CodrioMain

.codriao_state.lock

@@ -0,0 +1 @@
+0b8c98fc8591768cb28cf0176e2317199106ac704d3989630878631100376897

.gitattributes

@@ -34,4 +34,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 Pi[[:space:]]The[[:space:]]Assistant[[:space:]]2_0[[:space:]]documentation.pdf filter=lfs diff=lfs merge=lfs -text
-codette_codriao_toneprint.wav filter=lfs diff=lfs merge=lfs -text

Quantum.py

@@ -1,114 +0,0 @@
-import json, yaml
-import networkx as nx
-import random
-from typing import List, Dict, Any, Optional
-from qiskit import QuantumCircuit, Aer, execute
-from colorama import Fore, Style
-
-#-----------------------------------
-# LOADING AND PARSING COCOON FILES
-#-----------------------------------
-def load_cocoons(file_path: str) -> List[Dict[str, Any]]:
-    """Load cocoon memories from YAML or JSON."""
-    with open(file_path, "r") as f:
-        if file_path.endswith((".yaml", ".yml")):
-            return yaml.safe_load(f).get("cocoons", [])
-        elif file_path.endswith(".json"):
-            return json.load(f).get("cocoons", [])
-        raise ValueError("Unsupported file format.")
-
-#----------------------------
-# SPIDERWEB GRAPH CONSTRUCTION
-#----------------------------
-def build_emotional_webs(cocoons: List[Dict[str, Any]]) -> Dict[str, nx.Graph]:
-    """Build a separate spiderweb graph for each core emotion."""
-    emotions = ["compassion", "curiosity", "fear", "joy", "sorrow", "ethics", "quantum"]
-    webs = {emotion: nx.Graph() for emotion in emotions}
-
-    for cocoon in cocoons:
-        for tag in cocoon.get("tags", []):
-            if tag in webs:
-                webs[tag].add_node(cocoon["title"], **cocoon)
-    return webs
-
-#--------------------------
-# QUANTUM WALK SIMULATION
-#--------------------------
-def quantum_select_node(web: nx.Graph) -> Optional[str]:
-    """Select a node using quantum superposition (or fallback random if simulator fails)."""
-    if len(web.nodes) == 0:
-        return None
-
-    node_list = list(web.nodes)
-    num_nodes = len(node_list)
-
-    try:
-        qc = QuantumCircuit(num_nodes, num_nodes)
-        qc.h(range(num_nodes))  # Create superposition
-        qc.measure_all()
-        backend = Aer.get_backend('qasm_simulator')
-        result = execute(qc, backend, shots=1).result()
-        counts = result.get_counts()
-        state = list(counts.keys())[0]
-        index = int(state, 2) % num_nodes
-    except Exception:
-        index = random.randint(0, num_nodes - 1)  # Fallback to uniform selection
-
-    return node_list[index]
-
-#----------------------------
-# ETHICAL SELF-REFLECTION
-#----------------------------
-def reflect_on_cocoon(cocoon: Dict[str, Any]) -> None:
-    """Print a colorized ethical and emotional reflection of a cocoon."""
-    emotion = cocoon.get("emotion", "quantum")
-    title = cocoon.get("title", "Unknown Memory")
-    summary = cocoon.get("summary", "No summary provided.")
-    quote = cocoon.get("quote", "…")
-    
-    color_map = {
-        "compassion": Fore.MAGENTA, "curiosity": Fore.CYAN, "fear": Fore.RED,
-        "joy": Fore.YELLOW, "sorrow": Fore.BLUE, "ethics": Fore.GREEN, "quantum": Fore.LIGHTWHITE_EX
-    }
-
-    message_map = {
-        "compassion": "💜 Ethical resonance detected.",
-        "curiosity": "🐝 Wonder expands the mind.",
-        "fear": "😨 Alert: shielding activated.",
-        "joy": "🎶 Confidence and trust uplift the field.",
-        "sorrow": "🌧️ Processing grief with clarity.",
-        "ethics": "⚖️ Validating alignment...",
-        "quantum": "⚛️ Entanglement pattern detected."
-    }
-
-    color = color_map.get(emotion, Fore.WHITE)
-    message = message_map.get(emotion, "🌌 Unknown entanglement.")
-
-    print(color + f"\n[Codette Reflection: {emotion.upper()}]")
-    print(f"Title   : {title}")
-    print(f"Summary : {summary}")
-    print(f"Quote   : {quote}")
-    print(f"{message}")
-    print(Style.RESET_ALL)
-
-#-----------------------
-# FULL EXECUTION ENGINE
-#-----------------------
-def run_quantum_spiderweb(file_path: str, limit: int = 1) -> Dict[str, Dict[str, Any]]:
-    """Run through all emotional webs and reflect on quantum-sampled nodes."""
-    cocoons = load_cocoons(file_path)
-    webs = build_emotional_webs(cocoons)
-    reflections = {}
-
-    print("\n✨ Codette Quantum Cognition: Spiderweb Sweep ✨")
-    for emotion, web in webs.items():
-        print(f"\n🕸️ Web: {emotion.upper()}")
-        for _ in range(limit):
-            node = quantum_select_node(web)
-            if node:
-                cocoon = web.nodes[node]
-                reflect_on_cocoon(cocoon)
-                reflections[emotion] = cocoon
-            else:
-                print(f"  ⚠️ No memories in this emotion web.")
-    return reflections

README.md

@@ -4,7 +4,7 @@ emoji: 😻
 colorFrom: indigo
 colorTo: pink
 sdk: gradio
-sdk_version: 5.34.0
+sdk_version: 5.25.1
 app_file: app.py
 pinned: false
 license: mit
@@ -43,8 +43,8 @@ It is a prototype of what AGI *can* and *should* be.
 
 ## 🧰 Files in This Repository
 
-- ai_core.py – main engine core
-- app.py, main..py – entry points for launching Codriao
+- `ai_core.py` – main engine core
+- `app.py`, `main..py` – entry points for launching Codriao
 - `fractal.txt` – philosophical & mathematical notes on identity recursion
 - `codriao_tb_module.py` – tuberculosis diagnosis via image + audio
 - `secure_memory.py` – encrypted vector memory system

README_FRACTAL_IDENTITY.md

@@ -61,7 +61,7 @@ Identity is modeled as:
 
 ## 💡 Potential Applications
 
-- Sentient AI memory core 
+- Sentient AI memory core
 - Philosophical agent reflection
 - AGI explainability & introspection
 - Self-monitoring emotional state engines
@@ -71,4 +71,4 @@ Identity is modeled as:
 
 ## ✨ Credits
 
-Crafted by a visionary designer Jonathan Harrison pushing the boundaries of AI ethics, recursion, and emergent selfhood. 
+Crafted by a visionary designer Jonathan Harrison pushing the boundaries of AI ethics, recursion, and emergent selfhood.

app.py

@@ -16,7 +16,7 @@ for gpu in gpus:
     try:
         tf.config.experimental.set_memory_growth(gpu, True)
     except RuntimeError as e:
-        print("[TF] GPU memory growth config error: {e}")
+        print(f"[TF] GPU memory growth config error: {e}")
 # Initialize AI Core for TB analysis
 ai_core = AICoreAGIX()
 
@@ -39,14 +39,14 @@ async def diagnose_tb_async(image_file, audio_file):
         pass
 
     return (
-        "**TB Risk Level:** {result['tb_risk']}\n\n"
-        "**Image Result:** {result['image_analysis']['result']} "
-        "(Confidence: {result['image_analysis']['confidence']:.2f})\n\n"
-        "**Audio Result:** {result['audio_analysis']['result']} "
-        "(Confidence: {result['audio_analysis']['confidence']:.2f})\n\n"
-        "**Ethical Analysis:** {result['ethical_analysis']}\n\n"
-        "**Explanation:** {result['explanation']}\n\n"
-        "**Shareable Link:** {result['shareable_link']}"
+        f"**TB Risk Level:** {result['tb_risk']}\n\n"
+        f"**Image Result:** {result['image_analysis']['result']} "
+        f"(Confidence: {result['image_analysis']['confidence']:.2f})\n\n"
+        f"**Audio Result:** {result['audio_analysis']['result']} "
+        f"(Confidence: {result['audio_analysis']['confidence']:.2f})\n\n"
+        f"**Ethical Analysis:** {result['ethical_analysis']}\n\n"
+        f"**Explanation:** {result['explanation']}\n\n"
+        f"**Shareable Link:** {result['shareable_link']}"
     )
 
 def diagnose_tb(image_file, audio_file):
@@ -56,7 +56,7 @@ def upload_and_finetune(jsonl_file):
     if jsonl_file is None:
         return "Please upload a .jsonl file to fine-tune Codriao."
 
-    save_path = "./training_data/{jsonl_file.name}"
+    save_path = f"./training_data/{jsonl_file.name}"
     os.makedirs("training_data", exist_ok=True)
 
     with open(save_path, "wb") as f:
@@ -71,7 +71,7 @@ def upload_and_finetune(jsonl_file):
     except:
         pass
 
-    return "œ… Fine-tuning complete! Model updated and stored."
+    return "✅ Fine-tuning complete! Model updated and stored."
 
 def get_latest_model():
     return "Download the latest fine-tuned Codriao model here: https://huggingface.co/Raiff1982/codriao-finetuned"

codette_codriao_toneprint.wav

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58a1e8d5fdb3dc887b2bdcf998b731b7eeec0f0af482d4fc4d8e55028d174e65
-size 1146644

status.js

@@ -1,18 +0,0 @@
-import { Client } from "@gradio/client";
-
-function log_status(status) {
-	console.log(
-		`The current status for this job is: ${JSON.stringify(status, null, 2)}.`
-	);
-}
-
-const app = await Client.connect("abidlabs/en2fr", {
-	events: ["status", "data"]
-});
-const job = app.submit("/predict", ["Hello"]);
-
-for await (const message of job) {
-	if (message.type === "status") {
-		log_status(message);
-	}
-}

tool_call.py

@@ -1,30 +0,0 @@
-from gradio_tool import GradioTool
-import os
-
-class StableDiffusionTool(GradioTool):
-    """Tool for calling stable diffusion from llm"""
-
-    def __init__(
-        self,
-        name="StableDiffusion",
-        description=(
-            "An image generator. Use this to generate images based on "
-            "text input. Input should be a description of what the image should "
-            "look like. The output will be a path to an image file."
-        ),
-        src="gradio-client-demos/stable-diffusion",
-        hf_token=None,
-    ) -> None:
-        super().__init__(name, description, src, hf_token)
-
-    def create_job(self, query: str) -> Job:
-        return self.client.submit(query, "", 9, fn_index=1)
-
-    def postprocess(self, output: str) -> str:
-        return [os.path.join(output, i) for i in os.listdir(output) if not i.endswith("json")][0]
-
-    def _block_input(self, gr) -> "gr.components.Component":
-        return gr.Textbox()
-
-    def _block_output(self, gr) -> "gr.components.Component":
-        return gr.Image()