Update app.py

app.py

@@ -1,47 +1,90 @@
-from fastapi import FastAPI, Request
-from fastapi.responses import StreamingResponse
-from openai import OpenAI
 import os
+import uuid
+import pypdf
 import chromadb
+from fastapi import FastAPI, Request, File, UploadFile, BackgroundTasks
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+from openai import OpenAI
+from chromadb.utils import embedding_functions
+
+# --- الإعدادات الفنية ---
+app = FastAPI(title="Neural RAG Engine")
 
-app = FastAPI()
+# تفعيل CORS للسماح لواجهات Next.js بالاتصال بالخادم
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-# إعدادات الذاكرة (نفس منطق الكود السابق)
-STORAGE_PATH = "./neural_memory"
+STORAGE_PATH = "/data/neural_memory" if os.path.exists("/data") else "./neural_memory"
 chroma_client = chromadb.PersistentClient(path=STORAGE_PATH)
-collection = chroma_client.get_or_create_collection(name="advanced_brain_v6")
+default_ef = embedding_functions.DefaultEmbeddingFunction()
+collection = chroma_client.get_or_create_collection(name="advanced_brain_v6", embedding_function=default_ef)
 
 client = OpenAI(
     base_url="https://router.huggingface.co/hf-inference/v1",
     api_key=os.getenv("HF_TOKEN")
 )
 
+# --- وظائف المعالجة ---
+def ingest_document(file_content, filename):
+    # (نفس منطق التقطيع الاحترافي السابق مع الـ Overlap)
+    text = file_content.decode("utf-8", errors="ignore")
+    # إذا كان PDF يحتاج لمكتبة pypdf (يمكن دمجها هنا)
+    chunk_size, overlap = 1000, 200
+    chunks = [text[i : i + chunk_size] for i in range(0, len(text), chunk_size - overlap)]
+    ids = [str(uuid.uuid4()) for _ in chunks]
+    metadatas = [{"source": filename} for _ in chunks]
+    collection.add(documents=chunks, ids=ids, metadatas=metadatas)
+
+# --- الـ Endpoints المتوافقة مع Next.js ---
+
+@app.get("/")
+def home():
+    return {"status": "Neural Engine is Running", "docs_in_memory": collection.count()}
+
+@app.post("/upload")
+async def upload_file(background_tasks: BackgroundTasks, file: UploadFile = File(...)):
+    content = await file.read()
+    background_tasks.add_task(ingest_document, content, file.filename)
+    return {"message": f"Processing {file.filename} in background..."}
+
 @app.post("/v1/chat/completions")
-async def chat_proxy(request: Request):
-    data = await request.json()
-    messages = data.get("messages", [])
-    user_query = messages[-1]["content"]
+async def chat_endpoint(request: Request):
+    """هذا المسار يجعل الخادم متوافقاً تماماً مع واجهات Next.js"""
+    body = await request.json()
+    messages = body.get("messages", [])
+    temperature = body.get("temperature", 0.7)
     
-    # البحث في الذاكرة
+    # البحث الدلالي (RAG) بناءً على آخر رسالة
+    user_query = messages[-1]["content"] if messages else ""
     results = collection.query(query_texts=[user_query], n_results=3)
     knowledge = "\n".join(results['documents'][0]) if results['documents'] else ""
     
-    # حقن المعرفة في أول رسالة (System Prompt)
-    messages.insert(0, {"role": "system", "content": f"Context: {knowledge}"})
+    # حقن المعرفة
+    system_instruction = f"Context from Memory:\n{knowledge}\n\nAnswer based on this context."
+    messages.insert(0, {"role": "system", "content": system_instruction})
 
-    def stream_response():
+    def stream_gen():
         response = client.chat.completions.create(
             model="huihui-ai/Qwen2.5-72B-Instruct-abliterated",
             messages=messages,
+            temperature=temperature,
             stream=True
         )
         for chunk in response:
-            if chunk.choices[0].delta.content:
-                yield f"data: {chunk.choices[0].delta.content}\n\n"
+            content = chunk.choices[0].delta.content
+            if content:
+                # التنسيق المطلوب لـ Server-Sent Events (SSE)
+                yield f"data: {content}\n\n"
         yield "data: [DONE]\n\n"
 
-    return StreamingResponse(stream_response(), media_type="text/event-stream")
+    return StreamingResponse(stream_gen(), media_type="text/event-stream")
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    # تشغيل الخادم
+    uvicorn.run(app, host="0.0.0.0", port=7860)