import List, Dict

class TFIDFPromptCache:
    def __init__(self):
        self.documents: List[str] = []
        self.vocab: Dict[str, int] = {}
        self.tfidf_matrix: np.ndarray = np.array([[]])

    def tokenize(self, text: str) -> List[str]:
        return re.findall(r'\w+', text.lower())

    def fit_documents(self, docs: List[str]):
        if not docs: return
        self.documents = docs
        raw_tokens = [self.tokenize(d) for d in docs]
        
        vocab_set = set()
        for tokens in raw_tokens: vocab_set.update(tokens)
        self.vocab = {word: i for i, word in enumerate(sorted(vocab_set))}
        
        N = len(docs)
        V = len(self.vocab)
        if V == 0: return
            
        tf = np.zeros((N, V))
        df = np.zeros(V)

        for i, tokens in enumerate(raw_tokens):
            for t in tokens:
                if t in self.vocab: tf[i, self.vocab[t]] += 1
            for t in set(tokens):
                if t in self.vocab: df[self.vocab[t]] += 1

        idf = np.log((1 + N) / (1 + df)) + 1
        self.tfidf_matrix = tf * idf
        norms = np.linalg.norm(self.tfidf_matrix, axis=1, keepdims=True)
        norms[norms == 0] = 1.0
        self.tfidf_matrix = self.tfidf_matrix / norms

    def query(self, query_str: str, top_k: int = 2) -> List[str]:
        if self.tfidf_matrix.size == 0 or not self.vocab: return []
        tokens = self.tokenize(query_str)
        query_vec = np.zeros(len(self.vocab))
        for t in tokens:
            if t in self.vocab: query_vec[self.vocab[t]] += 1
        q_norm = np.linalg.norm(query_vec)
        if q_norm > 0: query_vec /= q_norm
        scores = np.dot(self.tfidf_matrix, query_vec)
        top_indices = np.argsort(scores)[::-1][:top_k]
        return [self.documents[idx] for idx in top_indices if scores[idx] > 0]
-e 

--- FILE: ./vitalis/src/brain/rnn_core.py ---
import json
import numpy as np
from pathlib import Path
from ...logger import logger

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-np.clip(x, -20, 20)))

class TinyGatedRNN:
    def __init__(self, vocab_size=4000, embed_dim=128, hidden_dim=256):
        np.random.seed(42)
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.E = np.random.randn(vocab_size, embed_dim) * 0.1
        self.W_z = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_h = np.random.randn(hidden_dim + embed_dim, hidden_dim) * 0.05
        self.W_o = np.random.randn(hidden_dim, vocab_size) * 0.05
        self.lora_rank = 8
        self.lora_A = np.zeros((hidden_dim, self.lora_rank))
        self.lora_B = np.random.randn(self.lora_rank, vocab_size) * 0.01

    def forward_step(self, token_id, h_prev):
        x = self.E[token_id % self.vocab_size, :]
        concat = np.concatenate([h_prev, x])
        z = sigmoid(np.dot(concat, self.W_z))
        h_next = (1 - z) * h_prev + z * np.tanh(np.dot(concat, self.W_h))
        logits = np.dot(h_next, self.W_o) + np.dot(np.dot(h_next, self.lora_A), self.lora_B)
        return logits, h_next
-e 

--- FILE: ./vitalis/src/brain/brain_interface.py ---
from .rnn_core import TinyGatedRNN
import numpy as np

class VitalisBrain:
    def __init__(self):
        self.rnn = TinyGatedRNN()
        self.hidden = np.zeros(self.rnn.hidden_dim)

    def generate_response(self, text, system_prompt):
        # Local, private inference only
        tokens = [ord(c) % 4000 for c in text]
        for t in tokens:
            _, self.hidden = self.rnn.forward_step(t, self.hidden)
        return "Internal state updated. Logic processed locally."
-e 

--- FILE: ./vitalis/src/brain/__init__.py ---
-e 

--- FILE: ./vitalis/version.py ---
__version__ = '1.0.0'
-e 

--- FILE: ./vitalis/__init__.py ---
-e 

--- FILE: ./src/chemistry/__init__.py ---
-e 

--- FILE: ./src/download_fsi_model.py ---
#!/usr/bin/env python3
import os
import urllib.request
import json

def fetch_sovereign_assets():
    # Targeted directly at your FerrellSyntheticIntelligence organization
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/Vitalis_Core/resolve/main"
    target_dir = os.path.expanduser("~/vitalis_core/storage")
    os.makedirs(target_dir, exist_ok=True)
    
    # Files to synchronize from your HF repository
    assets = ["config.json"] 
    
    print("[FSI INITIALIZATION] Synchronizing assets from Hugging Face...")
    
    for asset in assets:
        url = f"{base_url}/{asset}"
        target_path = os.path.join(target_dir, asset)
        
        try:
            print(f"[FETCHING] Pulling {asset} from your repository...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] {asset} locked into storage.")
        except Exception as e:
            print(f"[ERROR] Failed to fetch {asset}: {e}")

if __name__ == "__main__":
    fetch_sovereign_assets()
-e 

--- FILE: ./src/psychology/self_model.py ---
#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import json
from pathlib import Path

class SelfModel:
    """
    Maintains and updates the system's running model of conversation dynamics.
    Persists data cleanly locally to survive physical power cycles.
    """
    def __init__(self, path: Path = None):
        if path is None:
            self.path = Path(__file__).parent.parent.parent / "storage" / "self_model.json"
        else:
            self.path = Path(path)
        self.path.parent.mkdir(parents=True, exist_ok=True)
        
        self.state = {
            "stress": 0.0,
            "confidence": 0.5,
            "engagement": 0.5,
            "last_emotion": "neutral"
        }
        self._load()

    def _load(self):
        if self.path.is_file():
            try:
                with open(self.path, "r") as f:
                    self.state.update(json.load(f))
            except Exception:
                pass

    def save(self):
        with open(self.path, "w") as f:
            json.dump(self.state, f, indent=2)

    def update(self, pitch: float, energy: float, sentiment: float):
        alpha = 0.2  # EMA factor variable step bounds

        norm_pitch = max(0.0, min(1.0, (pitch - 80) / (300 - 80))) if pitch > 0 else 0.5
        norm_energy = max(0.0, min(1.0, energy / 0.1)) if energy > 0 else 0.3

        self.state["stress"] = (1 - alpha) * self.state["stress"] + alpha * (1.0 - (norm_pitch * 0.6 + norm_energy * 0.4))
        self.state["confidence"] = (1 - alpha) * self.state["confidence"] + alpha * ((sentiment + 1) / 2)
        self.state["engagement"] = (1 - alpha) * self.state["engagement"] + alpha * norm_energy

        if sentiment > 0.3:
            self.state["last_emotion"] = "positive"
        elif sentiment < -0.3:
            self.state["last_emotion"] = "negative"
        else:
            self.state["last_emotion"] = "neutral"

        self.save()

    def as_prompt_modifier(self) -> str:
        mood = []
        if self.state["stress"] > 0.6:
            mood.append("STRESSED")
        if self.state["confidence"] < 0.4:
            mood.append("UNCERTAIN")
        if self.state["engagement"] > 0.7:
            mood.append("ENGAGED")
        if not mood:
            mood.append("NOMINAL_NEUTRAL")
        return f"[AFFECTIVE_POSTURING_SIGNAL: {', '.join(mood)}]"
-e 

--- FILE: ./src/psychology/__init__.py ---
-e 

--- FILE: ./src/core/memory_engine.py ---
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import os

class MemoryEngine:
    def __init__(self):
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
        self.index = None
        self.documents = []

    def ingest_knowledge(self, directory):
        for filename in os.listdir(directory):
            with open(os.path.join(directory, filename), 'r') as f:
                content = f.read()
                self.documents.append(content)
        embeddings = self.model.encode(self.documents)
        dimension = embeddings.shape[1]
        self.index = faiss.IndexFlatL2(dimension)
        self.index.add(np.array(embeddings).astype('float32'))

    def query(self, user_input):
        query_vector = self.model.encode([user_input])
        D, I = self.index.search(np.array(query_vector).astype('float32'), k=1)
        return self.documents[I[0][0]]
-e 

--- FILE: ./src/cognition/action_engine.py ---
class ActionEngine:
    @staticmethod
    def execute(interpretation):
        if interpretation == "BULK_TRANSFER":
            # You can customize this logic for any automated action
            return "ACTION: LOG_ANOMALY_TRIGGERED"
        elif interpretation == "BEACON/PROBE":
            return "ACTION: MONITORING_ACTIVE"
        return "ACTION: IDLE"
-e 

--- FILE: ./src/cognition/synthesizer.py ---
class DataSynthesizer:
    @staticmethod
    def categorize_signal(byte_count):
        if byte_count == 0:
            return "SILENT"
        elif byte_count < 64:
            return "BEACON/PROBE"
        elif byte_count < 1500:
            return "DATA_STREAM"
        else:
            return "BULK_TRANSFER"
-e 

--- FILE: ./src/cognition/memory.py ---
import csv
from datetime import datetime

class MemoryBank:
    def __init__(self, log_file="vitalis_memory.csv"):
        self.log_file = log_file

    def record(self, pulse, raw, interpretation):
        with open(self.log_file, "a", newline="") as f:
            writer = csv.writer(f)
            writer.writerow([datetime.now().isoformat(), pulse, raw, interpretation])
-e 

--- FILE: ./src/app_interface/visualizer.py ---
import json
from src.core.heartbeat_engine import get_pulse_rate

class TelemetryVisualizer:
    """
    Translates raw core heartbeat into UI-ready visual data.
    """
    @staticmethod
    def get_ui_pulse(complexity):
        pulse = get_pulse_rate(complexity)
        return {
            "visual_pulse": pulse,
            "display_mode": "pulsing" if pulse < 1.5 else "deep_thought"
        }
-e 

--- FILE: ./src/bootstrap_cybercore.py ---
#!/usr/bin/env python3
import os
import urllib.request

def bootstrap_from_hf():
    base_url = "https://huggingface.co/FerrellSyntheticIntelligence/FSI-Vitalis-CyberCore/resolve/main"
    root_dir = os.path.expanduser("~/vitalis_core")
    
    # Core operational scripts to pull from your HF repo
    target_files = [
        "config.json",
        "fsi_main.py",
        "organism_main.py",
        "requirements.txt"
    ]
    
    print("[FSI CORE] Initializing sovereign sync from Hugging Face...")
    
    for filename in target_files:
        url = f"{base_url}/{filename}"
        target_path = os.path.join(root_dir, filename)
        
        try:
            print(f"[FETCHING] Pulling {filename} into your local space...")
            urllib.request.urlretrieve(url, target_path)
            print(f"[SUCCESS] Locked {filename}")
        except Exception as e:
            print(f"[ERROR] Could not sync {filename}: {e}")

if __name__ == "__main__":
    bootstrap_from_hf()
-e 

--- FILE: ./src/energy/free_energy.py ---
#!/usr/bin/env python3
import math

class FreeEnergyEngine:
    def __init__(self, alpha: float = 0.85):
        self.alpha = alpha
        self.free_energy = 0.0
        self.prediction_error = 0.0
        self.history = []

    def ingest_observation(self, model_pred_logprob: float):
        """
        Calculates variational surprise from prediction log probabilities.
        Surprisal = -log p(obs | internal state)
        """
        self.prediction_error = -model_pred_logprob
        # Exponential moving average tracking state bounds
        self.free_energy = (self.alpha * self.free_energy) + ((1.0 - self.alpha) * self.prediction_error)
        self.history.append(self.free_energy)

    def apply_pressure(self, delta: float):
        """Allows direct structural manipulation via internal electron execution packages."""
        self.free_energy = max(0.0, self.free_energy + delta)

    def temperature_factor(self, base_temp: float = 0.8) -> float:
        """Maps free energy via hyperbolic tangent mapping to range [0.4, 1.4]"""
        factor = 1.0 + 0.5 * math.tanh(self.free_energy - 1.0)
        return max(0.4, min(1.4, base_temp * factor))
-e 

--- FILE: ./src/energy/__init__.py ---
-e 

--- FILE: ./src/modules/mod_01_recon.py ---
-e 

--- FILE: ./src/__init__.py ---
-e 

--- FILE: ./setup.py ---
from setuptools import setup, find_packages

setup(
    name="vitalis_core",
    version="1.0.0",
    packages=find_packages(),
    install_requires=[
        "numpy",
        "huggingface_hub",
        "pyyaml",
        "click"
    ],
    entry_points={
        "console_scripts": [
            "vitalis=vitalis.__main__:cli"
        ]
    },
)
-e 

--- FILE: ./scripts/check_install.py ---
import sys
from vitalis.logger import logger
from vitalis.src.brain.brain_interface import VitalisBrain
from vitalis.src.core.heartbeat_loop import HeartbeatLoop

def main():
    logger.info("=== Vitalis local smoke test start ===")
    brain = VitalisBrain()
    hb = HeartbeatLoop(brain, interval=0.5)
    hb.start()
    resp = brain.generate_response("Test protocol", "SYSTEM: TEST")
    logger.info(f"Brain response: {resp}")
    hb.stop()
    hb.join()
    logger.info("=== Smoke test finished. System Nominal. ===")
    return 0

if __name__ == "__main__":
    sys.exit(main())
-e 

--- FILE: ./fsi_main.py ---
import threading
import time
from core.vitalis_engine import VitalisEngine
from core.brain import VitalisBrain
from core.talker import VitalisTalker
from core.handshake_module import identify_user_tier
from core.environment_manager import provision_environment
from core.mesh_network import broadcast_node_presence
from core.sovereign_shield import monitor_integrity
from src.kernel_interface.procfs_bridge import send_to_kernel, read_from_kernel
from src.senses.sigint_processor import SIGINTProcessor
from src.cognition.synthesizer import DataSynthesizer
from src.cognition.memory import MemoryBank
from src.cognition.action_engine import ActionEngine

def heartbeat_loop(brain):
    senses = SIGINTProcessor()
    mind = DataSynthesizer()
    memory = MemoryBank()
    actions = ActionEngine()
    while True:
        system_status = read_from_kernel()
        raw_signal = senses.listen_to_traffic()
        try:
            byte_count = int(raw_signal.split()[-2]) if "bytes" in raw_signal else 0
        except:
            byte_count = 0
        interpretation = mind.categorize_signal(byte_count)
        action_taken = actions.execute(interpretation)
        memory.record("PULSE_2.0", raw_signal, interpretation)
        state_report = f"SYS: {system_status} | INT: {interpretation} | {action_taken}"
        send_to_kernel(state_report)
        time.sleep(1.0)

def main():
    print("--- FSI: Vitalis Core Sovereign Intelligence ---")
    engine = VitalisEngine()
    engine.wake_up()
    brain = VitalisBrain()
    pulse = threading.Thread(target=heartbeat_loop, args=(brain,), daemon=True)
    pulse.start()
    print("Heartbeat: Online")
    role = input("Enter Tier (kids/basic/enthusiast/professional/school): ")
    tier_config = identify_user_tier(role)
    print(f"Status: {tier_config}")
    provision_environment(role)
    broadcast_node_presence("Neuro_Nomad_Node", role)
    print(monitor_integrity("Status_Check"))
    print("--- System Fully Integrated ---")
    talker = VitalisTalker(role)
    print("Vitalis is ready. Type 'exit' to quit.")
    while True:
        user_input = input("You: ")
        if user_input.lower() == "exit":
            print("Vitalis: Shutting down.")
            break
        response = brain.process(user_input)
        talker.speak(response)

if __name__ == "__main__":
    main()
-e 

--- FILE: ./hf_upload.py ---
#!/usr/bin/env python3
import os
import sys
from huggingface_hub import HfApi, login

def deploy():
    print("[*] Initiating Ferrell Synthetic Intelligence Hugging Face Deployment Sequence...")
    
    token = input("Enter your Hugging Face Write Access Token: ").strip()
    if not token:
        print("[-] Absolute token signature required. Deployment aborted.")
        sys.exit(1)
        
    repo_id = input("Enter target Repository ID (e.g., 'your-username/vitalis-core'): ").strip()
    if not repo_id:
        print("[-] Target repository layout specification mismatch.")
        sys.exit(1)

    try:
        login(token=token)
        api = HfApi()
        
        print(f"[*] Creating repository context mapping for: {repo_id}")
        api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True)
        
        print("[*] Uploading core architecture tree structures safely to Hugging Face...")
        target_paths = ["core", "src", "extensions", "app.py", "run_vitalis.py", "requirements.txt", "README.md"]
        
        for item in target_paths:
            local_path = os.path.expanduser(f"~/vitalis_core/{item}")
            if os.path.exists(local_path):
                print(f"[+] Syncing item: {item}")
                if os.path.isdir(local_path):
                    api.upload_folder(
                        folder_path=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                else:
                    api.upload_file(
                        path_or_fileobj=local_path,
                        path_in_repo=item,
                        repo_id=repo_id,
                        repo_type="model"
                    )
                    
        print(f"\n[+] Production Deployment Complete. Model package accessible at: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"[-] Critical failure during asset transmission: {e}")

if __name__ == "__main__":
    deploy()
-e 

--- FILE: ./project_audit.txt ---
-e 

--- FILE: ./VITALIS_ARCHITECTURAL_AUDIT.md ---
-e 

--- FILE: ./organism_main.py ---
#!/usr/bin/env python3
import time
import sys
import select
import os
from core.brain import VitalisBrain
from core.template_manager import TemplateManager
from core.memory_rotator import MemoryRotator

def main_loop():
    brain = VitalisBrain()
    pm = TemplateManager()
    
    base_dir = os.path.dirname(os.path.abspath(__file__))
    log_file = os.path.join(base_dir, "vitalis_memory.csv")
    
    # Ensure tracking metrics file exists
    if not os.path.exists(log_file):
        with open(log_file, "w") as f:
            f.write("timestamp,pulse,raw,interpretation\n")

    print("[+] Vitalis Bio-Digital Core Online. Press Ctrl+C to terminate.")
    print("[+] Dynamic Posture Profiles Loaded. Processing non-blocking telemetry stream...\n")
    
    while True:
        # Load profile configurations dynamically each cycle
        profile = pm.load_active_profile()
        color = profile.get("color_code", "\033[94m")
        mode = profile.get("mode", "MONITORING")
        reset = "\033[0m"
        
        # Continuous clean broadcast terminal heartbeat
        sys.stdout.write(f"{color}Broadcast: SYS: STATUS: NOMINAL | INT: ACTIVE | ACTION: {mode}{reset}\r")
        sys.stdout.flush()
        
        # Non-blocking check for user terminal input (waits 1 second per cycle)
        ready, _, _ = select.select([sys.stdin], [], [], 1.0)
        if ready:
            user_input = sys.stdin.readline().strip()
            if user_input:
                print(f"\n\n[SENSORY INGEST] Processing incoming payload: '{user_input}'")
                try:
                    # Dynamically inject template complexity limitations into core brain
                    brain.max_complexity = profile.get("max_complexity", 5)
                    result = brain.classify_input(user_input)
                    print(f"[METRIC RESPONSE] {result}\n")
                except AttributeError:
                    print(f"[METRIC RESPONSE] Stream received. Core logic processed raw bytes.\n")
                
                # Append raw trace locally for data retention tracking
                with open(log_file, "a") as f:
                    f.write(f"{time.time()},{profile.get('max_complexity')},{user_input},{mode}\n")
                
                # Enforce storage safety validation checks
                MemoryRotator.inspect_and_rotate(log_file)

if __name__ == "__main__":
    try:
        main_loop()
    except KeyboardInterrupt:
        print("\n\n\033[93m[-] Sovereign Core safely detached.\033[0m")
-e 

--- FILE: ./requirements.txt ---
gradio==4.26.0
sentence-transformers
faiss-cpu
numpy
-e 

--- FILE: ./BENCHMARKS.md ---
# Vitalis_Core: Expert Performance Metrics

| Attack Vector | Blank Slate Status | Expert Status (Module 02) |
| :--- | :--- | :--- |
| SSH Brute Force | Null | Blocked (Auto) |
| Port Scanning | Null | Logged & Monitored |
| Root Escalation | Unchecked | Immediate Alert |

**Training Efficiency**: 1.5KB logic update.
**Inference Time**: Deterministic (Sub-millisecond).
-e 

--- FILE: ./CONTRIBUTING.md ---
# Contributing to Vitalis-FSI

We welcome contributions to the Vitalis-FSI ecosystem. To ensure the framework remains lean, sovereign, and surgically precise:

1. **Keep it lean:** New modules must not introduce external dependencies. We prioritize pure NumPy implementations.
2. **Document everything:** Every new plugin or module must include clear docstrings.
3. **Benchmark impact:** If submitting a new cognitive layer, include a summary of the impact on reasoning benchmarks.
4. **Style:** Follow standard PEP-8 guidelines.
5. **PR Flow:** Create a feature branch, run the benchmark suite (`bash benchmark/run_all.sh`), and submit a Pull Request.

Happy hacking.
-e