Update model.py

model.py

@@ -1,34 +1,35 @@
-import joblib
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.decomposition import TruncatedSVD
-
-# Define file paths
-MODEL_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\random_forest_model.pkl"
-VECTORIZER_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\vectorizer.pkl"
-SVD_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\svd.pkl"
-
-try:
-    # Load the trained model, vectorizer, and SVD transformer
-    model = joblib.load(MODEL_PATH)
-    vectorizer = joblib.load(VECTORIZER_PATH)
-    svd = joblib.load(SVD_PATH)
-    print("✅ Model, vectorizer, and SVD loaded successfully!")
-except Exception as e:
-    print(f"❌ Error loading model files: {e}")
-    exit(1)
-
-def predict_text(text: str) -> int:
-    """Preprocess input text and predict using trained model."""
-    try:
-        X_tfidf = vectorizer.transform([text])  # Convert to TF-IDF
-        X_reduced = svd.transform(X_tfidf)  # Apply dimensionality reduction
-        prediction = model.predict(X_reduced)[0]  # Predict label (0 or 1)
-
-        probability = float(model.predict_proba(X_reduced)[0][1])
-        print(probability) #printing probability
-
-        return {"generated": int(prediction), "probability": probability}
-    except Exception as e:
-        print(f"❌ Prediction error: {e}")
-        return {"generated": -1, "probability": 0.0}
-        
+import joblib
+import os
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.decomposition import TruncatedSVD
+
+# Define relative file paths (assuming 'models/' is in the same directory as this script)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_PATH = os.path.join(BASE_DIR, "models", "random_forest_model.pkl")
+VECTORIZER_PATH = os.path.join(BASE_DIR, "models", "vectorizer.pkl")
+SVD_PATH = os.path.join(BASE_DIR, "models", "svd.pkl")
+
+try:
+    # Load the trained model, vectorizer, and SVD transformer
+    model = joblib.load(MODEL_PATH)
+    vectorizer = joblib.load(VECTORIZER_PATH)
+    svd = joblib.load(SVD_PATH)
+    print("✅ Model, vectorizer, and SVD loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading model files: {e}")
+    exit(1)
+
+def predict_text(text: str) -> dict:
+    """Preprocess input text and predict using trained model."""
+    try:
+        X_tfidf = vectorizer.transform([text])  # Convert to TF-IDF
+        X_reduced = svd.transform(X_tfidf)  # Apply dimensionality reduction
+        prediction = model.predict(X_reduced)[0]  # Predict label (0 or 1)
+
+        probability = float(model.predict_proba(X_reduced)[0][1])
+        print(probability)  # Printing probability
+
+        return {"generated": int(prediction), "probability": probability}
+    except Exception as e:
+        print(f"❌ Prediction error: {e}")
+        return {"generated": -1, "probability": 0.0}