Update src/inference.py

src/inference.py

@@ -3,67 +3,49 @@ import numpy as np
 import joblib
 import scipy.sparse
 from textblob import TextBlob
+import nltk
+
+# Download NLTK punkt tokenizer to avoid runtime errors
+nltk.download('punkt')
 
 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
 
-# Define path to models folder, adjust as needed
+# Define the path to the models directory
 BASE_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "models"))
 
-# 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
+# Load models and vectorizer
+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):
-    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": {}
-        }
+    # Preprocess the input ticket text
+    clean = clean_text(ticket_text)
+    
+    # TF-IDF transformation
+    X_tfidf = tfidf.transform([clean])
+    
+    # Additional features
+    ticket_length = len(clean.split())
+    sentiment = TextBlob(clean).sentiment.polarity
+
+    # Combine sparse TF-IDF with dense features
+    X_features = scipy.sparse.hstack([
+        X_tfidf,
+        np.array([[ticket_length]]),
+        np.array([[sentiment]])
+    ])
+
+    # Make 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
+    }