Update README.md

README.md

@@ -21,5 +21,36 @@ The sentiment analysis model is trained using a Support Vector Machine (SVM) cla
 
 # Usage :
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/65dd9dc387458446d0a9da32/MlAfwDkAouKP5iWtzqHPj.png)
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+from sklearn.preprocessing import LabelEncoder
 
+# Download and load the sentiment analysis model from Hugging Face Model Hub
+model = joblib.load(hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib"))
+
+# Load the TF-IDF vectorizer
+tfidf_vectorizer = joblib.load(hf_hub_download("DineshKumar1329/Sentiment_Analysis", "vectorizer_model.joblib"))
+
+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
+
+# Get user input
+user_input = input("Enter a sentence: ")
+
+# Predict sentiment
+predicted_sentiment = predict_sentiment(user_input)
+
+# Output the prediction
+print(f"Predicted Sentiment: {predicted_sentiment}")