enable log

app.py

@@ -2,17 +2,24 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from llama_cpp import Llama
-import uvicorn
+import logging
 import os
 
+# 🔧 إعداد السجل
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="🪵 [%(asctime)s] [%(levelname)s] %(message)s",
+)
+logger = logging.getLogger(__name__)
+
 MODEL_REPO = "QuantFactory/Qwen2.5-7B-Instruct-GGUF"
 MODEL_FILE = "Qwen2.5-7B-Instruct.Q4_K_M.gguf"
 MODEL_PATH = f"/home/user/app/data/cache/{MODEL_FILE}"
 
-# تأكد من أن النموذج موجود
 if not os.path.exists(MODEL_PATH):
     from huggingface_hub import hf_hub_download
     os.makedirs("/home/user/app/data/cache", exist_ok=True)
+    logger.info("📦 تحميل النموذج من Hugging Face Hub...")
     hf_hub_download(
         repo_id=MODEL_REPO,
         filename=MODEL_FILE,
@@ -20,10 +27,9 @@ if not os.path.exists(MODEL_PATH):
     )
 
 if os.path.exists(MODEL_PATH):
-    print(f"Model found at {MODEL_PATH}")
+    logger.info(f"✅ النموذج موجود: {MODEL_PATH}")
 else:
-    print(f"Model not found at {MODEL_PATH}")
-
+    logger.error(f"❌ النموذج غير موجود: {MODEL_PATH}")
 
 # تحميل النموذج
 llm = Llama(
@@ -44,9 +50,13 @@ You are Qwen, created by Alibaba Cloud. You are an AI development assistant. Fol
 # API setup
 app = FastAPI()
 
+@app.on_event("startup")
+async def startup_event():
+    logger.info("🚀 الخادم بدأ بنجاح – /chat جاهز")
+
 class ChatRequest(BaseModel):
     message: str
-    history: list[list[str]] = []  # [[user, assistant], ...]
+    history: list[list[str]] = []
 
 class ChatResponse(BaseModel):
     response: str
@@ -66,6 +76,7 @@ def format_prompt(messages):
 
 @app.post("/chat", response_model=ChatResponse)
 def chat(req: ChatRequest):
+    logger.info(f"📩 طلب جديد: {req.message}")
     messages = [("system", SYSTEM_PROMPT.strip())]
     for user_msg, bot_msg in req.history:
         messages.append(("user", user_msg))
@@ -73,7 +84,11 @@ def chat(req: ChatRequest):
     messages.append(("user", req.message))
 
     prompt = format_prompt(messages)
+    logger.debug(f"📝 prompt المُرسل للنموذج:\n{prompt[:500]}...")
+
     output = llm(prompt, max_tokens=1024, temperature=0.7, top_p=0.9, repeat_penalty=1.05, stop=["<|im_end|>"])
     reply = output["choices"][0]["text"].split("<|im_end|>")[0].strip()
+    logger.info(f"🤖 رد النموذج: {reply}")
+
     req.history.append([req.message, reply])
     return ChatResponse(response=reply, updated_history=req.history)