Uploaded main files

app.py

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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()

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5a4b903f3d2d4ca89a076d5cbc1b617abcebb350b3e6b08252525fc36b924f5
+size 83503

requirements.txt

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+streamlit
+joblib
+scikit-learn

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c14381948a71be92a18d99100b4ff7c9edb23f5061777f8b677cfd7179b8bfe
+size 166684