Update README.md

README.md

@@ -41,4 +41,58 @@ new_text = "I recently started a new fitness program at a local wellness center,
 predicted_sentiment, sentiment_score = predict_sentiment(new_text, model, tokenizer, label_encoder)
 
 print(f"Predicted Sentiment: {predicted_sentiment}")
-print(f"Sentiment Scores: {sentiment_score}")
+print(f"Sentiment Scores: {sentiment_score}")
+
+## Using HDFS (H5)
+
+```python
+import numpy as np
+import pandas as pd
+import re
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+
+# Load the saved Keras model
+model_hybrid = load_model('< DistilSentiNet-42M.h5 File Path >')
+
+# Sample data
+df = pd.read_csv("./train.csv")
+
+# Preprocessing
+df['text'] = df['text'].str.lower().str.replace('[^\w\s]', '', regex=True)
+
+# Encode labels
+label_encoder = LabelEncoder()
+df['label'] = label_encoder.fit_transform(df['sentiment'])
+
+# Tokenization and padding
+tokenizer = Tokenizer(num_words=5000)
+tokenizer.fit_on_texts(df['text'])
+X = tokenizer.texts_to_sequences(df['text'])
+X = pad_sequences(X, maxlen=100)
+
+# Function to predict sentiment of new input text
+def predict_sentiment(text, tokenizer, model):
+    # Preprocess the input text
+    text = text.lower()
+    text = re.sub(r'[^\w\s]', '', text)
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=100)
+    
+    # Predict sentiment
+    pred = model.predict(padded_sequence)
+    sentiment = label_encoder.inverse_transform(pred.argmax(axis=1))
+    sentiment_score = pred[0]
+    
+    return sentiment[0], sentiment_score
+
+# Example usage
+new_text = "I recently started a new fitness program at a local wellness center, and it has been an incredibly positive experience. The trainers are highly knowledgeable and provide personalized guidance to help me achieve my fitness goals. The facilities are state-of-the-art, with a wide range of equipment and classes to choose from. The supportive community and motivating environment have made working out enjoyable and rewarding. I have already noticed significant improvements in my health and fitness levels, and the positive changes have greatly enhanced my overall well-being."
+
+predicted_sentiment, sentiment_score = predict_sentiment(new_text, tokenizer, model_hybrid)
+
+print(f"The sentiment of the input text is: {predicted_sentiment} with scores {sentiment_score}")