Update README.md

README.md

@@ -19,31 +19,30 @@ The sentiment analysis model is trained using a Support Vector Machine (SVM) cla
 
 # Usage :
 
-  from huggingface_hub import hf_hub_download
-  import joblib
-  from sklearn.preprocessing import LabelEncoder
+from huggingface_hub import hf_hub_download
+import joblib
+from sklearn.preprocessing import LabelEncoder
   
-  model = joblib.load(
-      hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
-  )
+model = joblib.load(
+  hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
+)
+tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path
   
-  tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path
+def clean_text(text):
+  return text.lower()
   
-  def clean_text(text):
-    return text.lower()
+def predict_sentiment(user_input):
+  """Predicts sentiment for a given user input."""
+   cleaned_text = clean_text(user_input)
+   input_matrix = tfidf_vectorizer.transform([cleaned_text])
+   prediction = model.predict(input_matrix)[0]
+   if isinstance(model.classes_, LabelEncoder):
+    prediction = model.classes_.inverse_transform([prediction])[0]
+  return prediction
   
-  def predict_sentiment(user_input):
-    """Predicts sentiment for a given user input."""
-    cleaned_text = clean_text(user_input)
-    input_matrix = tfidf_vectorizer.transform([cleaned_text])
-    prediction = model.predict(input_matrix)[0]
-    if isinstance(model.classes_, LabelEncoder):
-      prediction = model.classes_.inverse_transform([prediction])[0]
-    return prediction
-  
-  user_input = input("Enter a sentence: ")
-  predicted_sentiment = predict_sentiment(user_input)
-  print(f"Predicted Sentiment: {predicted_sentiment}")
+user_input = input("Enter a sentence: ")
+predicted_sentiment = predict_sentiment(user_input)
+print(f"Predicted Sentiment: {predicted_sentiment}")