Update llm_model.py

llm_model.py

@@ -1,19 +1,24 @@
 import torch
 import traceback
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
 from log import log
 from pydantic import BaseModel
 import os
+import json
 
 class Message(BaseModel):
     user_input: str
 
 class LLMModel:
     def __init__(self):
-        self.model = None
+        self.model = None  # ana model
         self.tokenizer = None
         self.eos_token_id = None
 
+        self.intent_model = None  # intent modeli
+        self.intent_tokenizer = None
+        self.intent_label2id = None
+
     def setup(self, s_config, project_config, project_path):
         try:
             log("🧠 LLMModel setup() başladı")
@@ -27,7 +32,7 @@ class LLMModel:
                 log(f"📦 Hugging Face cloud modeli yükleniyor: {model_base}")
                 self.tokenizer = AutoTokenizer.from_pretrained(model_base, token=token, use_fast=False)
                 self.model = AutoModelForCausalLM.from_pretrained(model_base, token=token, torch_dtype=torch.float32).to(device)
-            elif s_config.work_mode == "cloud" or s_config.work_mode == "on-prem":
+            elif s_config.work_mode in ["cloud", "on-prem"]:
                 log(f"📦 Model indiriliyor veya yükleniyor: {model_base}")
                 self.tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
                 self.model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float32).to(device)
@@ -44,6 +49,18 @@ class LLMModel:
             log(f"❌ LLMModel setup() hatası: {e}")
             traceback.print_exc()
 
+    def load_intent_model(self, model_path):
+        try:
+            log(f"🔧 Intent modeli yükleniyor: {model_path}")
+            self.intent_tokenizer = AutoTokenizer.from_pretrained(model_path)
+            self.intent_model = AutoModelForSequenceClassification.from_pretrained(model_path)
+            with open(os.path.join(model_path, "label2id.json")) as f:
+                self.intent_label2id = json.load(f)
+            log("✅ Intent modeli yüklemesi tamamlandı.")
+        except Exception as e:
+            log(f"❌ Intent modeli yükleme hatası: {e}")
+            traceback.print_exc()
+
     async def generate_response(self, text, project_config):
         messages = [{"role": "user", "content": text}]
         encodeds = self.tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
@@ -75,4 +92,4 @@ class LLMModel:
             if start != -1:
                 decoded = decoded[start + len(tag):].strip()
                 break
-        return decoded, top_conf
+        return decoded, top_conf