Update README.md

README.md

@@ -18,50 +18,3 @@ The sentiment analysis model is trained using a Support Vector Machine (SVM) cla
 
 
 
-
-
-- from huggingface_hub import hf_hub_download
-- import joblib
-- from sklearn.preprocessing import LabelEncoder
-
-# Download the sentiment analysis model
-- model = joblib.load(
-    hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
-)
-
-# Load the TF-IDF vectorizer
-tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path
-
-def clean_text(text):
-  # Implement your text cleaning logic here (e.g., lowercase, remove punctuation, etc.)
-  # This example simply lowercases the 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
-
-# Get user input
-user_input = input("Enter a sentence: ")
-
-# Predict sentiment
-predicted_sentiment = predict_sentiment(user_input)
-
-print(f"Predicted Sentiment: {predicted_sentiment}")
-
-# Optional: Save predictions (modify paths as needed)
-try:
-  df_existing = pd.read_excel('/content/output_predictions.xlsx')
-except FileNotFoundError:
-  df_existing = pd.DataFrame()
-
-new_prediction = pd.DataFrame({'User_Input': [user_input], 'Predicted_Sentiment': [predicted_sentiment]})
-df_combined = pd.concat([df_existing, new_prediction], ignore_index=True)
-df_combined.to_excel('/content/output_predictions.xlsx', index=False)