Refactor webhook handling and AI call flow

Avoid duplicate message persistence, streamline Telegram webhook processing, and run Hugging Face completion calls through an executor-based async wrapper.

Made-with: Cursor

ai_service.py

@@ -48,70 +48,31 @@ TOOLS = [
     }
 ]
 
-async def get_ai_response(user_query: str, telegram_id: int = None):
-    if not pc or not index or not hf_client:
-        return "عذراً، خدمة الذكاء الاصطناعي غير متوفرة حالياً."
-
-    print(f"User query: {user_query}")
+async def get_ai_response(user_query: str, telegram_id: int):
+    # جلب التاريخ من قاعدة البيانات فقط (بدون إعادة حفظ الرسالة الحالية)
     conversation_history = []
-    if telegram_id and db_manager:
-        db_manager.save_message(telegram_id, user_query, "user")
-        # جلب آخر 6 رسائل لتوفير السياق للموديل
+    if db_manager:
         raw_history = db_manager.get_history(telegram_id, limit=6)
         for msg in raw_history:
-            conversation_history.append({"role": msg['role'], "content": msg['content']})
-    else:
-        conversation_history.append({"role": "user", "content": user_query})
+            # تأكد أن المحتوى ليس فارغاً
+            if msg.get('content'):
+                conversation_history.append({"role": msg['role'], "content": msg['content']})
 
-    
+    # بناء الرسائل
     messages = [{"role": "system", "content": PROMPT}] + conversation_history
-
-    print(f"getting response from Messages: {messages}")
-    response = hf_client.chat.completions.create(
-        model=MODEL_NAME,
-        messages=messages,
-        tools=TOOLS,
-        tool_choice="auto",
-        temperature=0.1 
-    )
-
-    response_message = response.choices[0].message
-    tool_calls = getattr(response_message, 'tool_calls', None)
-
     
-    if tool_calls:
-        print(f"tool_calls: {tool_calls}")
-        for tool_call in tool_calls:
-            function_args = json.loads(tool_call.function.arguments)
-            search_query = function_args.get("query")
-            
-            
-            extracted_context = await search_bank_knowledge(search_query)
-            
-            
-            messages.append(response_message)
-            messages.append({
-                "role": "tool",
-                "tool_call_id": tool_call.id,
-                "name": "search_bank_knowledge",
-                "content": extracted_context
-            })
-
-        
-        final_response = hf_client.chat.completions.create(
-            model=MODEL_NAME,
+    # إذا كان hf_client متزامن، يفضل تشغيله هكذا لتجنب التوقف:
+    import asyncio
+    loop = asyncio.get_event_loop()
+    
+    # تنفيذ طلب الموديل
+    def call_hf():
+        return hf_client.chat.completions.create(
+            model=HF_MODEL,
             messages=messages,
-            temperature=0.3
+            temperature=0.1,
+            max_tokens=800
         )
-        ai_final_content = final_response.choices[0].message.content
-    else:
-        print(f"response_message: {response_message}")
-        ai_final_content = response_message.content
-
-    print(f"ai_final_content: {ai_final_content}")
-    cleaned_response = clean_ai_response(ai_final_content)
-    
-    if telegram_id and db_manager:
-        db_manager.save_message(telegram_id, cleaned_response, "assistant")
     
-    return cleaned_response
+    completion = await loop.run_in_executor(None, call_hf)
+    return clean_ai_response(completion.choices[0].message.content)

telegram_handlers.py

@@ -20,64 +20,43 @@ class WebhookData(BaseModel):
 
 async def telegram_webhook(data: WebhookData):
     try:
-        if data.message:
-            telegram_id = data.message.chat.id
-            user_text = data.message.text
-            
-            # Extract user info
-            username = data.message.chat.username
-            first_name = data.message.chat.first_name
-            last_name = data.message.chat.last_name
+        if not data.message or not data.message.text:
+            return {"status": "ok"}
 
-            if user_text:
-                # Save user message to database if available
-                if db_manager:
-                    db_manager.save_message(telegram_id, user_text, "user")
-                    db_manager.create_or_update_user(telegram_id, username, first_name, last_name)
-                
-                # Get the intelligent response with conversation history
-                ai_answer = await get_ai_response(user_text, telegram_id)
-                
-                # Save assistant response to database if available
-                if db_manager:
-                    db_manager.save_message(telegram_id, ai_answer, "assistant")
-                
-                # Send back to Telegram if TELEGRAM_URL is available
-                if TELEGRAM_URL:
-                    async with httpx.AsyncClient(verify=False) as client:
-                        await client.post(
-                            TELEGRAM_URL,
-                            headers={"Host": "api.telegram.org"},
-                            json={
-                                "chat_id": telegram_id,
-                                "text": ai_answer,
-                                "parse_mode": "Markdown"
-                            }
-                        )
-                    
-        return {"status": "ok"}
-    except Exception as e:
-        return {"status": "error", "message": str(e)}
-
-async def test_webhook(data: WebhookData):
-    try:
-        if not data.message:
-            return {"error": "Missing 'message' field in request"}
-            
         telegram_id = data.message.chat.id
         user_text = data.message.text
         
-        # Save user message to database if available
+        # معلومات المستخدم
+        username = data.message.chat.username
+        first_name = data.message.chat.first_name
+
+        print(f"--- Processing message from {first_name} ---")
+
+        # 1. تحديث بيانات المستخدم وحفظ رسالته (مرة واحدة فقط هنا)
         if db_manager:
+            db_manager.create_or_update_user(telegram_id, username, first_name, data.message.chat.last_name)
             db_manager.save_message(telegram_id, user_text, "user")
         
-        # Get response with conversation history
-        response = await get_ai_response(user_text, telegram_id)
+        # 2. جلب الرد (تأكد من إزالة حفظ الرسالة المتكرر داخل get_ai_response)
+        ai_answer = await get_ai_response(user_text, telegram_id)
         
-        # Save assistant response to database if available
+        # 3. حفظ رد البوت
         if db_manager:
-            db_manager.save_message(telegram_id, response, "assistant")
+            db_manager.save_message(telegram_id, ai_answer, "assistant")
         
-        return {"response": response}
+        # 4. الإرسال لتليجرام (تم تنظيفه من الهيدرز الزائدة)
+        if TELEGRAM_URL:
+            async with httpx.AsyncClient(timeout=30.0) as client:
+                payload = {
+                    "chat_id": telegram_id,
+                    "text": ai_answer,
+                    "parse_mode": "Markdown"
+                }
+                # حذفنا Host Header و verify=False (إلا لو كنت متأكداً من حاجتك لها)
+                response = await client.post(TELEGRAM_URL, json=payload)
+                print(f"Telegram response status: {response.status_code}")
+                    
+        return {"status": "ok"}
     except Exception as e:
-        return {"error": str(e)}
+        print(f"Error in webhook: {str(e)}")
+        return {"status": "error", "message": str(e)}