Update README.md

README.md

@@ -13,4 +13,51 @@ tags:
 
 ## Model Training
 
-The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as `Sentiment_classifier_model.joblib`, and the corresponding TF-IDF vectorizer is saved as `vectorizer_model.joblib`.
+The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as `Sentiment_classifier_model.joblib`, and the corresponding TF-IDF vectorizer is saved as `vectorizer_model.joblib`.
+
+# Model Usage : 
+
+from huggingface_hub import hf_hub_download
+import joblib
+model = joblib.load(
+	hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
+)
+# only load pickle files from sources you trust
+# read more about it here https://skops.readthedocs.io/en/stable/persistence.html
+
+# Load the TF-IDF vectorizer used during training
+tfidf_vectorizer = joblib.load('/content/vectorizer_model.joblib')  # Replace with your actual filename
+
+
+# Take user input
+user_input = input("Enter a sentence: ")
+
+# Clean the user input
+cleaned_input = clean_text(user_input)
+
+# Transform the cleaned text data using the TF-IDF vectorizer
+input_matrix = tfidf_vectorizer.transform([cleaned_input])
+
+# Make prediction
+prediction = model.predict(input_matrix)[0]
+
+# Display the prediction
+print(f"Predicted Sentiment: {prediction}")
+# Create a DataFrame with the results
+df_result = pd.DataFrame({'User_Input': [user_input], 'Predicted_Sentiment': [prediction]})
+
+# Save the DataFrame to an Excel file (append if the file already exists)
+excel_filename = '/content/output_predictions.xlsx'  # Replace with your desired filename
+try:
+    # Load existing predictions from the Excel file
+    df_existing = pd.read_excel(excel_filename)
+
+    # Append the new predictions to the existing DataFrame
+    df_combined = pd.concat([df_existing, df_result], ignore_index=True)
+
+except FileNotFoundError:
+    # If the file doesn't exist, create a new DataFrame
+    df_combined = df_result
+
+# Save the combined DataFrame to the Excel file
+df_combined.to_excel(excel_filename, index=False)