update-to-qwin3

.dockerignore.txt

@@ -0,0 +1,8 @@
+.env
+*.pyc
+__pycache__/
+data/
+.cache/
+.vscode/
+*.log
+tmp/

.gitattributes.txt

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile.txt

@@ -0,0 +1,28 @@
+FROM python:3.10-slim
+
+# إضافة حزم أساسية فقط
+RUN apt-get update && apt-get install -y \
+    build-essential pkg-config curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# إضافة مستخدم غير root
+RUN useradd -m -u 1000 user
+WORKDIR /home/user/app
+COPY --chown=user . .
+
+# مجلد التخزين المؤقت للنموذج
+RUN mkdir -p /home/user/app/data && chown -R user:user /home/user/app/data
+
+# إنشاء البيئة الافتراضية
+RUN python -m venv /home/user/venv
+ENV PATH="/home/user/venv/bin:$PATH"
+ENV FORCE_CMAKE=1
+ENV CMAKE_ARGS="-DGGML_CUDA=off"
+
+# تثبيت المتطلبات
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+USER user
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,216 +1,667 @@
-# إعداد السجل العام للتطبيق
-import logging
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="🪵 [%(asctime)s] [%(levelname)s] %(message)s",
-    handlers=[
-        logging.StreamHandler(),
-        logging.FileHandler("data/app.log")
-    ]
-)
-logger = logging.getLogger(__name__)
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from llama_cpp import Llama
-import os
-import threading
-from fastapi.middleware.cors import CORSMiddleware
-from monitor import get_current_metrics, start_monitoring_thread
-from huggingface_hub import hf_hub_download
-from memory import get_history, save_history
-
-# إعدادات النموذج
-MODEL_REPO = "Qwen/Qwen2-1.5B-Instruct-GGUF"
-MODEL_FILE = "qwen2-1_5b-instruct-q6_k.gguf"
-MODEL_PATH = f"/home/user/app/data/cache/{MODEL_FILE}"
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-SYSTEM_PROMPT = """<|im_start|>system
-You are Qwen2, created by Alibaba Cloud. You are a helpful assistant specialized in AI development.
-Follow these rules:
-1. Provide accurate and safe answers
-2. For coding tasks, think step-by-step before answering
-3. Keep responses concise but complete
-4. For complex requests, ask clarifying questions<|im_end|>"""
-
-# تنسيق prompt الدردشة
-def format_chat(history, new_message):
-    messages = [("system", SYSTEM_PROMPT)]
-
-    for item in history[-8:]:
-        if isinstance(item, list) and len(item) == 2:
-            user_msg, bot_msg = item
-            messages.append(("user", user_msg))
-            messages.append(("assistant", bot_msg))
-        else:
-            logger.warning(f"⚠️ عنصر غير متوافق في التاريخ: {item}")
-
-    messages.append(("user", new_message))
-
-    formatted = []
-    for role, content in messages:
-        if role == "system":
-            formatted.append(f"<|im_start|>system\n{content}<|im_end|>")
-        elif role == "user":
-            formatted.append(f"<|im_start|>user\n{content}<|im_end|>")
-        else:
-            formatted.append(f"<|im_start|>assistant\n{content}<|im_end|>")
-    formatted.append("<|im_start|>assistant\n")
-    return "\n".join(formatted)
-
-# تحميل النموذج إذا لم يكن موجوداً
-def load_model():
-    if not os.path.exists(MODEL_PATH):
-        os.makedirs("/home/user/app/data/cache", exist_ok=True)
-        logger.info("📦 تحميل النموذج من Hugging Face Hub...")
-        try:
-            hf_hub_download(
-                repo_id=MODEL_REPO,
-                filename=MODEL_FILE,
-                local_dir="/home/user/app/data/cache",
-                token=HF_TOKEN,
-            )
-            logger.info(f"✅ تم تحميل النموذج: {MODEL_FILE}")
-        except Exception as e:
-            logger.error(f"❌ فشل تحميل النموذج: {str(e)}")
-            raise RuntimeError("فشل تحميل النموذج") from e
-
-    if not os.path.exists(MODEL_PATH):
-        raise RuntimeError("النموذج غير موجود بعد التحميل")
-
-    return Llama(
-        model_path=MODEL_PATH,
-        n_ctx=1024,
-        n_threads=2,
-        n_gpu_layers=0,
-        n_batch=64,
-        use_mlock=False,
-        verbose=False
-    )
-
-# إنشاء تطبيق FastAPI
-app = FastAPI()
-
-# تحميل النموذج في الخلفية
-llm = None
-model_loading_thread = None
-
-def load_model_in_background():
-    global llm
-    try:
-        llm = load_model()
-        logger.info("✅ تم تحميل النموذج بنجاح")
-        test_prompt = format_chat([], "مرحبا، كيف يمكنك المساعدة؟")
-        test_output = llm(test_prompt, max_tokens=50)['choices'][0]['text'].strip()
-        logger.info(f"✅ اختبار النموذج ناجح: {test_output[:100]}...")
-    except Exception as e:
-        logger.error(f"❌ فشل تحميل النموذج: {str(e)}")
-        logger.exception(e)
-
-# تمكين CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.on_event("startup")
-async def startup_event():
-    global model_loading_thread
-    logger.info("🚀 بدء تحميل النموذج في الخلفية...")
-    model_loading_thread = threading.Thread(target=load_model_in_background, daemon=True)
-    model_loading_thread.start()
-    
-    # بدء مراقبة الموارد بعد إنشاء التطبيق
-    start_monitoring_thread()
-
-# نموذج الطلب والرد
-class ChatRequest(BaseModel):
-    session_id: str
-    message: str
-
-class ChatResponse(BaseModel):
-    response: str
-    updated_history: list[list[str]]
-
-# التحقق من حالة النموذج
-@app.get("/model-status")
-def model_status():
-    status = "loaded" if llm else "loading"
-    thread_alive = model_loading_thread.is_alive() if model_loading_thread else False
-    return {"status": status, "thread_alive": thread_alive}
-
-# endpoint عرض المقاييس
-@app.get("/metrics")
-def read_metrics():
-    return get_current_metrics()
-
-# الدردشة الرئيسية
-@app.post("/chat", response_model=ChatResponse)
-def chat(req: ChatRequest):
-    global llm
-    if not llm:
-        if model_loading_thread and model_loading_thread.is_alive():
-            raise HTTPException(status_code=503, detail="النموذج قيد التحميل، الرجاء المحاولة لاحقاً")
-        else:
-            raise HTTPException(status_code=500, detail="فشل تحميل النموذج")
-
-    logger.info(f"📩 طلب جديد من جلسة {req.session_id}: {req.message}")
-    try:
-        history = get_history(req.session_id)
-        logger.info(f"📜 التاريخ الحالي: {len(history)} رسائل")
-        prompt = format_chat(history, req.message)
-
-        output = llm(
-            prompt,
-            max_tokens=512,
-            temperature=0.5,
-            top_p=0.9,
-            stop=["<|im_end|>", "<|im_start|>"],
-            echo=False
-        )
-
-        reply = output['choices'][0]['text'].strip()
-        logger.info(f"🤖 رد النموذج: {reply[:100]}...")
-
-        updated_history = (history + [[req.message, reply]])[-8:]
-        save_history(req.session_id, updated_history)
-
-        return ChatResponse(response=reply, updated_history=updated_history)
-
-    except Exception as e:
-        logger.error(f"❌ خطأ أثناء المعالجة: {str(e)}")
-        logger.exception(e)
-        raise HTTPException(status_code=500, detail="حدث خطأ أثناء توليد الرد")
-
-# root
-@app.get("/")
-def root():
-    return {"message": "الخادم يعمل", "status": "ok"}
-
-# endpoint قراءة سجل مراقبة الموارد
-@app.get("/monitor-log")
-def read_monitor_log():
-    import os
-    # استخدام المسار المطلق للتأكد من العثور على الملف
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    log_path = os.path.join(base_dir, "data", "monitor.log")
-    
-    if not os.path.exists(log_path):
-        # إنشاء ملف فارغ إذا لم يكن موجوداً
-        open(log_path, "w", encoding="utf-8").close()
-        logger.warning(f"⚠️ تم إنشاء ملف سجل جديد: {log_path}")
-    
-    try:
-        with open(log_path, "r", encoding="utf-8") as f:
-            content = f.read()
-        return {"log": content}
-    except Exception as e:
-        logger.error(f"❌ فشل قراءة سجل المراقبة: {str(e)}")
-        raise HTTPException(
-            status_code=500,
-            detail=f"حدث خطأ أثناء قراءة السجل: {str(e)}"
-        )
+# app.py
+import os
+import json
+import hashlib
+import logging
+import threading
+from pathlib import Path
+from typing import List, Dict, Any, Tuple
+
+import numpy as np
+import faiss
+import pickle
+import ast as python_ast
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+
+from monitor import get_current_metrics, start_monitoring_thread
+from memory import get_history, save_history
+
+# =========================
+# إعداد السجلّات
+# =========================
+logging.basicConfig(
+    level=logging.INFO,
+    format="🪵 [%(asctime)s] [%(levelname)s] %(message)s"
+)
+logger = logging.getLogger("app")
+
+# =========================
+# ثوابت ومسارات
+# =========================
+DATA_DIR = Path("data")
+CACHE_DIR = DATA_DIR / "cache"
+INDEX_DIR = DATA_DIR / "index"
+FILES_DIR = DATA_DIR / "files"           # تخزين النص الكامل لكل ملف
+REPORT_FILE = DATA_DIR / "analysis_report.md"
+GRAPH_FILE = DATA_DIR / "code_graph.json"
+EMB_FILE = INDEX_DIR / "embeddings.faiss"
+META_FILE = INDEX_DIR / "chunks.pkl"
+HASH_MAP_FILE = INDEX_DIR / "hash_map.json"
+
+for p in [DATA_DIR, CACHE_DIR, INDEX_DIR, FILES_DIR]:
+    p.mkdir(parents=True, exist_ok=True)
+
+# Env
+HF_TOKEN = os.getenv("HF_TOKEN", "")
+MODEL_REPO = os.getenv("MODEL_REPO", "Qwen/Qwen3-8B-Instruct")
+
+# GGUF المحلي (إن توفر)
+LOCAL_GGUF_REPO = os.getenv("LOCAL_GGUF_REPO", "Triangle104/Qwen3-8B-Q4_K_M-GGUF")
+LOCAL_GGUF_FILE = os.getenv("LOCAL_GGUF_FILE", "qwen3-8b-q4_k_m.gguf")
+LOCAL_GGUF_PATH = CACHE_DIR / LOCAL_GGUF_FILE
+
+# تضمين
+EMBED_MODEL_NAME = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+EMBED_DIM = int(os.getenv("EMBED_DIM", "384"))
+
+# تقسيم الشيفرة
+CHUNK_STEP = int(os.getenv("CHUNK_STEP", "40"))  # ✅ قابل للتهيئة
+MAX_FILE_BYTES = int(os.getenv("MAX_FILE_BYTES", str(10 * 1024 * 1024)))  # 10MB احتياطيًا
+
+SYSTEM_PROMPT = """<|im_start|>system
+You are a senior AI code analyst. Analyze projects with hybrid indexing (code graph + retrieval).
+Return structured, accurate, concise answers. Use Arabic + English labels in the final report.
+<|im_end|>"""
+
+# =========================
+# الحالة العالمية والقفل
+# =========================
+embed_model: SentenceTransformer | None = None
+faiss_index: faiss.Index | None = None
+all_chunks: List[Tuple[str, str]] = []  # (file_name, chunk_text)
+code_graph: Dict[str, Any] = {"files": {}}
+hash_map: Dict[str, str] = {}
+
+index_lock = threading.RLock()  # ✅ لتأمين الفهرسة/الاسترجاع
+
+# =========================
+# LLM (محلي/سحابي)
+# =========================
+try:
+    from llama_cpp import Llama
+except Exception:
+    Llama = None
+
+llm = None  # كائن النموذج المحلي إن توفر
+
+def load_local_model_if_configured():
+    """تحميل GGUF محليًا إن كان مفعّلًا."""
+    global llm
+    if Llama is None:
+        logger.info("ℹ️ llama_cpp غير متوفر. سيتم الاعتماد على HF Inference عند الحاجة.")
+        return
+    if not LOCAL_GGUF_PATH.exists():
+        try:
+            logger.info(f"⬇️ تنزيل GGUF: {LOCAL_GGUF_REPO}/{LOCAL_GGUF_FILE}")
+            hf_hub_download(
+                repo_id=LOCAL_GGUF_REPO,
+                filename=LOCAL_GGUF_FILE,
+                local_dir=str(CACHE_DIR),
+                token=HF_TOKEN or None
+            )
+        except Exception as e:
+            logger.warning(f"⚠️ تعذر تنزيل GGUF: {e}. سيتجاهل التحميل المحلي.")
+            return
+    try:
+        llm = Llama(
+            model_path=str(LOCAL_GGUF_PATH),
+            n_ctx=int(os.getenv("N_CTX", "32768")),
+            rope_scaling={"type": "yarn", "factor": 4.0},
+            n_threads=int(os.getenv("N_THREADS", "2")),
+            n_gpu_layers=int(os.getenv("N_GPU_LAYERS", "0")),
+            n_batch=int(os.getenv("N_BATCH", "64")),
+            use_mlock=False,
+            verbose=False
+        )
+        logger.info("✅ تم تحميل النموذج المحلي (GGUF).")
+    except Exception as e:
+        llm = None
+        logger.warning(f"⚠️ فشل تحميل النموذج المحلي: {e}")
+
+def call_local_llm(prompt: str, max_tokens: int = 800) -> str:
+    if llm is None or Llama is None:
+        return ""
+    try:
+        res = llm(
+            prompt,
+            max_tokens=max_tokens,
+            temperature=0.4,
+            top_p=0.9,
+            stop=["<|im_end|>", "<|im_start|>"],
+            echo=False
+        )
+        return res["choices"][0]["text"].strip()
+    except Exception as e:
+        logger.warning(f"⚠️ local LLM call failed: {e}")
+        return ""
+
+def call_hf_inference(prompt: str, max_new_tokens: int = 900) -> str:
+    import requests
+    if not HF_TOKEN:
+        raise RuntimeError("HF_TOKEN is not set and local LLM unavailable")
+    url = f"https://api-inference.huggingface.co/models/{MODEL_REPO}"
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": max_new_tokens,
+            "temperature": 0.4,
+            "top_p": 0.9,
+            "return_full_text": False
+        }
+    }
+    r = requests.post(url, headers=headers, json=payload, timeout=120)
+    r.raise_for_status()
+    data = r.json()
+    if isinstance(data, list) and data and "generated_text" in data[0]:
+        return data[0]["generated_text"]
+    if isinstance(data, dict) and "generated_text" in data:
+        return data["generated_text"]
+    if isinstance(data, dict) and "error" in data:
+        raise RuntimeError(data["error"])
+    return json.dumps(data)
+
+def call_llm(prompt: str, max_tokens: int = 900) -> str:
+    out = call_local_llm(prompt, max_tokens)
+    if out:
+        return out
+    return call_hf_inference(prompt, max_tokens)
+
+# =========================
+# أدوات التضمين والفهرسة
+# =========================
+def sha256_text(text: str) -> str:
+    return hashlib.sha256(text.encode("utf-8")).hexdigest()
+
+def init_embed():
+    """تهيئة نموذج التضمين والقراءات من القرص."""
+    global embed_model, faiss_index, all_chunks, hash_map, code_graph
+    embed_model = SentenceTransformer(EMBED_MODEL_NAME)
+
+    if EMB_FILE.exists() and META_FILE.exists():
+        try:
+            faiss_index = faiss.read_index(str(EMB_FILE))
+            with open(META_FILE, "rb") as f:
+                all_chunks = pickle.load(f)
+            logger.info(f"✅ تم تحميل الفهرس ({faiss_index.ntotal} متجه) من القرص.")
+        except Exception as e:
+            logger.warning(f"⚠️ تعذر تحميل الفهرس: {e}. إنشاء فهرس جديد.")
+            faiss_index = faiss.IndexFlatL2(EMBED_DIM)
+            all_chunks = []
+    else:
+        faiss_index = faiss.IndexFlatL2(EMBED_DIM)
+        all_chunks = []
+
+    if HASH_MAP_FILE.exists():
+        try:
+            hash_map = json.loads(HASH_MAP_FILE.read_text(encoding="utf-8"))
+        except Exception:
+            hash_map = {}
+    else:
+        hash_map = {}
+
+    if GRAPH_FILE.exists():
+        try:
+            code_graph = json.loads(GRAPH_FILE.read_text(encoding="utf-8"))
+        except Exception:
+            code_graph = {"files": {}}
+    else:
+        code_graph = {"files": {}}
+
+def chunk_code_structured(code: str) -> List[str]:
+    """قسّم الشيفرة إلى كتل حسب AST؛ وإن فشل فاقطع أسطريًا."""
+    try:
+        tree = python_ast.parse(code)
+        chunks: List[str] = []
+        lines = code.splitlines()
+        for node in tree.body:
+            s = max(getattr(node, "lineno", 1) - 1, 0)
+            e = getattr(node, "end_lineno", s + 1)
+            e = max(e, s + 1)
+            chunks.append("\n".join(lines[s:e]))
+        if chunks:
+            return chunks
+    except Exception as e:
+        # ✅ سجل نوع الاستثناء والموقع إن توفر
+        msg = f"{type(e).__name__}: {e}"
+        logger.debug(f"AST parse failed; fallback to line-based. Reason: {msg}")
+
+    step = CHUNK_STEP  # ✅ من ENV
+    lines = code.splitlines()
+    return ["\n".join(lines[i:i + step]) for i in range(0, len(lines), step)]
+
+def parse_code_meta(file_name: str, code: str) -> Dict[str, Any]:
+    """استخرج رموز/استيرادات/نداءات لملف."""
+    meta = {"hash": sha256_text(code), "symbols": [], "calls": [], "imports": []}
+    try:
+        tree = python_ast.parse(code)
+        for node in python_ast.walk(tree):
+            if isinstance(node, python_ast.FunctionDef):
+                meta["symbols"].append({"name": node.name, "kind": "function", "line": node.lineno})
+            elif isinstance(node, python_ast.ClassDef):
+                meta["symbols"].append({"name": node.name, "kind": "class", "line": node.lineno})
+            elif isinstance(node, python_ast.Assign):
+                for t in getattr(node, "targets", []):
+                    if isinstance(t, python_ast.Name):
+                        meta["symbols"].append({"name": t.id, "kind": "variable", "line": node.lineno})
+            elif isinstance(node, python_ast.Import):
+                for alias in node.names:
+                    meta["imports"].append(alias.name or "")
+            elif isinstance(node, python_ast.ImportFrom):
+                meta["imports"].append(node.module or "")
+            elif isinstance(node, python_ast.Call):
+                f = node.func
+                if hasattr(f, "id"):
+                    meta["calls"].append(getattr(f, "id", ""))
+                elif hasattr(f, "attr"):
+                    meta["calls"].append(getattr(f, "attr", ""))
+    except Exception as e:
+        logger.debug(f"parse_code_meta failed for {file_name}: {type(e).__name__}: {e}")
+    return meta
+
+def reconstruct_all_vectors() -> np.ndarray:
+    """إعادة بناء جميع المتجهات من الفهرس."""
+    if faiss_index is None or faiss_index.ntotal == 0:
+        return np.array([], dtype=np.float32)
+    xs = [faiss_index.reconstruct(i) for i in range(faiss_index.ntotal)]
+    return np.array(xs, dtype=np.float32) if xs else np.array([], dtype=np.float32)
+
+def persist_index():
+    """حفظ الفهرس والميتا والخرائط."""
+    faiss.write_index(faiss_index, str(EMB_FILE))
+    with open(META_FILE, "wb") as f:
+        pickle.dump(all_chunks, f)
+    GRAPH_FILE.write_text(json.dumps(code_graph, ensure_ascii=False, indent=2), encoding="utf-8")
+    HASH_MAP_FILE.write_text(json.dumps(hash_map, ensure_ascii=False, indent=2), encoding="utf-8")
+
+def upsert_file_to_index(file_name: str, content: str):
+    """إدراج/تحديث ملف داخل الفهرس والميتا والحفظ على القرص."""
+    global faiss_index, all_chunks, code_graph, hash_map
+
+    # ✅ حفظ نسخة أحدث على HF (القرص المحلي)
+    file_path = FILES_DIR / file_name
+    file_path.parent.mkdir(parents=True, exist_ok=True)
+    file_path.write_text(content, encoding="utf-8")
+
+    content_hash = sha256_text(content)
+    prev_hash = hash_map.get(file_name)
+    if prev_hash == content_hash:
+        return
+
+    chunks = chunk_code_structured(content)
+    embeds = embed_model.encode(chunks, normalize_embeddings=True)
+
+    with index_lock:
+        # إعادة الدمج (سهل وآمن)
+        old_vecs = reconstruct_all_vectors()
+        new_vecs = np.array(embeds, dtype=np.float32)
+        merged = new_vecs if old_vecs.size == 0 else np.vstack([old_vecs, new_vecs])
+        faiss_index = faiss.IndexFlatL2(EMBED_DIM)
+        faiss_index.add(merged)
+
+        all_chunks.extend([(file_name, c) for c in chunks])
+        code_graph["files"][file_name] = parse_code_meta(file_name, content)
+        hash_map[file_name] = content_hash
+        persist_index()
+
+def rebuild_index_from_files():
+    """إعادة بناء الفهرس بالكامل من محتويات data/files/."""
+    global faiss_index, all_chunks, code_graph, hash_map
+    with index_lock:
+        faiss_index = faiss.IndexFlatL2(EMBED_DIM)
+        all_chunks = []
+        code_graph = {"files": {}}
+        hash_map = {}
+
+        for p in sorted(FILES_DIR.rglob("*")):
+            if not p.is_file():
+                continue
+            try:
+                content = p.read_text(encoding="utf-8")
+            except Exception:
+                # تجاهل الملفات الثنائية/غير النصية
+                continue
+            fname = str(p.relative_to(FILES_DIR)).replace("\\", "/")
+            chunks = chunk_code_structured(content)
+            if not chunks:
+                continue
+            embeds = embed_model.encode(chunks, normalize_embeddings=True)
+            vecs = np.array(embeds, dtype=np.float32)
+            if vecs.size:
+                faiss_index.add(vecs)
+            all_chunks.extend([(fname, c) for c in chunks])
+            code_graph["files"][fname] = parse_code_meta(fname, content)
+            hash_map[fname] = sha256_text(content)
+        persist_index()
+
+def retrieve(query: str, k: int = 8) -> List[Tuple[str, str, float]]:
+    """استرجاع أفضل k كتل للسياق."""
+    if faiss_index is None or faiss_index.ntotal == 0 or embed_model is None:
+        return []
+    q = embed_model.encode([query], normalize_embeddings=True)
+    D, I = faiss_index.search(np.array(q, dtype=np.float32), k)
+    out: List[Tuple[str, str, float]] = []
+    for score, idx in zip(D[0], I[0]):
+        if idx < 0 or idx >= len(all_chunks):
+            continue
+        file_name, chunk = all_chunks[idx]
+        # تعزيز بسيط لو ظهر import/call من الاستعلام
+        boost = 1.0
+        meta = code_graph["files"].get(file_name, {})
+        imports = set(meta.get("imports", []))
+        calls = set(meta.get("calls", []))
+        if any(tok in query for tok in (list(imports) + list(calls))):
+            boost = 0.9
+        out.append((file_name, chunk, float(score) * boost))
+    out.sort(key=lambda x: x[2])
+    return out[:k]
+
+def render_graph_overview(limit: int = 100) -> str:
+    lines = []
+    files = list(code_graph.get("files", {}).items())[:limit]
+    for fname, meta in files:
+        syms = ", ".join([f"{s.get('kind')}:{s.get('name')}" for s in meta.get("symbols", [])][:8])
+        imps = ", ".join(meta.get("imports", [])[:6])
+        cls = ", ".join(meta.get("calls", [])[:8])
+        lines.append(f"- File: {fname}\n  Symbols: {syms}\n  Imports: {imps}\n  Calls: {cls}")
+    return "\n".join(lines)
+
+def build_chat_prompt(history: List[List[str]], user_msg: str, extra: str = "") -> str:
+    msgs = [("system", SYSTEM_PROMPT)]
+    for u, a in history[-8:]:
+        msgs.append(("user", u))
+        msgs.append(("assistant", a))
+    msgs.append(("user", (user_msg or "") + ("\n" + extra if extra else "")))
+    out = []
+    for role, content in msgs:
+        if role == "system":
+            out.append(f"<|im_start|>system\n{content}<|im_end|>")
+        elif role == "user":
+            out.append(f"<|im_start|>user\n{content}<|im_end|>")
+        else:
+            out.append(f"<|im_start|>assistant\n{content}<|im_end|>")
+    out.append("<|im_start|>assistant\n")
+    return "\n".join(out)
+
+def build_analysis_prompt(query: str, retrieved_docs: List[Tuple[str, str, float]]) -> str:
+    graph_overview = render_graph_overview(120)
+    ctx = []
+    for i, (fname, chunk, score) in enumerate(retrieved_docs, 1):
+        ctx.append(f"### Source {i}\n[File] {fname}\n[Score] {score:.4f}\n```\n{chunk}\n```")
+    context_block = "\n\n".join(ctx)
+    instructions = (
+        "المطلوب: تحليل الملفات المسترجعة مع السياق التالي لإنتاج تقرير تحليلي شامل يشمل:\n"
+        "1) التسلسل المنطقي (Logical Sequence) وخطوات التنفيذ\n"
+        "2) التسلسل الوظيفي (Functional Flow) والمخطط التدفق النصي (Flow Outline)\n"
+        "3) التبعيات بين الملفات (Dependencies) والاستدعاءات (Call Relations)\n"
+        "4) العلاقات بين الملفات والمتغيرات العامة ومكان تعريفها (Global Vars Map)\n"
+        "5) تحديد نقاط الضعف المحتملة (Logic/Security/Performance) إن وجدت\n"
+        "6) توصيات اصلاح عملية\n"
+        "صيغة المخرجات: Markdown منظم بعناوين عربية + English labels:\n"
+        "## نظرة عامة / Overview\n"
+        "## خريطة التبعيات / Dependency Map\n"
+        "## المخطط التدفق / Flow Outline\n"
+        "## تحليل منطقي ووظيفي / Logical & Functional Analysis\n"
+        "## المتغيرات العامة / Global Variables\n"
+        "## مشاكل محتملة / Potential Issues\n"
+        "## توصيات / Recommendations"
+    )
+    user = f"سؤال التحليل: {query}\n\n[Graph Overview]\n{graph_overview}\n\n[Retrieved Context]\n{context_block}"
+    prompt = (
+        f"<|im_start|>system\n{SYSTEM_PROMPT}\n<|im_end|>\n"
+        f"<|im_start|>user\n{instructions}\n\n{user}\n<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+    return prompt
+
+# =========================
+# نماذج الطلب/الاستجابة
+# =========================
+class ChatRequest(BaseModel):
+    session_id: str
+    message: str
+
+class ChatResponse(BaseModel):
+    response: str
+    updated_history: list[list[str]]
+
+class AnalyzeRequest(BaseModel):
+    files: dict[str, str]  # name -> content
+
+class AnalyzeAndReportRequest(BaseModel):
+    session_id: str
+    query: str
+    top_k: int | None = 10
+
+class DiffRequest(BaseModel):
+    modified: dict[str, str] = {}  # filename -> full new content
+    deleted: list[str] = []
+
+# =========================
+# (اختياري) هوكس Drive/GitHub
+# =========================
+def maybe_upload_to_drive(local_path: Path):
+    """هوك اختياري: ارفع نسخة إلى جوجل درايف إن كان الاتصال مهيّأً."""
+    # اتركها فارغة الآن حتى تضيف اعتماد Google Drive.
+    # يمكن لاحقًا قراءة ENV مثل DRIVE_ENABLED=1 و OAuth كشفيرة.
+    pass
+
+def maybe_commit_to_github(local_path: Path, message: str = "auto: update file"):
+    """هوك اختياري: قم بكومِت/دَفع تلقائي إن كان الاتصال مهيّأً."""
+    # اتركها فارغة الآن لحين ربط GitHub عبر توكن/Repo.
+    pass
+
+# =========================
+# عمليات التحليل/التقرير
+# =========================
+def analyze_and_report_internal(session_id: str, query: str, k: int = 10) -> str:
+    retrieved_docs = retrieve(query, k=k)
+    if not retrieved_docs:
+        raise HTTPException(status_code=400, detail="لا توجد بيانات مفهرسة بعد. استخدم /analyze-files أولًا.")
+    prompt = build_analysis_prompt(query, retrieved_docs)
+    report = call_llm(prompt, max_tokens=1400)
+    REPORT_FILE.write_text(report, encoding="utf-8")
+    history = get_history(session_id)
+    updated = (history + [[f"[ANALYZE] {query}", report]])[-8:]
+    save_history(session_id, updated)
+    return report
+
+# =========================
+# تطبيق FastAPI
+# =========================
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], allow_credentials=True,
+    allow_methods=["*"], allow_headers=["*"]
+)
+
+@app.on_event("startup")
+async def on_startup():
+    load_local_model_if_configured()
+    start_monitoring_thread()
+    init_embed()
+    logger.info("🚀 التطبيق جاهز.")
+
+@app.get("/")
+def root():
+    return {"message": "الخادم يعمل", "status": "ok"}
+
+@app.get("/model-status")
+def model_status():
+    status = "local_loaded" if llm else ("hf_ready" if HF_TOKEN else "no_model")
+    return {"status": status, "repo": MODEL_REPO, "local": str(LOCAL_GGUF_PATH) if llm else None}
+
+@app.get("/metrics")
+def read_metrics():
+    return get_current_metrics()
+
+@app.get("/monitor-log")
+def read_monitor_log():
+    log_path = DATA_DIR / "monitor.log"
+    if not log_path.exists():
+        log_path.touch()
+    return {"log": log_path.read_text(encoding="utf-8")}
+
+@app.post("/analyze-files")
+def analyze_files(req: AnalyzeRequest):
+    # ✅ حفظ واستبدال الأحدث محليًا + فهرسة
+    total_bytes = 0
+    for fname, content in req.files.items():
+        total_bytes += len(content.encode("utf-8", errors="ignore"))
+        if MAX_FILE_BYTES and len(content.encode("utf-8", errors="ignore")) > MAX_FILE_BYTES:
+            raise HTTPException(status_code=413, detail=f"الملف {fname} يتجاوز الحجم المسموح.")
+
+    for fname, content in req.files.items():
+        upsert_file_to_index(fname, content)
+        # اختياري: رفع نسخة للأرشفة السحابية
+        try:
+            maybe_upload_to_drive(FILES_DIR / fname)
+        except Exception as e:
+            logger.warning(f"Drive upload skipped for {fname}: {e}")
+        try:
+            maybe_commit_to_github(FILES_DIR / fname, "auto: analyze & save")
+        except Exception as e:
+            logger.warning(f"GitHub commit skipped for {fname}: {e}")
+
+    return {"status": "Files analyzed and cached", "files_indexed": list(req.files.keys())}
+
+@app.post("/diff-files")
+def diff_files(req: DiffRequest):
+    """تطبيق تعديلات Git (modified/deleted) مع إعادة بناء نظيفة للفهرس."""
+    # 1) حذف الملفات المطلوبة (من القرص ومن الخرائط)
+    for fname in req.deleted:
+        try:
+            # ح��ف من القرص
+            fp = FILES_DIR / fname
+            if fp.exists():
+                fp.unlink()
+        except Exception as e:
+            logger.warning(f"⚠️ تعذر حذف {fname} من القرص: {e}")
+        # تنظيف الخرائط
+        hash_map.pop(fname, None)
+        code_graph.get("files", {}).pop(fname, None)
+
+    # 2) كتابة/تحديث الملفات المعدّلة على القرص
+    for fname, content in req.modified.items():
+        if MAX_FILE_BYTES and len(content.encode("utf-8", errors="ignore")) > MAX_FILE_BYTES:
+            raise HTTPException(status_code=413, detail=f"الملف {fname} يتجاوز الحجم المسموح.")
+        fp = FILES_DIR / fname
+        fp.parent.mkdir(parents=True, exist_ok=True)
+        fp.write_text(content, encoding="utf-8")
+
+    # 3) إعادة بناء الفهرس بالكامل من الملفات الحالية
+    rebuild_index_from_files()
+
+    # 4) (اختياري) رفع النسخ المعدلة للأرشفة
+    for fname in req.modified.keys():
+        try:
+            maybe_upload_to_drive(FILES_DIR / fname)
+        except Exception as e:
+            logger.warning(f"Drive upload skipped for {fname}: {e}")
+        try:
+            maybe_commit_to_github(FILES_DIR / fname, "auto: diff-files update")
+        except Exception as e:
+            logger.warning(f"GitHub commit skipped for {fname}: {e}")
+
+    return {
+        "status": "ok",
+        "deleted": req.deleted,
+        "modified": list(req.modified.keys()),
+        "total_index_vectors": int(faiss_index.ntotal) if faiss_index else 0
+    }
+
+@app.post("/analyze-and-report")
+def analyze_and_report(req: AnalyzeAndReportRequest):
+    report = analyze_and_report_internal(req.session_id, req.query, k=req.top_k or 10)
+    return {"status": "ok", "report_path": str(REPORT_FILE), "preview": report[:1200]}
+
+def classify_intent(history: List[List[str]], message: str) -> Dict[str, Any]:
+    inst = (
+        "أعد JSON فقط دون أي نص آخر.\n"
+        "المفاتيح: intent (string), confidence (0-1), action (RETRIEVE_ONLY|ANALYZE_AND_REPORT|TRACE_SUBSET|NONE), "
+        "targets (list of strings), reason (string).\n"
+        "أمثلة:\n"
+        "س: ما عمل الملف X؟ → {\"intent\":\"ASK_FILE_ROLE\",\"confidence\":0.9,\"action\":\"RETRIEVE_ONLY\",\"targets\":[\"X\"],\"reason\":\"...\"}\n"
+        "س: لماذا لا تعمل ميزة الدخول؟ → {\"intent\":\"WHY_FEATURE_NOT_WORKING\",\"confidence\":0.85,\"action\":\"ANALYZE_AND_REPORT\",\"targets\":[],\"reason\":\"...\"}\n"
+        "س: اين يُعرّف المتغير TOKEN وكيف يتغير؟ → {\"intent\":\"CODE_FLOW_TRACE\",\"confidence\":0.8,\"action\":\"TRACE_SUBSET\",\"targets\":[\"TOKEN\"],\"reason\":\"...\"}\n"
+    )
+    p = (
+        f"<|im_start|>system\n{SYSTEM_PROMPT}\n<|im_end|>\n"
+        f"<|im_start|>user\n{inst}\nالسؤال: {message}\nأعد JSON فقط.\n<|im_end|>\n"
+        f"<|im_start|>assistant\n"
+    )
+    txt = call_llm(p, max_tokens=200)
+    try:
+        start = txt.find("{")
+        end = txt.rfind("}")
+        obj = json.loads(txt[start:end+1]) if start != -1 and end != -1 else {}
+    except Exception:
+        obj = {}
+    if not isinstance(obj, dict):
+        obj = {}
+    obj.setdefault("intent", "UNKNOWN")
+    obj.setdefault("confidence", 0.0)
+    obj.setdefault("action", "NONE")
+    obj.setdefault("targets", [])
+    obj.setdefault("reason", "")
+    return obj
+
+@app.post("/chat", response_model=ChatResponse)
+def chat(req: ChatRequest):
+    history = get_history(req.session_id)
+    decision = classify_intent(history, req.message)
+    action = decision.get("action", "NONE")
+    response_text = ""
+
+    if action == "ANALYZE_AND_REPORT":
+        try:
+            report = analyze_and_report_internal(req.session_id, req.message, k=10)
+            response_text = "تم إنشاء تقرير تحليلي:\n\n" + report
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"LLM error: {str(e)}")
+
+    elif action == "RETRIEVE_ONLY":
+        retrieved_docs = retrieve(req.message, k=6)
+        ctx = []
+        for fname, chunk, score in retrieved_docs:
+            ctx.append(f"From {fname} (score={score:.4f}):\n{chunk}")
+        extra = "\n\n[Context]\n" + "\n\n".join(ctx) + "\n\n" + render_graph_overview(60)
+        prompt = build_chat_prompt(history, req.message, extra)
+        try:
+            response_text = call_llm(prompt, max_tokens=700)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"LLM error: {str(e)}")
+
+    elif action == "TRACE_SUBSET":
+        targets = decision.get("targets", [])
+        key = " ".join(targets) if targets else req.message
+        retrieved_docs = retrieve(key, k=10)
+        ctx = []
+        for fname, chunk, score in retrieved_docs:
+            ctx.append(f"From {fname} (score={score:.4f}):\n{chunk}")
+        flow_query = req.message + "\nPlease trace variables/functions: " + ", ".join(targets)
+        prompt = build_analysis_prompt(flow_query, retrieved_docs)
+        try:
+            trace_report = call_llm(prompt, max_tokens=1200)
+            REPORT_FILE.write_text(trace_report, encoding="utf-8")
+            response_text = "تقرير التتبع:\n\n" + trace_report
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"LLM error: {str(e)}")
+
+    else:
+        prompt = build_chat_prompt(history, req.message, "")
+        try:
+            response_text = call_llm(prompt, max_tokens=600)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"LLM error: {str(e)}")
+
+    updated = (history + [[req.message, response_text]])[-8:]
+    save_history(req.session_id, updated)
+    return ChatResponse(response=response_text, updated_history=updated)

monitor.py

@@ -1,94 +1,52 @@
 import threading
 import time
 import psutil
-import logging
 from collections import deque
+import logging
 import os
-import tracemalloc
-import gc
-import sys
 
-# إنشاء logger
+# ===== إعداد بسيط للسجل (صامت افتراضيًا) =====
+VERBOSE = os.getenv("MONITOR_VERBOSE", "0") == "1"
+LOG_LEVEL = logging.INFO if VERBOSE else logging.WARNING
 logger = logging.getLogger("monitor")
-logger.setLevel(logging.DEBUG)
-
-base_dir = os.path.dirname(os.path.abspath(__file__))
-log_dir = os.path.join(base_dir, "data")
-os.makedirs(log_dir, exist_ok=True)
-log_path = os.path.join(log_dir, "monitor.log")
-
-file_handler = logging.FileHandler(log_path)
-file_handler.setLevel(logging.DEBUG)
-formatter = logging.Formatter("📁 [%(asctime)s] [%(levelname)s] %(message)s")
-file_handler.setFormatter(formatter)
-logger.addHandler(file_handler)
-
-stream_handler = logging.StreamHandler()
-stream_handler.setFormatter(logging.Formatter("🪵 [%(asctime)s] [%(levelname)s] %(message)s"))
-logger.addHandler(stream_handler)
-
-# تتبع الذاكرة
-tracemalloc.start()
-logger.info("🔍 بدأ تتبع الذاكرة")
+logger.setLevel(LOG_LEVEL)
+if not logger.handlers:
+    sh = logging.StreamHandler()
+    sh.setLevel(LOG_LEVEL)
+    logger.addHandler(sh)
 
+# ===== مخازن المقاييس (CPU/MEM فقط) =====
 cpu_history = deque(maxlen=10)
 mem_history = deque(maxlen=10)
-current_metrics = {'cpu': 0, 'memory': 0}
-
-def analyze_memory_objects():
-    try:
-        objects = gc.get_objects()
-        logger.info(f"📦 عدد كائنات بايثون: {len(objects)}")
-        sizes = {}
-        for obj in objects:
-            try:
-                key = type(obj).__name__
-                sizes[key] = sizes.get(key, 0) + sys.getsizeof(obj)
-            except Exception:
-                continue
-
-        top = sorted(sizes.items(), key=lambda x: x[1], reverse=True)[:10]
-        logger.info("🧮 أعلى 10 أنواع من الكائنات استهلاكًا:")
-        for name, size in top:
-            logger.info(f"🔹 {name}: {size / 1024 / 1024:.2f} MB")
-    except Exception as e:
-        logger.error(f"❌ خطأ أثناء تحليل كائنات الذاكرة: {str(e)}")
+current_metrics = {'cpu': 0.0, 'memory': 0.0}
 
 def monitor_resources():
-    counter = 0
-    logger.info("🔁 بدأ تشغيل مراقبة الموارد")
+    """تحديث نسب CPU و Memory كل 5 ثوانٍ فقط."""
+    logger.debug("بدأت مراقبة CPU/MEM")
     while True:
         try:
-            process = psutil.Process(os.getpid())
-            mem_info = process.memory_info()
+            # نسبة استعمال CPU للنظام كله (متوسط نصف ثانية)
             cpu_percent = psutil.cpu_percent(interval=0.5)
-            mem_percent = psutil.virtual_memory().percent
 
-            current_metrics['cpu'] = cpu_percent
-            current_metrics['memory'] = mem_percent
-            cpu_history.append(cpu_percent)
-            mem_history.append(mem_percent)
+            # نسبة استعمال الذاكرة للنظام كله
+            mem_percent = psutil.virtual_memory().percent
 
-            logger.info(f"🧠 RSS: {mem_info.rss / 1024**2:.2f} MB | VMS: {mem_info.vms / 1024**2:.2f} MB")
-            current, peak = tracemalloc.get_traced_memory()
-            logger.info(f"📊 tracemalloc: الحالي = {current / 1024**2:.2f} MB | الأعلى = {peak / 1024**2:.2f} MB")
+            # تحديث القيم الحالية والتاريخية
+            current_metrics['cpu'] = float(cpu_percent)
+            current_metrics['memory'] = float(mem_percent)
+            cpu_history.append(float(cpu_percent))
+            mem_history.append(float(mem_percent))
 
-            # كل دقيقة: تحليل إضافي
-            counter += 1
-            if counter % 12 == 0:
-                snapshot = tracemalloc.take_snapshot()
-                top_stats = snapshot.statistics('lineno')
-                logger.info("📌 أعلى 5 أسطر استهلاكًا للذاكرة:")
-                for stat in top_stats[:5]:
-                    logger.info(f"   {stat}")
-                analyze_memory_objects()
+            if VERBOSE:
+                logger.info(f"CPU: {cpu_percent:.1f}% | MEM: {mem_percent:.1f}%")
 
         except Exception as e:
-            logger.exception(f"❌ استثناء في مراقبة الموارد: {str(e)}")
+            logger.exception(f"مشكلة في مراقبة الموارد: {e}")
 
         time.sleep(5)
 
 def get_current_metrics():
+    """تُستخدم من مسار /metrics لإرجاع القيم للواجهة."""
     return {
         'cpu': current_metrics['cpu'],
         'memory': current_metrics['memory'],
@@ -97,6 +55,7 @@ def get_current_metrics():
     }
 
 def start_monitoring_thread():
-    thread = threading.Thread(target=monitor_resources, daemon=True)
-    thread.start()
-    logger.info("✅ تم بدء مراقبة الموارد في خيط منفصل")
+    """تشغيل المراقبة في خيط منفصل."""
+    t = threading.Thread(target=monitor_resources, daemon=True)
+    t.start()
+    logger.debug("تم تشغيل خيط مراقبة CPU/MEM")

requirements.txt

@@ -4,4 +4,8 @@ huggingface_hub==0.23.0
 llama-cpp-python==0.2.77
 psutil==5.9.8
 aiosqlite==0.20.0
-python-multipart==0.0.9
+python-multipart==0.0.9
+sentence-transformers==2.7.0
+faiss-cpu==1.8.0
+networkx==3.3
+transformers==4.42.4