Update README.md

README.md

@@ -40,31 +40,37 @@ It classifies emails as **Spam (1)** or **Ham (0)** with high accuracy and fast
 
 ## 💡 How to Use
 ```python
+from huggingface_hub import hf_hub_download
 import joblib
+import string 
 import re
-import string
 
-# Load model and vectorizer
-model = joblib.load("spam_detection_model.pkl")
-vectorizer = joblib.load("spam_detection_vectorizer.pkl")
+# Download and load the vectorizer
+vectorizer_path = hf_hub_download("DarkNeuron-AI/darkneuron-spamdex-v1", "spam_detection_vectorizer.pkl")
+vectorizer = joblib.load(vectorizer_path)
+
+# Download and load the trained model
+model_path = hf_hub_download("DarkNeuron-AI/darkneuron-spamdex-v1", "spam_detection_model.pkl")
+model = joblib.load(model_path)
 
 # Text cleaning function
 def clean_text(text):
     text = text.lower()  # lowercase
     text = re.sub(r'\d+', '', text)  # remove digits
     text = text.translate(str.maketrans('', '', string.punctuation))  # remove punctuation
-    return text.strip()  # remove spaces
+    return text.strip()  # remove extra spaces
 
-# Example email
-email = "Congratulations! You have won $1000. Click here to claim now!"
+# Example usage
+email_text = "Congratulations! You are the topper!"
+cleaned_email = clean_text(email_text)
 
-# Clean and transform
-clean_email = clean_text(email)
-email_vec = vectorizer.transform([clean_email])
+# ✅ Wrap text in a list for vectorizer
+email_vector = vectorizer.transform([cleaned_email])
 
 # Predict
-result = model.predict(email_vec)
-print("Spam" if result[0] == 1 else "Ham")
+prediction = model.predict(email_vector)
+
+print("Prediction:", "🚨 Spam" if prediction[0] == 1 else "✅ Not Spam")
 ```
 
 # Developed With ❤️ By DarkNeuronAI