app.py

import streamlit as st
import pandas as pd

# Set the GROC API Key directly in the code
GROC_API_KEY = "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"  # Replace with your actual GROC API key

# Function to load and preprocess data
@st.cache_data
def load_data(file):
    df = pd.read_csv(file)
    return df

# Function to provide detailed health advice based on user data
def provide_observed_advice(data):
    advice = []

    # High depression and anxiety with low stress-relief activities
    if data['depression'] > 7 and data['anxiety'] > 7:
        advice.append("You seem to be experiencing high levels of both depression and anxiety. It's important to consider professional mental health support. You might also benefit from engaging in calming activities like deep breathing, mindfulness, or yoga.")

    # Moderate depression or anxiety
    elif data['depression'] > 5 or data['anxiety'] > 5:
        advice.append("You are showing moderate levels of depression or anxiety. It would be helpful to develop healthy coping strategies like maintaining a regular sleep schedule, engaging in physical activity, and reaching out to friends or family for support.")

    # High isolation and low stress-relief activities
    if data['isolation'] > 7 and data['stress_relief_activities'] < 5:
        advice.append("It seems you are feeling isolated, and your engagement in stress-relief activities is low. It's important to connect with friends or join community groups. Incorporate activities that help alleviate stress, such as walking, journaling, or meditation.")

    # High future insecurity
    if data['future_insecurity'] > 7:
        advice.append("You are feeling a significant amount of insecurity about the future. It can be helpful to break down your larger goals into smaller, manageable tasks. Seeking career counseling or mentorship could provide valuable guidance and reduce anxiety about the future.")

    # Overall low engagement in stress-relief activities
    if data['stress_relief_activities'] < 5:
        advice.append("Your engagement in stress-relief activities is quite low. It's essential to engage in activities that reduce stress and promote mental wellness, such as hobbies, physical exercise, and relaxation techniques like deep breathing or yoga.")

    return advice

# Streamlit app layout
def main():
    st.title("Student Health Advisory Assistant")
    st.subheader("Analyze your well-being and get personalized advice")

    # File upload
    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
    if uploaded_file:
        df = load_data(uploaded_file)
        st.write("Dataset preview:")
        st.dataframe(df.head())

        # User input for analysis
        st.header("Input Your Details")
        gender = st.selectbox("Gender", ["Male", "Female"])
        age = st.slider("Age", 18, 35, step=1)
        depression = st.slider("Depression Level (1-10)", 1, 10)
        anxiety = st.slider("Anxiety Level (1-10)", 1, 10)
        isolation = st.slider("Isolation Level (1-10)", 1, 10)
        future_insecurity = st.slider("Future Insecurity Level (1-10)", 1, 10)
        stress_relief_activities = st.slider("Stress Relief Activities Level (1-10)", 1, 10)

        # Data dictionary for advice
        user_data = {
            "gender": gender,
            "age": age,
            "depression": depression,
            "anxiety": anxiety,
            "isolation": isolation,
            "future_insecurity": future_insecurity,
            "stress_relief_activities": stress_relief_activities,
        }

        # Provide advice based on user inputs
        if st.button("Get Observed Advice"):
            st.subheader("Health Advice Based on Observations")
            advice = provide_observed_advice(user_data)
            for i, tip in enumerate(advice, 1):
                st.write(f"{i}. {tip}")

if __name__ == "__main__":
    main()