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import json, torch, gc, os, sys, re, warnings, time, uuid |
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from flask import Flask, render_template, request, jsonify |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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warnings.filterwarnings("ignore") |
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app = Flask(__name__) |
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MODEL_ID = "kofdai/AXIS-Sovereign-Logic-Engine" |
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SYSTEM_NAME = "AXIS: Advanced Cross-Integrated System" |
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LICENSE_TERMS = """ |
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====================================================================== |
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[AXIS PROPRIETARY SOURCE-AVAILABLE LICENSE (APSL) v1.0] |
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---------------------------------------------------------------------- |
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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。 |
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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。 |
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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。 |
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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。 |
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====================================================================== |
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""" |
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def ai_logic_lathe(target, mode="LOGIC_EXTRACT"): |
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process_logs = [f"🚀 [AXIS] 旋盤起動: {mode}"] |
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device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.bfloat16 if device != "cpu" else torch.float32, |
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low_cpu_mem_usage=True |
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).to(device) |
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session_salt = uuid.uuid4().hex[:8] |
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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" |
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if mode == "LOGIC_EXTRACT": |
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prompt = ( |
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f"{base_instr}入力「{target}」を多角的に分解せよ。\n" |
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"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" |
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"必ず以下のJSON形式のみを出力せよ。解説は一切不要。\n" |
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"Format: {\"subject\":\"対象名称\", \"lattice\":{\"物理\":\"\", \"意味\":\"\", \"背景\":\"\"}, \"conflicts\":[]}" |
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) |
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else: |
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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\":\"\"}}" |
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True, return_tensors="pt").to(device) |
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with torch.no_grad(): |
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outputs = model.generate(inputs, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.7) |
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res_text = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True) |
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del model, tokenizer, inputs, outputs |
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gc.collect() |
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if torch.cuda.is_available(): torch.cuda.empty_cache() |
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elif torch.backends.mps.is_available(): torch.mps.empty_cache() |
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try: |
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match = re.search(r"\{.*\}", res_text, re.DOTALL) |
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return json.loads(match.group()), process_logs |
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except: |
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return {"error": "LATTICE_MISS", "raw": res_text}, process_logs |
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@app.route('/') |
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def index(): |
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return render_template('index.html') |
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@app.route('/api/chat', methods=['POST']) |
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def chat(): |
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user_input = request.json.get('message', '') |
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total_logs = [f"📡 [SYSTEM] 信号解析開始: {time.strftime('%H:%M:%S')}"] |
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logic_parts, logs1 = ai_logic_lathe(user_input, mode="LOGIC_EXTRACT") |
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total_logs.extend(logs1) |
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if "error" in logic_parts: |
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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。" |
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else: |
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subj = logic_parts.get('subject', user_input) |
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lat = logic_parts.get('lattice', {}) |
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lattice_str = " | ".join([f"{k}:{v}" for k, v in lat.items()]) |
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raw_result = ( |
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f"対象「{subj}」を論理格子（Lattice）へ展開完了。\n" |
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f"【解析格子】{lattice_str}\n" |
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f"矛盾検知: {len(logic_parts.get('conflicts', []))}。" |
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) |
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total_logs.append("✅ [SYSTEM] 立体十字の整合性を確認。") |
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adherent, logs2 = ai_logic_lathe(raw_result, mode="ADHERENT") |
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total_logs.extend(logs2) |
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final_output = ( |
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f"--- [AXIS LOGIC REPORT] ---\n" |
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f"{adherent.get('prefix', '演算結果：')}\n\n" |
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f"【確定データ】\n{raw_result}\n\n" |
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f"{adherent.get('suffix', '報告終了。')}\n" |
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f"-------------------------------\n" |
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f"AXIS_SESSION: {uuid.uuid4().hex[:4].upper()}\n" |
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f"STATUS: LOGIC_CONSOLIDATED." |
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) |
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return jsonify({"response": final_output, "process_logs": total_logs}) |
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if __name__ == '__main__': |
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banner = r""" |
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___ _ __ _____ _____ |
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/ _ \ \ \ / / |_ _| / ___| |
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/ /_\ \ \ V / | | \ `--. |
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| _ | / \ | | `--. \ |
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| | | | / /^\ \ _| |_ /\__/ / |
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\_| |_/ \/ \/ \___/ \____/ |
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[ AXIS: Sovereign Logic Engine V1.3 ] |
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""" |
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print(banner) |
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print(LICENSE_TERMS) |
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if input(">> APSL v1.0 条項に同意しますか？ (y/n): ").lower() != 'y': |
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print("❌ ライセンス拒否。アクセスを遮断します。") |
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sys.exit() |
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print(f"✅ [SYSTEM] 認証完了。Port 5001 でサーバーを起動します。") |
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app.run(host='0.0.0.0', port=5001) |
