Update inference.py

inference.py

@@ -1,42 +1,39 @@
-import os
-from src.preprocessing import clean_text  # updated imports with src.
-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
-
-# Define base path to notebooks folder relative to this file
-BASE_PATH = os.path.join(os.path.dirname(__file__), '..', 'notebooks')
-
-# Load saved models and vectorizer once from notebooks folder
-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'))
-
-def predict_ticket(ticket_text):
-    clean = clean_text(ticket_text)
-    # Create features
-    X_tfidf = tfidf.transform([clean])
-    ticket_length = len(clean.split())
-    
-    from textblob import TextBlob
-    sentiment = TextBlob(clean).sentiment.polarity
-    
-    # Combine features (make sure shape matches training)
-    import scipy.sparse
-    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,
-        'entities': entities
-    }
+import os
+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
+
+# ✅ Correct path to model files
+BASE_PATH = os.path.join(os.path.dirname(__file__), 'models')
+
+# ✅ Load models from correct location
+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'))
+
+def predict_ticket(ticket_text):
+    clean = clean_text(ticket_text)
+    X_tfidf = tfidf.transform([clean])
+    ticket_length = len(clean.split())
+    sentiment = TextBlob(clean).sentiment.polarity
+    
+    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,
+        'entities': entities
+    }