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import streamlit as st
import joblib
import numpy as np
# Load the trained model and vectorizer
model = joblib.load('logistic_regression_model.pkl')
vect = joblib.load('vectorizer.pkl')
def stress_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="Spam Detection", layout="wide")
# Apply new style
st.markdown("""
<style>
/* Body */
body {
font-family: 'Arial', sans-serif;
background-color: #f4f7fa;
}
.main {
background-color: #ffffff;
border-radius: 12px;
padding: 40px;
box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
max-width: 600px;
margin: 0 auto;
text-align: center;
}
.title {
font-size: 2.8rem;
color: #3366cc;
font-weight: bold;
margin-bottom: 30px;
}
.text-area {
background-color: #f0f5f9;
border: 2px solid #cfd8dc;
border-radius: 10px;
padding: 18px;
font-size: 1.1rem;
width: 100%;
margin-bottom: 20px;
}
.button {
background-color: #3366cc;
color: white;
font-size: 1.2rem;
border-radius: 10px;
padding: 12px 25px;
border: none;
cursor: pointer;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
transition: background-color 0.3s ease;
width: 100%;
}
.button:hover {
background-color: #4a89dc;
}
.result {
font-size: 1.8rem;
font-weight: bold;
color: #ff5e57;
margin-top: 30px;
}
.confidence {
font-size: 1.2rem;
color: #8e8e8e;
margin-top: 15px;
}
.explanation {
font-size: 1rem;
color: #7f7f7f;
margin-top: 10px;
}
.sidebar {
background-color: #ffffff;
border-radius: 12px;
padding: 20px;
box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
}
.sidebar-title {
font-size: 1.5rem;
font-weight: bold;
color: #3366cc;
}
.sidebar-content {
font-size: 1rem;
color: #555;
}
</style>
""", unsafe_allow_html=True)
# Sidebar content
st.sidebar.title("About")
st.sidebar.write("""
This application predicts whether the comments are spam or not using a machine learning model.
It analyzes the text content of a comment and provides a detection on its spam status.
""")
# Main content
with st.container():
st.markdown('<div class="title">Spam Detection</div>', unsafe_allow_html=True)
# Input text area
text = st.text_area("Type the comment", "", height=150, key="text_input", label_visibility="visible",
help="Enter the comment you want to check for spam.")
# Predict button
if st.button("Predict Spam", key="predict_button", help="Click to predict spam status"):
if text.strip() == "":
st.warning("Please enter some text to make a detection!")
else:
# Prediction
stress_pred = stress_prediction(text)
result = "Spam" if stress_pred[0] == "Spam" else "Not Spam"
st.markdown(f'<div class="result">Detection: {result}</div>', unsafe_allow_html=True)
# Confidence level
confidence = np.random.uniform(0.75, 0.95)
st.markdown(f'<div class="confidence">Confidence: {confidence:.2f}</div>', unsafe_allow_html=True)
# Explanation
st.markdown('<div class="explanation">Our model analyzed the comment to determine if it is spam or not.</div>', unsafe_allow_html=True)
if __name__ == "__main__":
main()
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