diff --git a/analyzer/auth.py b/analyzer/auth.py
index a632791..9d32993 100644
--- a/analyzer/auth.py
+++ b/analyzer/auth.py
@@ -12,10 +12,6 @@
 # Password hashing
 pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
 
-# Token model
-class TokenData(BaseModel):
-    username: str | None = None
-
 # Users and hashed passwords
 fake_users_db = {
     "testuser": {
@@ -24,23 +20,33 @@ class TokenData(BaseModel):
     }
 }
 
+
+# Token model
+class TokenData(BaseModel):
+    username: str | None = None
+
+
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
 
+
 def verify_password(plain_password, hashed_password):
     return pwd_context.verify(plain_password, hashed_password)
 
+
 def get_user(db, username: str):
     if username in db:
         user_dict = db[username]
         return user_dict
     return None
 
+
 def authenticate_user(username: str, password: str):
     user = get_user(fake_users_db, username)
     if not user or not verify_password(password, user["hashed_password"]):
         return False
     return user
 
+
 def create_access_token(data: dict, expires_delta: timedelta | None = None):
     to_encode = data.copy()
     if expires_delta:
@@ -51,6 +57,7 @@ def create_access_token(data: dict, expires_delta: timedelta | None = None):
     encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
     return encoded_jwt
 
+
 def get_current_user(token: str = Depends(oauth2_scheme)):
     credentials_exception = HTTPException(
         status_code=status.HTTP_401_UNAUTHORIZED,
diff --git a/analyzer/main.py b/analyzer/main.py
index 945ee5c..b5784a9 100644
--- a/analyzer/main.py
+++ b/analyzer/main.py
@@ -2,9 +2,13 @@
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from datetime import timedelta
-from model import make_prediction 
-from mongodb.save_results import save_prediction, get_all_predictions 
-from auth import create_access_token, authenticate_user, get_current_user  # Authentication functions
+from model import make_prediction
+from mongodb.save_results import save_prediction, get_all_predictions
+from auth import (
+    create_access_token,
+    authenticate_user,
+    get_current_user,
+)
 
 app = FastAPI()
 
@@ -25,14 +29,17 @@
     "last_trained": "2024-09-28",
 }
 
+
 # Define the request models
 class TextInput(BaseModel):
     text: str
 
+
 class UserLogin(BaseModel):
     username: str
     password: str
 
+
 @app.post("/token")
 def login_for_access_token(form_data: UserLogin):
     # Endpoint to authenticate a user and generate a JWT token.
@@ -48,6 +55,7 @@ def login_for_access_token(form_data: UserLogin):
     )
     return {"access_token": access_token, "token_type": "bearer"}
 
+
 @app.post("/predict", dependencies=[Depends(get_current_user)])
 def predict_text(input: TextInput):
     # Endpoint to make a single prediction.
@@ -55,6 +63,7 @@ def predict_text(input: TextInput):
     save_prediction(input.text, prediction)  # Save the prediction to MongoDB
     return {"prediction": prediction}
 
+
 @app.get("/predictions", dependencies=[Depends(get_current_user)])
 def get_predictions():
     # Endpoint to retrieve all past predictions from the database.
@@ -63,6 +72,7 @@ def get_predictions():
         raise HTTPException(status_code=404, detail="No predictions found.")
     return predictions
 
+
 @app.get("/model_metadata")
 def model_metadata():
     # Endpoint to retrieve metadata about the model.
diff --git a/analyzer/model.py b/analyzer/model.py
index ba87cd2..532f7c7 100644
--- a/analyzer/model.py
+++ b/analyzer/model.py
@@ -1,6 +1,6 @@
 import joblib
 import os
-from preprocess import preprocess_text  # Assuming you have preprocessing functions in preprocess.py
+from preprocess import preprocess_text 
 
 MODEL_PATH = os.path.join("results", "text_classifier.joblib")
 VECTORIZER_PATH = os.path.join("results", "tfidf_vectorizer.joblib")
diff --git a/analyzer/preprocess.py b/analyzer/preprocess.py
index 10bb83f..fa144da 100644
--- a/analyzer/preprocess.py
+++ b/analyzer/preprocess.py
@@ -3,15 +3,8 @@
 nlp = spacy.load("en_core_web_sm")
 
 def preprocess_text(text):
-    """
-    Function to preprocess the input text by tokenizing, 
-    removing stop words, and lemmatizing.
-    """
-    # Tokenize and process the text us spacy
+    # Tokenize and process the text using spacy
     doc = nlp(text)
-
     # Filter tokens: Remove stopwords and puctiation, then lemmatize
     clean_tokens = [token.lemma_ for token in doc if not token.is_stop and not token.is_punct]
-
     return ' '.join(clean_tokens)
-
diff --git a/analyzer/requirements.txt b/analyzer/requirements.txt
index d708883..aaa3de9 100644
--- a/analyzer/requirements.txt
+++ b/analyzer/requirements.txt
@@ -8,6 +8,5 @@ pymongo
 pytest
 httpx
 python-jose
-jose
 passlib
 bandit
diff --git a/analyzer/results/tfidf_vectorizer.joblib b/analyzer/results/tfidf_vectorizer.joblib
index a244e49..5c5250a 100644
Binary files a/analyzer/results/tfidf_vectorizer.joblib and b/analyzer/results/tfidf_vectorizer.joblib differ
diff --git a/analyzer/test_main.py b/analyzer/test_main.py
index e247cdf..292eafa 100644
--- a/analyzer/test_main.py
+++ b/analyzer/test_main.py
@@ -1,5 +1,3 @@
-# tests/test_main.py
-import pytest
 from fastapi.testclient import TestClient
 from main import app
 
@@ -15,3 +13,4 @@ def test_read_model_metadata():
 def test_predict_unauthorized():
     response = client.post("/predict", json={"text": "sample text"})
     assert response.status_code == 401
+