Update app.py

app.py

@@ -1,102 +1,102 @@
-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)
+import streamlit as st
+import pandas as pd
+import re
+import joblib
+from sklearn.feature_extraction.text import CountVectorizer
+from keras.preprocessing.sequence import pad_sequences
+from keras.preprocessing.text import Tokenizer
+from 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)