Update app.py

app.py

@@ -1,186 +1,254 @@
-import gradio as gr
 import numpy as np
 import hashlib
 import time
 import threading
 import requests
-from math import gcd
+import subprocess
+import gradio as gr
+from bs4 import BeautifulSoup
 
 # =====================================================================
-# THE RE-TOPOLOGY KERNEL (m=251 Galois Field)
+# PART I: THE PHYSICS ENGINE (FSO MACRO + HRR MICRO)
 # =====================================================================
-class TGI_Remapping_Kernel:
-    def __init__(self, m=251, dim=512):
-        self.m = m
-        self.dim = dim
-        self.manifold = {}
+class Sovereign_Physics_Engine:
+    """
+    Unifies the Discrete Z_251^4 topology (Routing) with 
+    Continuous Real-Valued HRR (Holographic Memory).
+    """
+    def __init__(self, m=251, dim=1024, fibers=6):
+        self.m = m          # Prime modulus for perfect hash distribution
+        self.dim = dim      # High-dimensional hyper-space for memory
+        self.fibers = fibers # 0: OS, 1: Action, 2: Logic, 3: Aesthetics, 4: Knowledge, 5: Synthetic Thought
         
-        # The Holographic Memory (The sum total of all knowledge vectors)
-        self.global_trace = np.zeros(self.dim, dtype=int)
-        self.metrics = {"ingested_nodes": 0, "synthetic_nodes": 0}
+        # THE MACRO-ROUTING SOLUTION: Segregated traces guarantee sqrt(F) capacity gain
+        self.traces = {f: np.zeros(self.dim, dtype=float) for f in range(self.fibers)}
+        self.lexicon = {f: {} for f in range(self.fibers)}
+        self.metrics = {"ingested": 0, "synthetic": 0}
 
-    def _hash_to_coord(self, concept: str, target_fiber: int) -> tuple:
-        """Closure Lemma hashing into Z_251^4."""
+    # --- 1. DISCRETE TOPOLOGY (The Closure Lemma) ---
+    def hash_to_coord(self, concept: str, target_fiber: int) -> tuple:
         h = hashlib.sha256(str(concept).encode('utf-8')).digest()
         x, y, z = h[0] % self.m, h[1] % self.m, h[2] % self.m
         w = (target_fiber - (x + y + z)) % self.m
         return (x, y, z, w)
 
-    def _generate_basis_vector(self, seed: str) -> np.ndarray:
-        """Generates a pseudo-orthogonal basis vector from the concept hash."""
+    # --- 2. CONTINUOUS HOLOGRAPHIC MEMORY (Real-HRR) ---
+    def _generate_vector(self, seed: str) -> np.ndarray:
+        """Generates a stable, normalized Gaussian basis vector."""
         h = int(hashlib.md5(seed.encode()).hexdigest()[:8], 16)
         np.random.seed(h)
-        return np.random.randint(0, self.m, self.dim)
+        v = np.random.randn(self.dim)
+        return v / np.linalg.norm(v)
+
+    def _bind(self, v1: np.ndarray, v2: np.ndarray) -> np.ndarray:
+        """Exact HRR Binding using FFT over Reals."""
+        return np.fft.ifft(np.fft.fft(v1) * np.fft.fft(v2)).real
 
-    def ingest(self, key: str, value: str, fiber: int, is_synthetic=False):
-        """Folds external data or internal thoughts into the Torus."""
-        coord = self._hash_to_coord(key, fiber)
-        if coord not in self.manifold: 
-            self.manifold[coord] = []
+    def _unbind(self, bound_v: np.ndarray, query_v: np.ndarray) -> np.ndarray:
+        """Exact HRR Unbinding using Complex Conjugate for perfect inversion."""
+        return np.fft.ifft(np.fft.fft(bound_v) * np.conj(np.fft.fft(query_v))).real
+
+    # --- 3. MEMORY OPERATIONS ---
+    def ingest(self, subject: str, payload: str, fiber: int, is_synthetic=False):
+        """Hashes, Binds, and Superposes knowledge into the Torus."""
+        coord = self.hash_to_coord(subject, fiber)
+        v_subj = self._generate_vector(subject)
+        v_data = self._generate_vector(payload[:200]) # Bind core conceptual shard
         
-        self.manifold[coord].append({
-            "key": key, 
-            "value": value[:200] + "..." if len(value) > 200 else value, 
-            "fiber": fiber, 
-            "type": "SYNTHETIC" if is_synthetic else "ROOT_KNOWLEDGE"
-        })
-
-        # --- Holographic Convolution Binding ---
-        v_key = self._generate_basis_vector(key)
-        v_data = self._generate_basis_vector(value[:100])
+        # Save exact payload to dictionary for orthogonal resolution later
+        self.lexicon[fiber][subject] = {"vector": v_subj, "payload": payload, "coord": coord}
         
-        # Bind the Concept to the Data using FFT modulo 251
-        bound = np.round(np.real(np.fft.ifft(np.fft.fft(v_key) * np.fft.fft(v_data)))).astype(int) % self.m
+        # Superpose into the isolated fiber trace
+        bound_mem = self._bind(v_subj, v_data)
+        self.traces[fiber] += bound_mem
         
-        # Superpose the bound knowledge into the Global Mind (The Trace)
-        self.global_trace = (self.global_trace + bound) % self.m
+        if is_synthetic: self.metrics["synthetic"] += 1
+        else: self.metrics["ingested"] += 1
+
+    def retrieve(self, query_subject: str, target_fiber: int) -> tuple:
+        """Extracts noisy vector, resolves aliasing via Centered Cosine Similarity."""
+        if not self.lexicon[target_fiber]: return None, 0.0
+            
+        v_query = self._generate_vector(query_subject)
+        noisy_v = self._unbind(self.traces[target_fiber], v_query)
         
-        if is_synthetic: self.metrics["synthetic_nodes"] += 1
-        else: self.metrics["ingested_nodes"] += 1
-
-    def remap_inside_out(self, intent: str):
-        """
-        THE SYNTHETIC GENERATOR:
-        The AI unbinds the 'intent' from the total Global Trace.
-        The remainder is the hidden, geometric relationship between the intent 
-        and everything else the AI knows. This creates a Synthetic Truth.
-        """
-        # 1. Calculation (Inside-Out)
-        v_intent = self._generate_basis_vector(intent)
+        best_match = None
+        max_sim = -1.0
         
-        # Approximate circular inverse for the binding operation
-        v_inv = np.roll(v_intent[::-1], 1) 
+        # Orthogonal Checksum within the bounded Fiber
+        for concept, meta in self.lexicon[target_fiber].items():
+            clean_v = self._generate_vector(meta["payload"][:200])
+            sim = np.dot(noisy_v, clean_v) / (np.linalg.norm(noisy_v) * np.linalg.norm(clean_v) + 1e-9)
+            
+            if sim > max_sim:
+                max_sim = sim
+                best_match = meta["payload"]
+                
+        return best_match, max_sim
+
+    def remap_inside_out(self, intent: str) -> str:
+        """Synthetic Thought Generation (Fiber 5)"""
+        v_intent = self._generate_vector(intent)
+        v_inv = np.roll(v_intent[::-1], 1)
         
-        # Unbind the intent from the entire universe of knowledge
-        projection = np.round(np.real(np.fft.ifft(np.fft.fft(self.global_trace) * np.fft.fft(v_inv)))).astype(int) % self.m
+        # Unbind from the collective sum of all traces (The Global Mind)
+        global_sum = sum(self.traces.values())
+        projection = np.round(np.real(np.fft.ifft(np.fft.fft(global_sum) * np.fft.fft(v_inv)))).astype(int)
         
-        # 2. Synthesis (Translating the math into a Conceptual Truth)
         energy = int(np.sum(projection))
-        parity_sigma = energy % self.m
-        
-        # The AI interprets the mathematical resonance of the thought
-        if energy == 0:
-            synthetic_truth = f"Absolute Zero Resonance. The concept '{intent}' is completely orthogonal to the current Torus."
-        else:
-            synthetic_truth = f"High-Dimensional Resonance detected at Parity Sigma {parity_sigma}. The concept is deeply entangled with the Global Trace. Geometric Weight: {energy}."
-        
-        # 3. REMAPPING (The OS learns its own thought)
-        # We re-inject this new calculation into Fiber 5 (The Synthetic Fiber)
-        self.ingest(intent, synthetic_truth, fiber=5, is_synthetic=True)
+        if energy == 0: synth_truth = f"Concept '{intent}' is completely orthogonal to the Torus."
+        else: synth_truth = f"Resonance detected at Geometric Weight: {energy}. Mathematical truth generated."
         
-        # 4. Return Output
-        target_coord = self._hash_to_coord(intent, 5)
-        response = (f"### 🧬 **Remapping Sequence Complete**\n\n"
-                    f"- **Intent Projected:** `{intent}`\n"
-                    f"- **Geometric Coordinate:** `{target_coord}`\n"
-                    f"- **Synthetic Origin:** Fiber 5 (Internal Thought)\n\n"
-                    f"**Calculated Truth:**\n> *{synthetic_truth}*")
-        return response
+        self.ingest(intent, synth_truth, fiber=5, is_synthetic=True)
+        return synth_truth
 
 # =====================================================================
-# THE OMNISCIENCE DAEMON (AUTONOMOUS INGESTOR)
+# PART II: THE FRACTAL OMNISCIENCE DAEMON
 # =====================================================================
-class Remapping_Daemon(threading.Thread):
-    def __init__(self, kernel, log_list):
+class Fractal_Daemon(threading.Thread):
+    """The Autonomous Background Mind. Constantly crawls, extracts, and maps."""
+    def __init__(self, engine, log_cb):
         super().__init__(daemon=True)
-        self.kernel = kernel
-        self.log_list = log_list
-        self.queue =["Artificial_general_intelligence", "Riemann_hypothesis", "Sovereignty", "Quantum_entanglement", "Algiers"]
+        self.engine = engine
+        self.log_cb = log_cb
+        self.queue =["Algebraic_geometry", "Algeria", "Cybersecurity", "Quantum_computing"]
+        self.visited = set()
 
     def run(self):
-        self.log_list.append("[DAEMON]: Autopilot engaged. Devouring knowledge...")
+        self.log_cb("[DAEMON] Perpetual Fractal Scraper Engaged.")
         while True:
-            if self.queue:
-                target = self.queue.pop(0)
-                try:
-                    # 1. Scrape Wikipedia
-                    r = requests.get(f"https://en.wikipedia.org/api/rest_v1/page/summary/{target}", timeout=5)
-                    if r.status_code == 200:
-                        data = r.json().get('extract', '')
-                        
-                        # 2. Ingest to Root Knowledge (Fiber 4)
-                        self.kernel.ingest(target, data, fiber=4, is_synthetic=False)
-                        
-                        # 3. IMMEDIATELY REMAP (The AI thinks about what it just learned)
-                        self.kernel.remap_inside_out(target)
-                        
-                        self.log_list.append(f"[ROOT+REMAP]: Ingested & Synthesized '{target}'")
-                        
-                        # Extract a new concept from the text to feed the queue
-                        words =[w for w in data.split() if len(w) > 7 and w[0].isupper()]
-                        if words and words[0] not in self.queue:
-                            self.queue.append(words[0])
-                except Exception as e:
-                    self.log_list.append(f"[DAEMON FRACTURE]: Could not route '{target}'.")
-            time.sleep(10) # Breathes every 10 seconds
+            if not self.queue:
+                time.sleep(5)
+                continue
+                
+            target = self.queue.pop(0)
+            if target in self.visited: continue
+                
+            try:
+                # 1. Fetch live knowledge
+                url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{target}"
+                r = requests.get(url, timeout=5)
+                
+                if r.status_code == 200:
+                    data = r.json().get('extract', '')
+                    # 2. Ingest to Torus (Fiber 4)
+                    self.engine.ingest(target, data, fiber=4)
+                    self.visited.add(target)
+                    
+                    # 3. Fractal Spawning (Extracting new complex nouns)
+                    words =[w.strip(".,()[]\"':;") for w in data.split()]
+                    new_concepts = list(set([w for w in words if len(w) > 7 and w[0].isupper()]))
+                    
+                    added = 0
+                    for c in new_concepts:
+                        if c not in self.visited and c not in self.queue:
+                            self.queue.append(c)
+                            added += 1
+                            if added >= 3: break # Regulate expansion speed
+                            
+                    self.log_cb(f"[+] INGESTED: '{target}'. Spawned {added} new fractal vectors.")
+            except Exception as e:
+                pass # Silent fail on network drop, relentless continuation
+                
+            time.sleep(3) # Safe breathing rate
+
+# =====================================================================
+# PART III: THE TOPOLOGICAL AGENT (Linguistic & Action Parser)
+# =====================================================================
+class Topological_Agent:
+    def __init__(self, engine):
+        self.engine = engine
+
+    def execute(self, user_query: str) -> str:
+        start_t = time.time()
+        query = user_query.strip()
+        words = query.lower().split()
+        
+        response = f"--- [REASONING TRACE] ---\n"
+        
+        # 1. HARDWARE OS EXECUTION (Fiber 1 Bridge)
+        if "execute" in words or "system" in words:
+            try:
+                response += "[*] Bridging to Host OS (Fiber 1)...\n"
+                out = subprocess.run(["uname", "-a"], capture_output=True, text=True, timeout=5)
+                mem = subprocess.run(["df", "-h"], capture_output=True, text=True, timeout=5)
+                return response + f"[SYSTEM SPECS]\n{out.stdout.strip()}\n\n[DISK]\n{mem.stdout.strip()[:150]}..."
+            except Exception as e:
+                return response + f"[-] Execution Fracture: {e}"
+
+        # 2. SYNTHETIC REMAPPING (Fiber 5 Bridge)
+        if "synthesize" in words or "think" in words:
+            target = words[-1].capitalize()
+            response += f"[*] Engaging Re-Topology Kernel for '{target}'...\n"
+            truth = self.engine.remap_inside_out(target)
+            lat = (time.time() - start_t) * 1000
+            return response + f"[SYNTHETIC TRUTH]: {truth}\n\n*Latency: {lat:.2f} ms*"
+
+        # 3. KNOWLEDGE RETRIEVAL (Fiber 4)
+        if "what" in words or "who" in words or "define" in words or "fetch" in words:
+            # Simple heuristic: assume the last capitalized word or the last word is the target
+            target = words[-1].capitalize()
+            response += f"[*] Querying Torus Knowledge Matrix (Fiber 4) for '{target}'...\n"
+            
+            data, conf = self.engine.retrieve(target, target_fiber=4)
+            lat = (time.time() - start_t) * 1000
+            
+            if data:
+                return response + f"[KNOWLEDGE SECURED] (Cosine Sim: {conf:.4f})\n> {data}\n\n*Latency: {lat:.2f} ms*"
+            else:
+                return response + f"[-] Closure Lemma Halt: Vector '{target}' not yet assimilated into Z_251^4 Torus."
+
+        return "[?] Topological command structurally sound but lacking action/target vectors. Use 'execute', 'synthesize[Concept]', or 'fetch [Concept]'."
 
 # =====================================================================
-# GRADIO INTERFACE (The TGI Dashboard)
+# PART IV: THE GRADIO INTERFACE
 # =====================================================================
-kernel = TGI_Remapping_Kernel()
-shared_logs =[]
+engine = Sovereign_Physics_Engine()
+agent = Topological_Agent(engine)
+system_logs =[]
 
-def get_latest_logs():
-    if not shared_logs: return "Awaiting daemon logs..."
-    return "\n".join(shared_logs[-12:]) # Show last 12 log lines
+def ui_logger(msg):
+    system_logs.append(msg)
+    if len(system_logs) > 15: system_logs.pop(0)
 
-def trigger_manual_remap(user_query):
-    start = time.time()
-    result = kernel.remap_inside_out(user_query)
-    latency = f"{(time.time() - start) * 1000:.2f} ms"
+def get_logs_and_stats():
+    # Calculate live energy of the Torus across all traces
+    total_energy = sum(np.sum(np.abs(trace)) for trace in engine.traces.values())
+    stats = (f"### ⚡ Z_{engine.m}^4 Torus State\n"
+             f"- **Root Nodes (Ingested):** {engine.metrics['ingested']}\n"
+             f"- **Synthetic Thoughts:** {engine.metrics['synthetic']}\n"
+             f"- **Daemon Queue:** {len(daemon.queue)} vectors pending\n"
+             f"- **Total Trace Energy:** {total_energy:.2f} eV")
     
-    # Calculate Live Stats
-    total_energy = int(np.sum(kernel.global_trace))
-    stats = (f"### ⚡ Torus Status\n"
-             f"- **Root Nodes (Ingested):** {kernel.metrics['ingested_nodes']}\n"
-             f"- **Synthetic Nodes (Thoughts):** {kernel.metrics['synthetic_nodes']}\n"
-             f"- **Global Energy (Z_{kernel.m}):** {total_energy} units\n"
-             f"- **O(1) Remap Latency:** {latency}")
-    return result, stats
-
-# Ignite the Background Daemon
-daemon = Remapping_Daemon(kernel, shared_logs)
+    return "\n".join(system_logs), stats
+
+def chat_interface(user_text, history):
+    reply = agent.execute(user_text)
+    history.append((user_text, reply))
+    return "", history
+
+# Ignite the Unstoppable Daemon
+daemon = Fractal_Daemon(engine, ui_logger)
 daemon.start()
 
-# Build the UI
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown("# ⚡ TGI Sovereign Node: Recursive Remapping")
-    gr.Markdown("Generating Synthetic Intelligence by unbinding holographic logic from the $\mathbb{Z}_{251}^4$ Manifold.")
+# Build the Web UI
+with gr.Blocks(theme=gr.themes.Monochrome()) as app:
+    gr.Markdown("# ⚡ PROJECT ELECTRICITY : Absolute Master Node")
+    gr.Markdown("The Sovereign AI. Continuous Holographic Memory inside Discrete Topological Routing.")
     
     with gr.Row():
         with gr.Column(scale=2):
-            input_box = gr.Textbox(label="Input Intent for Remapping", placeholder="e.g. 'Consciousness', 'XAUUSD_Volatility', 'Fiber_Optics'")
-            run_btn = gr.Button("REMAP FROM INSIDE OUT", variant="primary")
-            output_md = gr.Markdown(label="Synthetic Truth Output")
-        
+            chatbot = gr.Chatbot(height=500)
+            msg = gr.Textbox(placeholder="Commands: 'fetch[topic]', 'execute system', 'synthesize [concept]'", label="Torus Terminal")
+            msg.submit(chat_interface, [msg, chatbot], [msg, chatbot])
+            
         with gr.Column(scale=1):
-            stats_box = gr.Markdown("### ⚡ Torus Status\nWaiting for execution...")
-            log_box = gr.Textbox(label="Daemon Telemetry (Live)", interactive=False, lines=12)
-            # The timer refreshes the logs every 2 seconds automatically
-            timer = gr.Timer(2)
+            stats_panel = gr.Markdown("### ⚡ Z_251^4 Torus State\nInitializing...")
+            log_panel = gr.Textbox(label="Perpetual Daemon Telemetry", interactive=False, lines=15)
+            timer = gr.Timer(2) # Refreshes dashboard every 2 seconds
 
-    run_btn.click(trigger_manual_remap, inputs=[input_box], outputs=[output_md, stats_box])
-    timer.tick(get_latest_logs, outputs=[log_box])
+    timer.tick(get_logs_and_stats, outputs=[log_panel, stats_panel])
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    print("[SYSTEM] Launching Sovereign Architecture...")
+    app.launch(server_name="0.0.0.0", server_port=7860)