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app.py

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+# Contents of app.py
+import eda
+import prediction
+import streamlit as st
+
+PAGES = {
+    "Exploratory Data Analysis": eda,
+    "Make Predictions": prediction
+}
+st.sidebar.title('Navigation')
+selection = st.sidebar.radio("Go to", list(PAGES.keys()))
+page = PAGES[selection]
+page.app()

eda.py

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+# Contents of `app1.py`
+import streamlit as st
+
+def app():
+    st.title('Exploratory Data Analysis')
+
+    st.header('Target Label Proportion')
+    st.image('target label proportion.png', caption='Picture 1: Target Label Proportion')
+    st.write('''The plot shows the distribution of user suggestions for a yes or no question. The left pie chart shows that 83.8% of users suggesting yes, while 16.2% suggesting no. 
+                The plot also shows a class imbalance, where one class has a significantly higher proportion of data points compared to the other. In this case, it's a binary classification problem with:''')
+    st.write('''- **Majority Class**: This is the dominant class, represented by the larger pie slice (83.8%) who responded `Yes` in your example.''')
+    st.write('''- **Minority Class**: This is the smaller class, represented by the smaller pie slice (16.2%) who responded `No`.''')
+    
+    
+    st.header('Most Words Appear In Review')
+    st.image('wordcloud.png', caption='Picture 2: Most Words Appear In Review')
+    st.write('''The word cloud depicts words commonly found in skincare product reviews. Here are some of the most prominent words:''')
+    st.write('''- **Moisturizer**: This suggests that the reviews are about a product that hydrates the skin.''')
+    st.write('''- **Skin**: This is not surprising as the word cloud is about product reviews.''')
+    st.write('''- **Love**: People seem to really like this product!''')
+    st.write('''- **Dry**: This might be an indicator that the product is effective for dry skin.''')
+    st.write('''- **Soft**: Another positive descriptor for the product’s effect on skin.''')
+    st.write('''- **Cream**: This could be the type of product being reviewed.''')
+    st.write('''- **Face**: This again reinforces that the reviews are about a facial skin care product.''')
+    st.write('''- **Buy**: This indicates positive sentiment towards the product.''')
+    st.write('''- **Price**: People are considering the price of the product in their reviews.''')
+    st.write('''- **Breakout**: This suggests that some reviewers experienced irritation from the product.''')
+    st.write('''- **Sensitive**: This indicates that the product might not be suitable for all skin types.''')
+
+
+
+if __name__ == '__main__':
+    app()

prediction.py

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+# Contents of `app2.py`
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+# Function to load the model
+@st.cache_data
+def load_sentiment_model():
+    model = load_model('../model.keras')
+    return model
+
+# Function to make predictions
+def predict_sentiment(review_text, model):
+    # Perform prediction
+    pred = model.predict([review_text])
+    prediction = tf.where(pred >= 0.5, 1, 0)
+    
+    # Convert tensor to a list of 1s and 0s
+    predictions_list = prediction.numpy().flatten().tolist()
+    
+    # Replace 1 with 'Recommending' and 0 with 'Not Recommending'
+    predictions_recommended = ['The author recommending this product' if x == 1 else 'The author not recommending this product' for x in predictions_list]
+    
+    return predictions_recommended
+
+# Streamlit app function
+def app():
+    st.title('Make Predictions')
+    
+    # Load the model
+    model = load_sentiment_model()
+    
+    # Text input for user
+    user_input = st.text_area("Enter your review:", "")
+    
+    if st.button("Predict"):
+        if user_input:
+            # Display a loading message while predicting
+            with st.spinner('Predicting...'):
+                # Perform prediction
+                predictions = predict_sentiment(user_input, model)
+                
+                # Display the prediction result
+                st.success(f'Prediction: {predictions[0]}')
+        else:
+            st.warning("Please enter a review.")
+
+if __name__ == '__main__':
+    app()

requirements.txt

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+tensorflow == 2.15.0