Update intent.py

intent.py

@@ -10,7 +10,7 @@ global INTENT_MODEL, INTENT_TOKENIZER, LABEL2ID
 class TrainInput(BaseModel):
     intents: list
 
-def background_training(intents, app_config):
+def background_training(intents, service_config):
     try:
         log("🔧 Intent eğitimi başlatıldı...")
         texts, labels, label2id = [], [], {}
@@ -21,11 +21,11 @@ def background_training(intents, app_config):
                 labels.append(idx)
 
         dataset = Dataset.from_dict({"text": texts, "label": labels})
-        tokenizer = AutoTokenizer.from_pretrained(app_config.INTENT_MODEL_ID)
-        config = AutoConfig.from_pretrained(app_config.INTENT_MODEL_ID)
+        tokenizer = AutoTokenizer.from_pretrained(service_config.INTENT_MODEL_ID)
+        config = AutoConfig.from_pretrained(service_config.INTENT_MODEL_ID)
         config.problem_type = "single_label_classification"
         config.num_labels = len(label2id)
-        model = AutoModelForSequenceClassification.from_pretrained(app_config.INTENT_MODEL_ID, config=config)
+        model = AutoModelForSequenceClassification.from_pretrained(service_config.INTENT_MODEL_ID, config=config)
 
         tokenized_data = {"input_ids": [], "attention_mask": [], "label": []}
         for row in dataset:
@@ -69,15 +69,15 @@ def background_training(intents, app_config):
         for intent, total in counts.items():
             accuracy = correct.get(intent, 0) / total
             log(f"📊 Intent '{intent}' doğruluk: {accuracy:.2f} — {total} örnek")
-            if accuracy < app_config.TRAIN_CONFIDENCE_THRESHOLD or total < 5:
+            if accuracy < service_config.TRAIN_CONFIDENCE_THRESHOLD or total < 5:
                 log(f"⚠️ Yetersiz performanslı intent: '{intent}' — Doğruluk: {accuracy:.2f}, Örnek: {total}")
 
         log("📦 Intent modeli eğitimi kaydediliyor...")
-        if os.path.exists(app_config.INTENT_MODEL_PATH):
-            shutil.rmtree(app_config.INTENT_MODEL_PATH)
-        model.save_pretrained(app_config.INTENT_MODEL_PATH)
-        tokenizer.save_pretrained(app_config.INTENT_MODEL_PATH)
-        with open(os.path.join(app_config.INTENT_MODEL_PATH, "label2id.json"), "w") as f:
+        if os.path.exists(service_config.INTENT_MODEL_PATH):
+            shutil.rmtree(service_config.INTENT_MODEL_PATH)
+        model.save_pretrained(service_config.INTENT_MODEL_PATH)
+        tokenizer.save_pretrained(service_config.INTENT_MODEL_PATH)
+        with open(os.path.join(service_config.INTENT_MODEL_PATH, "label2id.json"), "w") as f:
             json.dump(label2id, f)
 
         log("✅ Intent eğitimi tamamlandı ve model kaydedildi.")