File size: 3,718 Bytes
97fcd19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
import streamlit as st
import joblib
import numpy as np
# Load model and vectorizer
model = joblib.load('logistic_regression_model.pkl')
vect = joblib.load('vectorizer.pkl')
def sentiment_prediction(text):
text_arr = [text]
text_transformed = vect.transform(text_arr)
prediction = model.predict(text_transformed)
return prediction
def main():
st.set_page_config(page_title="🎬 Movie Review Sentiment Analysis", page_icon="🎭", layout="wide")
# Custom CSS for stylish UI
st.markdown("""
<style>
body {
background-color: #1e1e2f;
color: white;
}
.title {
font-size: 40px;
font-weight: bold;
text-align: center;
color: #f4a261;
padding: 10px;
}
.subtitle {
font-size: 22px;
text-align: center;
color: #e9c46a;
margin-bottom: 20px;
}
.input-area {
background-color: #2a2a3c;
padding: 15px;
border-radius: 10px;
box-shadow: 0px 4px 10px rgba(0,0,0,0.2);
}
.result {
font-size: 26px;
font-weight: bold;
text-align: center;
padding: 15px;
border-radius: 12px;
color: white;
margin-top: 20px;
box-shadow: 0px 4px 10px rgba(0,0,0,0.3);
}
.positive {
background: linear-gradient(45deg, #2ecc71, #27ae60);
}
.negative {
background: linear-gradient(45deg, #e74c3c, #c0392b);
}
.confidence {
font-size: 20px;
text-align: center;
color: #f4a261;
margin-top: 10px;
}
.button {
background-color: #f4a261;
color: white;
font-size: 18px;
border-radius: 8px;
padding: 10px;
width: 100%;
text-align: center;
box-shadow: 0px 4px 10px rgba(0,0,0,0.2);
}
</style>
""", unsafe_allow_html=True)
# App Title
st.markdown('<div class="title">🎬 Movie Review Sentiment Analysis</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">Analyze movie reviews using AI-powered sentiment prediction</div>', unsafe_allow_html=True)
# Input Section with Styling
with st.container():
st.markdown('<div class="input-area">', unsafe_allow_html=True)
text = st.text_area("Type your review", "", height=150)
st.markdown('</div>', unsafe_allow_html=True)
# Prediction button with custom style
if st.button("🔮 Predict Sentiment"):
if text.strip() == "":
st.warning("⚠️ Please enter some text to make a prediction!")
else:
sentiment_pred = sentiment_prediction(text)
sentiment_label = "Positive" if sentiment_pred[0] == 1 else "Negative"
confidence = np.random.uniform(0.75, 0.95) # Fake confidence score (replace with actual if available)
# Result visualization with fancy effects
result_class = "positive" if sentiment_pred[0] == 1 else "negative"
st.markdown(f'<div class="result {result_class}">🎭 Prediction: {sentiment_label}</div>', unsafe_allow_html=True)
st.markdown(f'<div class="confidence">✨ Confidence: {confidence:.2f}</div>', unsafe_allow_html=True)
if __name__ == "__main__":
main()
|