app.py

import streamlit as st
import joblib

# Load saved models
TF_IDF = joblib.load('tfidf_vectorizer.pkl')
svd = joblib.load('dimensionality_reduction.pkl')
RF = joblib.load('random_forest_model.pkl')

# Custom CSS for styling
st.markdown(
    """
    <style>
    /* Set background image for the entire app */
    .stApp {
        background: url('https://wallpaperaccess.com/full/1436813.jpg') no-repeat center center fixed;
        background-size: cover;
    }
    
    /* Style for the title */
    .stApp h1 {
        background-color: rgba(0, 0, 128, 0.7); /* Semi-transparent navy blue */
        color: #ffffff; /* White */
        padding: 10px;
        border-radius: 5px;
        font-size: 2.5em; 
        text-align: center;
    }

    /* Style for input text area */
    .stTextArea textarea {
        background-color: rgba(255, 255, 255, 0.8); /* Semi-transparent white */
        color: #000000; /* Black */
        font-size: 1.2em; 
    }

    /* Style for the button */
    .stButton>button {
        background-color: #4CAF50; /* Green */
        color: white;
        font-size: 1.2em;
        border-radius: 10px;
        padding: 10px 24px;
        border: none;
    }

    /* Center the button */
    .stButton {
        display: flex;
        justify-content: center;
    }

    /* Style for the output container */
    .output-container {
        background-color: lightpink;
        color: black;
        font-size: 1.5em;
        padding: 15px;
        border-radius: 10px;
        margin-top: 20px;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
        width: 200%;
        margin-left: auto;
        margin-right: auto;
        text-align: center;
    }

    
    </style>
    """,
    unsafe_allow_html=True
)

# Streamlit App
st.title("Classify Game Feedback")

st.write("Enter text below to classify:")

# Text input from the user
user_input = st.text_area("Input Text", "", key="input_text", help="Type your game review here.")

# Center the predict button
col1, col2, col3 = st.columns([1,2,1])
with col2:
    if st.button("Predict"):
        if user_input.strip() == "":
            st.warning("Please enter some text to classify.")
        else:
            # Transform the input text
            tfidf_input = TF_IDF.transform([user_input])
            reduced_input = svd.transform(tfidf_input)
            
            # Make prediction
            prediction = RF.predict(reduced_input)[0]
            prediction_proba = RF.predict_proba(reduced_input)[0]
            confidence = max(prediction_proba) * 100
            
            if prediction == 1:
                sentiment = "Positive"
                insight = "Users appreciate the engaging gameplay and storyline."
            else:
                sentiment = "Negative"
                insight = "Feedback suggests concerns about game performance and frequent bugs."
            
            # Display the prediction with additional context in a single styled block
            st.markdown(
                f"""
                <div class="output-container">
                    <p>The review is <strong>{sentiment}</strong> with a confidence score of {confidence:.2f}%.</p>
                    <p><em>Insight:</em> {insight}</p>
                </div>
                """,
                unsafe_allow_html=True
            )