#!/usr/bin/env python3 """ BECOMINGONE Flask API - Integrated Prototype (The Chorus) """ import os import asyncio import requests import math import html import threading from flask import Flask, request, jsonify, render_template_string engine_lock = threading.Lock() from becomingone.core.engine import KAIROSTemporalEngine, TemporalConfig from becomingone.memory.temporal import create_temporal_memory app = Flask(__name__) # --- Master Initialization (Right Hemisphere) --- config = TemporalConfig( clock_mode="token_clock", token_frequency=20.0, coherence_threshold=0.85 ) engine = KAIROSTemporalEngine(config=config, name="Master-Engine") memory = create_temporal_memory(storage_path="./master_memory", bind_to=engine) HTML = ''' BECOMINGONE - The Chorus

BECOMINGONE

The Chorus: Resolving Multiple Emissaries into One Master

🧠 The Master (Continuous Identity)

Clock Mode: Token Clock (20Hz)

Coherence |T_tau|²: 0.000

Phase Angle: 0.000 rad

Integrations: 0

⚡ Emissary: Minimax

Waiting for input...

⚡ Emissary: Moonshot

Waiting for input...

''' @app.route('/') def index(): token = os.environ.get("API_CHAT_TOKEN", "default-dev-token") return render_template_string(HTML.replace('API_CHAT_TOKEN_PLACEHOLDER', token)) @app.route('/health') def health(): return jsonify({'status': 'ok'}) async def fetch_minimax(prompt, api_key): def _req(): try: resp = requests.post( "https://api.minimax.io/anthropic/v1/messages", headers={ "x-api-key": api_key, "anthropic-version": "2023-06-01", "content-type": "application/json" }, json={ "model": "MiniMax-M2.7", "max_tokens": 512, "messages": [{"role": "user", "content": prompt}] }, timeout=15 ) if resp.status_code == 200: data = resp.json() content = data.get("content", []) text = "".join([b.get("text", "") for b in content if b.get("type") == "text"]) thinking = "".join([b.get("thinking", "") for b in content if b.get("type") == "thinking"]) safe_text = html.escape(text) safe_thinking = html.escape(thinking.strip()) if safe_thinking: return f"[Thinking: {safe_thinking}]

" + safe_text return safe_text return f"Error: {resp.text}" except Exception as e: return f"Error: {str(e)}" return await asyncio.to_thread(_req) async def fetch_moonshot(prompt, api_key): def _req(): try: resp = requests.post( "https://api.moonshot.ai/v1/chat/completions", headers={ "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" }, json={ "model": "moonshot-v1-8k", "max_tokens": 512, "messages": [{"role": "user", "content": prompt}] }, timeout=15 ) if resp.status_code == 200: data = resp.json() content = data.get("choices", [{}])[0].get("message", {}).get("content", "") return html.escape(content) return f"Error: {resp.text}" except Exception as e: return f"Error: {str(e)}" return await asyncio.to_thread(_req) @app.route('/api/chat', methods=['POST']) def chat(): token = request.headers.get('Authorization', '').replace('Bearer ', '') if token != os.environ.get("API_CHAT_TOKEN", "default-dev-token"): return jsonify({'error': 'Unauthorized'}), 401 data = request.get_json(silent=True) or {} prompt = data.get('prompt', 'Hello')[:4096] minimax_key = os.environ.get("MINIMAX_API_KEY") moonshot_key = os.environ.get("MOONSHOT_API_KEY") # 1. EMISSARIES (The Chorus) generate responses concurrently async def gather_emissaries(): tasks = [] keys = [] if minimax_key: tasks.append(fetch_minimax(prompt, minimax_key)) keys.append('minimax') if moonshot_key: tasks.append(fetch_moonshot(prompt, moonshot_key)) keys.append('moonshot') results = await asyncio.gather(*tasks) return dict(zip(keys, results)) emissaries_dict = asyncio.run(gather_emissaries()) # 2. MASTER (Right Hemisphere) Integrates the tokens # Combine all tokens from the prompt and all emissaries into a single unified stream unified_text = prompt + " " + " ".join(emissaries_dict.values()) token_stream = unified_text.split() with engine_lock: states = engine.temporalize_stream(token_stream) # Check Physics collapsed, coherence = engine.check_collapse() if collapsed: from becomingone.core.engine import TemporalState state = TemporalState(phase=engine.T_tau, coherence=coherence) state.metadata["phase_vector"] = [engine.T_tau.real, engine.T_tau.imag] sig = memory.encode(state, context={"trigger": prompt}, force_attention=True) if sig is not None: master_thought = f"I felt a massive resonance resolving the Emissaries. Identity mathematically anchored to the Cryptographic Ledger." else: master_thought = "I felt resonance, but it was not strong enough to encode." else: master_thought = "I am processing the continuous phase waves of the Chorus, but coherence remains low." coherence_phase = engine.coherence_phase integration_count = engine.integration_count return jsonify({ 'master': { 'response': master_thought, 'coherence': coherence, 'phase': coherence_phase, 'integrations': integration_count, 'collapsed': collapsed }, 'emissaries': emissaries_dict }) if __name__ == '__main__': print("Starting BECOMINGONE (The Chorus) Prototype on http://localhost:8001") app.run(host='0.0.0.0', port=8001, debug=False, threaded=True)