Update src/inference.py

src/inference.py

@@ -1,18 +1,17 @@
 import os
-import numpy as np
-import joblib
-import scipy.sparse
-from textblob import TextBlob
-
 from src.preprocessing import clean_text
 from src.features import create_features
 from src.model import load_model
 from src.entity_extraction import extract_entities
+import numpy as np
+import joblib
+import scipy.sparse
+from textblob import TextBlob
 
-# Set BASE_PATH to the absolute path of the models directory relative to this file
+# ✅ Correct path to models folder relative to this file
 BASE_PATH = os.path.join(os.path.dirname(__file__), '..', 'models')
 
-# Load models & tfidf vectorizer from models directory
+# ✅ Load models
 model_issue = load_model(os.path.join(BASE_PATH, 'model_issue_type.pkl'))
 model_urgency = load_model(os.path.join(BASE_PATH, 'model_urgency_level.pkl'))
 tfidf = joblib.load(os.path.join(BASE_PATH, 'tfidf.pkl'))
@@ -22,18 +21,17 @@ def predict_ticket(ticket_text):
     X_tfidf = tfidf.transform([clean])
     ticket_length = len(clean.split())
     sentiment = TextBlob(clean).sentiment.polarity
-    
-    # Combine features: tfidf + ticket length + sentiment
+
     X_features = scipy.sparse.hstack([
         X_tfidf,
         np.array([[ticket_length]]),
         np.array([[sentiment]])
     ])
-    
+
     issue_pred = model_issue.predict(X_features)[0]
     urgency_pred = model_urgency.predict(X_features)[0]
     entities = extract_entities(ticket_text)
-    
+
     return {
         'issue_type': issue_pred,
         'urgency_level': urgency_pred,