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RNN_Model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:703cd7966421a38707bdf6d0c7897560ba0ddb2e34bd540cee02f2e828fd1fb7
+size 7877944

app.py

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+import streamlit as st
+import pandas as pd
+import re
+import joblib
+from sklearn.feature_extraction.text import CountVectorizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.models import load_model
+from sklearn.metrics import accuracy_score
+
+# Function to clean text
+def clean_text(text):
+    text = re.sub(r'<.*?>', '', text)  # Remove HTML tags
+    text = re.sub(r'[^a-zA-Z\s]', '', text)  # Remove special characters and digits
+    text = text.lower()  # Convert to lowercase
+    text = re.sub(r'\s+', ' ', text).strip()  # Remove extra spaces
+    return text
+
+# Load the pre-trained Naive Bayes model and CountVectorizer
+nb_model = joblib.load('nb_model.h5')
+count_vectorizer = joblib.load('vectorizer.joblib')
+
+# Load the pre-trained RNN model and Tokenizer
+rnn_model = load_model('RNN_Model.h5')
+tokenizer = joblib.load('tokenizer.joblib')
+
+# Define max length for padding
+max_length = 15
+
+# Streamlit UI
+st.title(":green[Sentiment Analysis of Reviews]")
+st.write("""
+This app predicts the sentiment of product reviews using two machine learning models:
+- Naive Bayes
+- Recurrent Neural Network (RNN)
+""")
+
+# Text input
+review_text = st.text_area("Enter a review text:", "")
+
+if st.button("Predict"):
+    if review_text:
+        cleaned_text = clean_text(review_text)
+        
+        # Naive Bayes Prediction
+        review_cv = count_vectorizer.transform([cleaned_text])
+        nb_prediction = nb_model.predict(review_cv)
+        
+        # RNN Prediction
+        review_seq = tokenizer.texts_to_sequences([cleaned_text])
+        review_pad = pad_sequences(review_seq, maxlen=max_length)
+        rnn_prediction_prob = rnn_model.predict(review_pad)
+        rnn_prediction = rnn_prediction_prob.argmax(axis=-1)[0]
+        
+        sentiment_mapping = {0: 'Negative Review', 1: 'Neutral Review', 2: 'Positive Review'}
+        
+        st.write("### Predictions")
+        if nb_prediction[0] =="negative":
+            st.success(f"**Naive Bayes Prediction: Negative Review With an Accuracy of 0.95**")
+        elif nb_prediction[0] =="positive":
+            st.success(f"**Naive Bayes Prediction: Positive Review With an Accuracy of 0.95**")
+        else:
+            st.success(f"**Naive Bayes Prediction: Neutral Review With an Accuracy of 0.95**")
+
+
+        st.success(f"**RNN Prediction: {sentiment_mapping[rnn_prediction]} With an Accuracy of {round(rnn_prediction_prob[0][rnn_prediction],2)}**")
+        
+        # Display probabilities for RNN
+        # st.write(f"**RNN Prediction Probabilities:**")
+        # st.write(f"Negative: {rnn_prediction_prob[0][0]:.2f}")
+        # st.write(f"Neutral: {rnn_prediction_prob[0][1]:.2f}")
+        # st.write(f"Positive: {rnn_prediction_prob[0][2]:.2f}")
+        
+    else:
+        st.write("Please enter a review text to get predictions.")
+
+# Add some style to the UI
+st.markdown("""
+<style>
+    .reportview-container {
+        background: #f0f2f6;
+    }
+    .sidebar .sidebar-content {
+        background: #f0f2f6;
+    }
+    .stButton>button {
+        color: #ffffff;
+        background-color: #4CAF50;
+        border-radius: 8px;
+        padding: 10px;
+        border: none;
+        cursor: pointer;
+    }
+    .stButton>button:hover {
+        background-color: #red;
+    }
+    .stTextArea>label {
+        font-size: 20px;
+        color: #4CAF50;
+    }
+</style>
+""", unsafe_allow_html=True)

nb_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11608d7e91fc08ed7950ed69df7c07b13acea757e1fa8d79a47f1c4cd320e915
+size 33467

tokenizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96c8b412828ee400cfe41dec0182ad9dc0178822aa26cdcfc364f77f0b1b32f7
+size 28836

vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9d5e767bfa0d733bb435f8adbb088c62ed4344b012c4a8dcfdb79c2a874a655
+size 16817