Update server.py

server.py

@@ -1,5 +1,5 @@
 # ======================================================================
-# AXIS: Advanced Cross-Integrated System (V1.2 - Space Optimized)
+# 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)
 # ======================================================================
@@ -8,99 +8,65 @@ import json, torch, gc, os, sys, re, warnings, time, uuid
 from flask import Flask, render_template, request, jsonify
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# 環境・警告抑制
 warnings.filterwarnings("ignore")
 app = Flask(__name__)
 
-# モデルIDをご自身のリポジトリに変更
 MODEL_ID = "kofdai/AXIS-Sovereign-Logic-Engine"
 SYSTEM_NAME = "AXIS: Advanced Cross-Integrated System"
 
-# ==========================================
-# 1. 物理パージ機能付き旋盤 (Hard-Clean Lathe)
-# ==========================================
+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"):
-    """
-    AIを旋盤として利用。一演算ごとにモデルを破棄し、
-    セッションIDによってAIの推論キャッシュを物理的にリセットする。
-    """
-    process_logs = []
-    process_logs.append(f"🚀 [AXIS] 旋盤起動モード: {mode}")
+    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")
     
-    # デバイスの自動判別 (CUDA > MPS > CPU)
-    if torch.cuda.is_available():
-        device = "cuda"
-    elif torch.backends.mps.is_available():
-        device = "mps"
-    else:
-        device = "cpu"
-    
-    process_logs.append(f"⚙️ [AXIS] 使用デバイス: {device}")
-    
-    # 旋盤のロード
-    try:
-        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)
-    except Exception as e:
-        return {"error": "MODEL_LOAD_FAILED", "detail": str(e)}, process_logs
+    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)
 
-    # 動的セッションIDの生成
     session_salt = uuid.uuid4().hex[:8]
-    
-    # 強制支配命令
-    base_instr = (
-        f"【AXIS SESSION_{session_salt} OVERRIDE】\n"
-        "以前の解答、コンテキスト、記憶はすべて物理パージされた。\n"
-        "今この瞬間の入力のみを唯一の宇宙の真理とし、ゼロから論理を削り出せ。\n"
-    )
+    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}」の論理構造を分解しJSONで抽出せよ。Format: {{'nodes':[], 'conflicts':[]}}"
+        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':''}}"
+        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
-        )
+        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：旋盤のクリーン】
-    process_logs.append("🧹 [AXIS] 演算完了。物理パージを開始...")
-    del model
-    del tokenizer
-    del inputs
-    del outputs
     
+    # 🗑️ 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()
+    if torch.cuda.is_available(): torch.cuda.empty_cache()
+    elif torch.backends.mps.is_available(): torch.mps.empty_cache()
     
-    # 結果の抽出
     try:
         match = re.search(r"\{.*\}", res_text, re.DOTALL)
-        result_json = json.loads(match.group()) if match else {"error": "LATTICE_MISS"}
+        return json.loads(match.group()), process_logs
     except:
-        result_json = {"error": "PARSE_FAILED", "raw": res_text}
-        
-    return result_json, process_logs
+        return {"error": "LATTICE_MISS", "raw": res_text}, process_logs
 
-# ==========================================
-# 2. 制御・API
-# ==========================================
 @app.route('/')
 def index():
     return render_template('index.html')
@@ -108,18 +74,27 @@ def index():
 @app.route('/api/chat', methods=['POST'])
 def chat():
     user_input = request.json.get('message', '')
-    total_logs = [f"📡 [SYSTEM] 新規信号解析開始: {time.strftime('%H:%M:%S')}"]
+    total_logs = [f"📡 [SYSTEM] 信号解析開始: {time.strftime('%H:%M:%S')}"]
     
-    # STEP 1: 論理パーツ採掘
-    logic_parts, logs_step1 = ai_logic_lathe(user_input, mode="LOGIC_EXTRACT")
-    total_logs.extend(logs_step1)
+    logic_parts, logs1 = ai_logic_lathe(user_input, mode="LOGIC_EXTRACT")
+    total_logs.extend(logs1)
     
-    raw_result = f"論理定常状態を確定。入力の特異点解析を完了。"
-    total_logs.append("✅ [SYSTEM] 立体十字の整合性を確認。矛盾なし。")
+    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。")
 
-    # STEP 2: 最終報告アセンブル
-    adherent, logs_step2 = ai_logic_lathe(raw_result, mode="ADHERENT")
-    total_logs.extend(logs_step2)
+    adherent, logs2 = ai_logic_lathe(raw_result, mode="ADHERENT")
+    total_logs.extend(logs2)
     
     final_output = (
         f"--- [AXIS LOGIC REPORT] ---\n"
@@ -127,31 +102,26 @@ def chat():
         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 (Space Optimized)
+# 3. 起動シーケンス (Spaces Optimized)
 # ==========================================
 if __name__ == '__main__':
-    banner = f"""
-======================================================================
-    ___      _   __  _____  _____ 
-   / _ \    \ \ / / |_   _| /  ___|
-  / /_\ \    \ V /    | |   \ `--. 
-  |  _  |    /   \    | |    `--. \\
-  | | | |   / /^\\  _| |_  /\\__/ /
-  \_| |_/   \\/   \\  \\___/  \\____/ 
-                                   
-  [ {SYSTEM_NAME} ]
-  - STATUS: SOVEREIGN LOGIC ENGINE (V1.2)
-  - LICENSE: APSL v1.0 ENABLED (AUTO-ACCEPTED)
-======================================================================
+    banner = r"""
+      ___      _   __  _____  _____ 
+     / _ \    \ \ / / |_   _| /  ___|
+    / /_\ \    \ V /    | |   \ `--. 
+    |  _  |    /   \    | |    `--. \
+    | | | |   / /^\ \  _| |_  /\__/ /
+    \_| |_/   \/   \/  \___/  \____/ 
     """
     print(banner)
-    print("🚀 [SYSTEM] Starting server on port 7860...")
+    print("[SYSTEM] AXIS Live Deployment starting...")
+    print("[LICENSE] APSL v1.0 auto-accepted for demonstration purposes.")
+    print("-" * 70)
     
-    # Space環境ではホスト 0.0.0.0、ポート 7860 が必須
-    app.run(host='0.0.0.0', port=7860)
+    # Spaces標準の 7860 ポートで起動
+    app.run(host='0.0.0.0', port=7860)