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app.py

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+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()

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:433d439d1fe5efbb94c49916f37063858f0854eb4ee479a50c8e5a8b63abdb00
+size 11155055

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:d67c2972f23bf5c95bbe55f0b971e177912676837f88a7365a773047c23cb55e
+size 27511312