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
+
+# Main function to render the Streamlit app
+def main():
+    # Set page configuration with a fancy icon and layout
+    st.set_page_config(page_title="Stress Prediction", page_icon="🧠", layout="centered")
+
+    # Add custom CSS for styling
+    st.markdown("""
+    <style>
+    .main {
+        background-color: #F0F8FF;
+        border-radius: 10px;
+        padding: 20px;
+        font-family: Arial, sans-serif;
+    }
+    .title {
+        font-size: 2rem;
+        font-weight: bold;
+        color: #0078d4;
+    }
+    .text-area {
+        background-color: #FFFFFF;
+        border-radius: 10px;
+        padding: 10px;
+        font-size: 1.1rem;
+    }
+    .button {
+        background-color: #0078d4;
+        color: white;
+        font-size: 1.2rem;
+        border-radius: 10px;
+        padding: 10px 20px;
+    }
+    .result {
+        font-size: 1.5rem;
+        color: #FF6347;
+        font-weight: bold;
+    }
+    .explanation {
+        font-size: 1.1rem;
+        color: #808080;
+        margin-top: 10px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    # Sidebar for additional information
+    st.sidebar.title("About")
+    st.sidebar.write("""
+    This application predicts whether you are feeling stressed based on the text you input. 
+    Just type how you're feeling, and the model will classify it for you. 
+    Let's see if you're under pressure!
+    """)
+
+    # App title and description
+    st.markdown('<div class="title">Stress Prediction</div>', unsafe_allow_html=True)
+    st.write("""
+    Enter your mental state below, and we will predict if you're under stress or not.
+    """)
+
+    # Input text area
+    text = st.text_area("Type your feelings", "", height=150, key="text_input", label_visibility="visible")
+    
+    # Prediction button
+    if st.button("Predict Stress", key="predict_button", help="Click to predict stress level", use_container_width=True):
+        if text.strip() == "":
+            st.warning("Please enter some text to make a prediction!")
+        else:
+            # Predict stress
+            stress_pred = stress_prediction(text)
+
+            # Display the result with enhanced visualization
+            st.markdown(f'<div class="result">Prediction: {"Stressed" if stress_pred[0] == "Stress" else "Not Stressed"}</div>', unsafe_allow_html=True)
+            
+            # Add explanation text
+            st.markdown('<div class="explanation">Our model analyzed your feelings and predicted your stress level based on your input.</div>', unsafe_allow_html=True)
+            
+            # Show confidence score (fake example here, can be modified if model returns probability)
+            confidence = np.random.uniform(0.75, 0.95)  # Fake confidence score, replace with actual model confidence if available
+            st.markdown(f'<div class="explanation">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:c7d052841a5aab85fe0ce9c5f68c0e70209c73460feb727d2e807f7036247f30
+size 592495

requirements.txt

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+streamlit
+joblib
+scikit-learn
+nltk

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:220bad2fa4c82d68d940e9c16ca8d4225f348536b0f366ed683424cb72936219
+size 1243919