Update src/components/model_nlp_intent.py

src/components/model_nlp_intent.py

@@ -5,21 +5,33 @@ from io import BytesIO
 import joblib
 
 # Load once at startup
-MODEL_PATH = "samithcs/nlp_intent_model/nlp_intent/intent_model"
-TOKENIZER_PATH = "samithcs/nlp_intent_model/nlp_intent/intent_tokenizer"
+
+MODEL_URL = "samithcs/nlp_intent_model"
+MODEL_SUBFOLDER = "nlp_intent/intent_model"
+TOKENIZER_SUBFOLDER = "nlp_intent/intent_tokenizer"
 LABEL_URL = "https://huggingface.co/samithcs/nlp_intent_model/resolve/main/nlp_intent/label_encoder.joblib"
 
-model = TFDistilBertForSequenceClassification.from_pretrained(MODEL_PATH, from_tf=True)
-tokenizer = DistilBertTokenizer.from_pretrained(TOKENIZER_PATH)
+# Load model and tokenizer with subfolder parameter
+model = TFDistilBertForSequenceClassification.from_pretrained(
+    MODEL_URL, 
+    subfolder=MODEL_SUBFOLDER,
+    from_tf=True
+)
+tokenizer = DistilBertTokenizer.from_pretrained(
+    MODEL_URL,
+    subfolder=TOKENIZER_SUBFOLDER
+)
+
+# Load label encoder
 response = requests.get(LABEL_URL)
 label_encoder = joblib.load(BytesIO(response.content))
 
 
-
 def predict_intent(text: str) -> dict:
     inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True, max_length=128)
     outputs = model(inputs)
     predicted_class = tf.argmax(outputs.logits, axis=1).numpy()[0]
     intent = label_encoder.inverse_transform([predicted_class])[0]
     confidence = float(tf.nn.softmax(outputs.logits)[0][predicted_class].numpy())
-    return {"intent": intent, "confidence": confidence}
+    
+    return {"intent": intent, "confidence": confidence}