Update README.md

README.md

@@ -50,3 +50,51 @@ 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."""
+    return text.lower()
+
+
+def predict_sentiment(user_input, model, tokenizer):
+    """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]
+
+    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}")
+
+