Fix feedback_store.csv paths, retrain.py syntax errors, and adminSeeder.js typos

backend/retrain.py

@@ -27,14 +27,12 @@
 
 import argparse
 import os
-        feature
 import pickle
 from collections import Counter
 
 import shutil
 import sys
 from datetime import datetime
-        main
 
 import pandas as pd
 import joblib
@@ -57,31 +55,6 @@ def backup_existing_files():
     backup_dir = os.path.join("backups", timestamp)
     files_to_backup = [MODEL_PATH, VECTORIZER_PATH, LABEL_ENCODER_PATH]
     existing = [f for f in files_to_backup if os.path.exists(f)]
-
-        feature
-    label_encoder = LabelEncoder()
-    y = label_encoder.fit_transform(labels)
-
-    # Hold out a test split when there's enough data per class to stratify;
-    # otherwise train on everything and skip the report.
-    counts = Counter(y)
-    can_split = len(samples) >= 5 and min(counts.values()) >= 2
-    if can_split:
-        X_train, X_test, y_train, y_test = train_test_split(
-            texts, y, test_size=0.2, random_state=42, stratify=y
-        )
-    else:
-        X_train, y_train = texts, y
-        X_test, y_test = [], []
-        print("Not enough samples per class for a held-out test split; "
-              "training on all available data.")
-
-    # Fit TF-IDF vectorizer with max_features=5000 to match model
-    print("\nFitting TfidfVectorizer (max_features=5000)...")
-    vectorizer = TfidfVectorizer(max_features=5000)
-    X_train_vec = vectorizer.fit_transform(X_train)
-    print(f"Vocabulary size: {len(vectorizer.vocabulary_)}")
-
     if not existing:
         print("No existing model files found to back up (first-time training).")
         return
@@ -100,7 +73,6 @@ def load_dataset(path):
     df = pd.read_csv(path)
     if "text" not in df.columns and "message" in df.columns:
         df.rename(columns={"message": "text"}, inplace=True)
-        main
 
     if "text" not in df.columns or "label" not in df.columns:
         print("Dataset CSV must have 'text' (or 'message') and 'label' columns.")
@@ -138,15 +110,19 @@ def load_feedback(path):
 
 
 def main():
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    default_feedback = os.path.join(script_dir, "output", "feedback_store.csv")
+    default_dataset = os.path.join(script_dir, "dataset.csv")
+
     parser = argparse.ArgumentParser(description="Retrain spam detection model with feedback data")
     parser.add_argument(
         "--dataset",
-        default=os.environ.get("DATASET_PATH", "dataset.csv"),
+        default=os.environ.get("DATASET_PATH", default_dataset),
         help="Path to original training dataset CSV (default: dataset.csv or $DATASET_PATH)",
     )
     parser.add_argument(
         "--feedback",
-        default="feedback_store.csv",
+        default=default_feedback,
         help="Path to feedback CSV collected from /feedback endpoint",
     )
     parser.add_argument(

backend/seeders/adminSeeder.js

@@ -14,8 +14,8 @@ const seedAdminUser = async () => {
                 name: 'Admin'
             });
 
-            conso;e.log('Admin user created successfully');
-            cnsole.log(`Email: ${email}`);
+            console.log('Admin user created successfully');
+            console.log(`Email: ${email}`);
             console.log(`Password: ${password}`);
         }
     }catch(error){

backend/spam_insights.py

@@ -95,7 +95,7 @@ def load_data():
 
     # Try feedback_store.csv in backend directory
     base_dir = os.path.dirname(__file__)
-    feedback_path = os.path.join(base_dir, "feedback_store.csv")
+    feedback_path = os.path.join(base_dir, "output", "feedback_store.csv")
     if os.path.isfile(feedback_path):
         try:
             with open(feedback_path, newline="", encoding="utf-8") as f: