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Update app.py
Browse files
app.py
CHANGED
@@ -41,26 +41,6 @@ def provide_observed_advice(data):
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"Low engagement in extracurricular activities detected. Consider joining clubs, sports, or creative groups that interest you. Participating in such activities can enhance your social connections, improve self-esteem, and provide a healthy outlet for stress."
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if data['academic_performance'] < 5:
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advice.append(
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"Low academic performance detected. Break study tasks into smaller, achievable goals, and create a distraction-free study environment. Seek help from teachers, peers, or online resources for difficult topics. Time management strategies can also help you stay on track."
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if data['bullying'] > 7:
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advice.append(
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"Bullying is a serious issue that can affect mental health. Talk to a trusted adult, teacher, or counselor about your experience. Build a support network with friends and consider attending therapy sessions to regain confidence and develop coping mechanisms."
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if data['sleep_quality'] < 5:
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advice.append(
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"Poor sleep quality detected. Establish a consistent bedtime routine and avoid screens before sleep. Create a calm sleeping environment and consider relaxation techniques like meditation or reading before bed."
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)
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if data['basic_needs'] < 5:
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advice.append(
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"Unmet basic needs detected. Address your primary needs by seeking help from local resources or organizations. Focus on creating a stable environment and prioritize your physical and emotional well-being."
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return advice
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def fetch_mental_health_articles():
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@@ -74,12 +54,37 @@ def fetch_mental_health_articles():
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return []
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def main():
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st.
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uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
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if uploaded_file:
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df = load_data(uploaded_file)
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st.write("### Dataset Preview:")
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st.dataframe(df.head())
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@@ -102,39 +107,52 @@ def main():
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st.warning("The dataset contains missing values. Rows with missing values will be skipped.")
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df = df.dropna()
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if __name__ == "__main__":
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main()
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"Low engagement in extracurricular activities detected. Consider joining clubs, sports, or creative groups that interest you. Participating in such activities can enhance your social connections, improve self-esteem, and provide a healthy outlet for stress."
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return advice
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def fetch_mental_health_articles():
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return []
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def main():
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# Set page config for a professional look
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st.set_page_config(
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page_title="Student Well-being Advisor",
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page_icon="π",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# Sidebar
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st.sidebar.title("Navigation")
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st.sidebar.write("Use the sidebar to navigate through the app.")
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st.sidebar.markdown("### π Upload Data")
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st.sidebar.write("Start by uploading your dataset for analysis.")
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st.sidebar.markdown("### π Analysis & Advice")
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st.sidebar.write("Get detailed insights and personalized advice.")
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# Main Content
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st.title("π Student Well-being Advisor")
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st.subheader("Analyze data and provide professional mental health recommendations.")
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st.write(
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"""
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This app helps identify areas of concern in students' well-being and provides personalized advice based on their responses.
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"""
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)
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# File Upload
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st.markdown("## π Upload Your Dataset")
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uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
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if uploaded_file:
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df = load_data(uploaded_file)
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st.success("Dataset uploaded successfully!")
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st.write("### Dataset Preview:")
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st.dataframe(df.head())
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st.warning("The dataset contains missing values. Rows with missing values will be skipped.")
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df = df.dropna()
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# Tabs for better organization
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tab1, tab2, tab3 = st.tabs(["π Home", "π Analysis", "π° Resources"])
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with tab1:
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st.write("### Welcome to the Well-being Advisor!")
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st.write(
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"""
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Use the tabs to explore data, generate advice, and access mental health resources.
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"""
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)
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with tab2:
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st.markdown("### π Select a Row for Analysis")
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selected_row = st.selectbox(
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"Select a row (based on index) to analyze:",
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options=df.index,
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format_func=lambda x: f"Row {x} - Stress Level: {df.loc[x, 'stress_level']}, Anxiety: {df.loc[x, 'anxiety_level']} (Depression: {df.loc[x, 'depression']})",
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)
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# Extract data for the selected row
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row_data = df.loc[selected_row].to_dict()
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# Show extracted details
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st.write("### Selected User Details:")
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st.json(row_data)
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# Generate advice
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st.subheader("π Health Advice Based on Observations")
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advice = provide_observed_advice(row_data)
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if advice:
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for i, tip in enumerate(advice, 1):
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st.write(f"π **{i}.** {tip}")
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else:
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st.warning("No specific advice available based on this user's data.")
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with tab3:
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# Fetch and display mental health articles
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st.subheader("π° Mental Health Resources")
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articles = fetch_mental_health_articles()
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if articles:
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for article in articles:
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st.write(f"**{article['title']}**")
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st.write(f"{article['description']}")
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st.write(f"[Read more]({article['url']})")
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else:
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st.write("No articles available at the moment.")
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if __name__ == "__main__":
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main()
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