server.py

# ======================================================================
# AXIS: Advanced Cross-Integrated System (V1.3 - Deep Lattice)
# Copyright (c) 2025 AXIS Project. All rights reserved.
# Licensed under APSL v1.0 (Non-Commercial / No-Redistribution)
# ======================================================================

import json, torch, gc, os, sys, re, warnings, time, uuid
from flask import Flask, render_template, request, jsonify
from transformers import AutoTokenizer, AutoModelForCausalLM

warnings.filterwarnings("ignore")
app = Flask(__name__)

MODEL_ID = "kofdai/AXIS-Sovereign-Logic-Engine"
SYSTEM_NAME = "AXIS: Advanced Cross-Integrated System"

LICENSE_TERMS = """
======================================================================
[AXIS PROPRIETARY SOURCE-AVAILABLE LICENSE (APSL) v1.0]
----------------------------------------------------------------------
1. 商用利用の禁止：本システムを直接的・間接的な収益化に用いることを禁じます。
2. 再配布の禁止：ソースコード、モデル重み、論理構造の無断配布を禁じます。
3. 統治ロジックの保護：AIを旋盤とし物理パージを行う設計の模倣を禁じます。
4. 演算の不可逆性：本システムの報告は、一過性推論の結果としての定数である。
======================================================================
"""

def ai_logic_lathe(target, mode="LOGIC_EXTRACT"):
    process_logs = [f"🚀 [AXIS] 旋盤起動: {mode}"]
    device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
    
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID, 
        torch_dtype=torch.bfloat16 if device != "cpu" else torch.float32, 
        low_cpu_mem_usage=True
    ).to(device)

    session_salt = uuid.uuid4().hex[:8]
    base_instr = f"【AXIS SESSION_{session_salt} OVERRIDE】\n記憶パージ完了。対象の論理的解像度を最大化せよ。\n"

    if mode == "LOGIC_EXTRACT":
        prompt = (
            f"{base_instr}入力「{target}」を多角的に分解せよ。\n"
            "物理的特性、機能的意味、抽象的概念の3層から要素を抽出すること。\n"
            "必ず以下のJSON形式のみを出力せよ。解説は一切不要。\n"
            "Format: {\"subject\":\"対象名称\", \"lattice\":{\"物理\":\"\", \"意味\":\"\", \"背景\":\"\"}, \"conflicts\":[]}"
        )
    else:
        prompt = f"{base_instr}演算データ「{target}」を報告する日本語パーツをJSONで返せ。Format: {{\"prefix\":\"\", \"suffix\":\"\"}}"

    inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model.generate(inputs, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.7)
    
    res_text = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
    
    # 🗑️ 物理パージ
    del model, tokenizer, inputs, outputs
    gc.collect()
    if torch.cuda.is_available(): torch.cuda.empty_cache()
    elif torch.backends.mps.is_available(): torch.mps.empty_cache()
    
    try:
        match = re.search(r"\{.*\}", res_text, re.DOTALL)
        return json.loads(match.group()), process_logs
    except:
        return {"error": "LATTICE_MISS", "raw": res_text}, process_logs

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

@app.route('/api/chat', methods=['POST'])
def chat():
    user_input = request.json.get('message', '')
    total_logs = [f"📡 [SYSTEM] 信号解析開始: {time.strftime('%H:%M:%S')}"]
    
    logic_parts, logs1 = ai_logic_lathe(user_input, mode="LOGIC_EXTRACT")
    total_logs.extend(logs1)
    
    if "error" in logic_parts:
        raw_result = f"論理特異点を検知。入力「{user_input[:10]}」を非定常信号として処理。解析不能。"
    else:
        subj = logic_parts.get('subject', user_input)
        lat = logic_parts.get('lattice', {})
        lattice_str = " | ".join([f"{k}:{v}" for k, v in lat.items()])
        raw_result = (
            f"対象「{subj}」を論理格子（Lattice）へ展開完了。\n"
            f"【解析格子】{lattice_str}\n"
            f"矛盾検知: {len(logic_parts.get('conflicts', []))}。"
        )
    
    total_logs.append("✅ [SYSTEM] 立体十字の整合性を確認。")

    adherent, logs2 = ai_logic_lathe(raw_result, mode="ADHERENT")
    total_logs.extend(logs2)
    
    final_output = (
        f"--- [AXIS LOGIC REPORT] ---\n"
        f"{adherent.get('prefix', '演算結果：')}\n\n"
        f"【確定データ】\n{raw_result}\n\n"
        f"{adherent.get('suffix', '報告終了。')}\n"
        f"-------------------------------\n"
        f"AXIS_SESSION: {uuid.uuid4().hex[:4].upper()}\n"
        f"STATUS: LOGIC_CONSOLIDATED."
    )
    return jsonify({"response": final_output, "process_logs": total_logs})
# ==========================================
# 3. 起動シーケンス (Spaces Optimized)
# ==========================================
if __name__ == '__main__':
    banner = r"""
      ___      _   __  _____  _____ 
     / _ \    \ \ / / |_   _| /  ___|
    / /_\ \    \ V /    | |   \ `--. 
    |  _  |    /   \    | |    `--. \
    | | | |   / /^\ \  _| |_  /\__/ /
    \_| |_/   \/   \/  \___/  \____/ 
    """
    print(banner)
    print("[SYSTEM] AXIS Live Deployment starting...")
    print("[LICENSE] APSL v1.0 auto-accepted for demonstration purposes.")
    print("-" * 70)
    
    # Spaces標準の 7860 ポートで起動
    app.run(host='0.0.0.0', port=7860)