Update src/inference.py

src/inference.py

@@ -5,41 +5,65 @@ 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
 
-# ─── Adjusted BASE_PATH to point at ../models ──────────────────────────────────
+# Define path to models folder, adjust as needed
 BASE_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "models"))
 
-# Load models and TF-IDF vectorizer from the models/ folder at repo root
-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"))
+# Load models and vectorizer safely with error handling
+try:
+    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"))
+except Exception as e:
+    print(f"Error loading models or vectorizer: {e}")
+    model_issue = None
+    model_urgency = None
+    tfidf = None
 
 def predict_ticket(ticket_text):
-    # Preprocess
-    clean = clean_text(ticket_text)
-    # TF-IDF transform
-    X_tfidf = tfidf.transform([clean])
-    # Additional features
-    ticket_length = len(clean.split())
-    sentiment = TextBlob(clean).sentiment.polarity
-
-    # Combine features (sparse + dense)
-    X_features = scipy.sparse.hstack([
-        X_tfidf,
-        np.array([[ticket_length]]),
-        np.array([[sentiment]])
-    ])
-
-    # Predictions
-    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
-    }
+    if not all([model_issue, model_urgency, tfidf]):
+        return {
+            "issue_type": "Model not loaded",
+            "urgency_level": "Model not loaded",
+            "entities": {}
+        }
+    try:
+        # Preprocess text
+        clean = clean_text(ticket_text)
+
+        # Transform text with loaded TF-IDF vectorizer
+        X_tfidf = tfidf.transform([clean])
+
+        # Additional numeric features
+        ticket_length = len(clean.split())
+        sentiment = TextBlob(clean).sentiment.polarity
+
+        # Combine sparse and dense features
+        X_features = scipy.sparse.hstack([
+            X_tfidf,
+            np.array([[ticket_length]]),
+            np.array([[sentiment]])
+        ])
+
+        # Predict using models
+        issue_pred = model_issue.predict(X_features)[0]
+        urgency_pred = model_urgency.predict(X_features)[0]
+
+        # Extract entities from original text
+        entities = extract_entities(ticket_text)
+
+        return {
+            "issue_type": issue_pred,
+            "urgency_level": urgency_pred,
+            "entities": entities
+        }
+
+    except Exception as e:
+        # Catch any runtime errors and report them for debugging
+        return {
+            "issue_type": f"Prediction error: {str(e)}",
+            "urgency_level": f"Prediction error: {str(e)}",
+            "entities": {}
+        }