Update README.md

README.md

@@ -19,82 +19,31 @@ 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
-
-model = joblib.load(
-    hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
-)
-
-tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path
-
-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
-
-user_input = input("Enter a sentence: ")
-
-predicted_sentiment = predict_sentiment(user_input)
-
-print(f"Predicted Sentiment: {predicted_sentiment}")
-
-
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from sklearn.preprocessing import LabelEncoder
-import joblib
-
-
-def load_model_and_tokenizer(model_name="DineshKumar1329/Sentiment_Analysis"):
-    """Loads the sentiment analysis model and tokenizer from Hugging Face Hub."""
-
-    # Replace with desired model name if using a different model
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    return model, tokenizer
-
-
-def clean_text(text):
-    """Converts the input text to lowercase for case-insensitive processing."""
+  <!-- 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")
+  )
+  
+  tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your path
+  
+  def clean_text(text):
     return text.lower()
-
-
-def predict_sentiment(user_input, model, tokenizer):
+  
+  def predict_sentiment(user_input):
     """Predicts sentiment for a given user input."""
-
     cleaned_text = clean_text(user_input)
-    encoded_text = tokenizer(cleaned_text, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model(**encoded_text)
-        logits = outputs.logits
-        prediction = torch.argmax(logits, dim=-1).item()
-
-    if isinstance(model.config.label_list, LabelEncoder):
-        prediction = model.config.label_list.inverse_transform([prediction])[0]
-
+    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
-
-
-if __name__ == "__main__":
-    model, tokenizer = load_model_and_tokenizer()
-
-    user_input = input("Enter a sentence: ")
-
-    predicted_sentiment = predict_sentiment(user_input, model, tokenizer)
-
-    print(f"Predicted Sentiment: {predicted_sentiment}")
+  
+  user_input = input("Enter a sentence: ")
+  predicted_sentiment = predict_sentiment(user_input)
+  print(f"Predicted Sentiment: {predicted_sentiment}")
+ -->