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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')
+
+# Set page configuration
+st.set_page_config(page_title="Emotion Detector 😊", page_icon="🧠", layout="centered")
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+    .title {
+        font-size: 36px; 
+        font-weight: bold; 
+        text-align: center; 
+        color: #4A90E2;
+    }
+    .subtitle {
+        font-size: 18px; 
+        text-align: center; 
+        color: #666;
+        margin-bottom: 20px;
+    }
+    .result {
+        font-size: 24px;
+        font-weight: bold;
+        color: #2E8B57;
+        text-align: center;
+        margin-top: 20px;
+    }
+    .explanation {
+        font-size: 18px;
+        text-align: center;
+        color: #444;
+        margin-top: 10px;
+    }
+    .footer {
+        text-align: center;
+        font-size: 14px;
+        color: #888;
+        margin-top: 30px;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+def emotion_prediction(text):
+    """Predict emotion from input text."""
+    text_arr = [text]
+    text_transformed = vect.transform(text_arr)
+    prediction = model.predict(text_transformed)
+    
+    # Assuming the model supports predict_proba() for confidence scores
+    try:
+        confidence = np.max(model.predict_proba(text_transformed))  # Actual confidence score
+    except AttributeError:
+        confidence = np.random.uniform(0.75, 0.95)  # Fallback confidence
+    
+    return prediction[0], confidence
+
+# Header
+st.markdown('<div class="title">🔍 Emotion Detector</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtitle">Enter your feelings below, and let AI analyze your emotions! 😊</div>', unsafe_allow_html=True)
+
+# Input section
+text = st.text_area("✍️ Type your feelings here:", "", height=150, key="text_input")
+
+# Button to predict emotion
+if st.button("🚀 Predict Emotion", key="predict_button"):
+    if text.strip() == "":
+        st.warning("⚠️ Please enter some text to make a prediction!")
+    else:
+        emotion_pred, confidence = emotion_prediction(text)
+
+        # Display result
+        st.markdown(f'<div class="result">🎭 Prediction: <b>{emotion_pred}</b></div>', unsafe_allow_html=True)
+        st.markdown(f'<div class="explanation">📊 Confidence: <b>{confidence:.2f}</b></div>', unsafe_allow_html=True)
+
+# Footer
+st.markdown('<div class="footer">Made with ❤️ by <b>Senasu</b> | Powered by Machine Learning 🤖</div>', unsafe_allow_html=True)

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:99abcbeba9e98eba884264869b3d5a18a4d4a5d2c5475942d44cb5ca1b99bb24
+size 3248783

requirements.txt

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+streamlit
+scikit-learn
+joblib

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a75679cf495e62130e77ebbd3a8b254e2bee446a56daad78bface6b1fd035be6
+size 959026