app.py

import os
import streamlit as st
import pandas as pd
import requests

# Fetch the News API key from the environment variable
news_api_key = "fe1e6bcbbf384b3e9220a7a1138805e0"  # Replace with your News API key

# Check if the API key is available
if not news_api_key:
    st.error("NEWS_API_KEY is not set. Please provide a valid API key.")
    st.stop()

# 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 = []
    if data['depression'] > 7 and data['anxiety'] > 7:
        advice.append(
            "You are experiencing high levels of both depression and anxiety. Seek professional support and try calming activities like mindfulness or yoga."
        )
    if data['depression'] > 5 or data['anxiety'] > 5:
        advice.append(
            "Moderate levels of depression or anxiety detected. Regular exercise, proper sleep, and connecting with loved ones may help."
        )
    if data['isolation'] > 7 and data['stress_relief_activities'] < 5:
        advice.append(
            "You may feel isolated and have low engagement in stress-relief activities. Connecting with community groups or engaging in a hobby might help."
        )
    if data['future_insecurity'] > 7:
        advice.append(
            "High insecurity about the future detected. Consider breaking down your goals into smaller, manageable tasks and seeking guidance from a mentor."
        )
    if data['stress_relief_activities'] < 5:
        advice.append(
            "Low stress-relief activity levels. Try physical activities or relaxation techniques like deep breathing or journaling."
        )
    return advice

# Function to fetch health articles from News API based on the query
def get_health_articles(query):
    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={news_api_key}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        articles = [{"title": item["title"], "url": item["url"]} for item in data.get("articles", [])]
        return articles
    except requests.exceptions.RequestException as err:
        st.error(f"Error fetching articles: {err}. Please check your internet connection.")
        return []

# Streamlit app layout
def main():
    st.set_page_config(page_title="Student Health Advisory Assistant", layout="wide")

    # Sidebar Navigation
    with st.sidebar:
        st.header("Navigation")
        option = st.radio("Go to", ["Home", "Analyze Your Well-being", "Health Articles"])

    # Home Page
    if option == "Home":
        st.title("🌟 Welcome to the Student Health Advisory Assistant 🌟")
        st.image("https://via.placeholder.com/800x300?text=Student+Health+Advisory", use_column_width=True)
        st.markdown(
            "### Helping you analyze your well-being and provide personalized advice for a healthier mind."
        )
        st.markdown(
            """
            **Features:**
            - Analyze your mental well-being through uploaded datasets.
            - Get personalized advice based on your inputs.
            - Explore the latest health-related articles for guidance.
            """
        )

    # Well-being Analysis
    elif option == "Analyze Your Well-being":
        st.title("🔍 Analyze Your Well-being")

        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())

            st.markdown("### Select a Row for Analysis")
            selected_row = st.selectbox(
                "Select a row (based on index) to analyze:",
                options=df.index,
                format_func=lambda x: f"Row {x} - Age: {df.loc[x, 'age']}, Gender: {df.loc[x, 'gender']}",
            )

            # Extract data for the selected row
            row_data = df.loc[selected_row].to_dict()

            # Show extracted details
            st.write("### Selected User Details:")
            st.json(row_data)

            # Generate advice
            st.subheader("🔔 Health Advice Based on Observations")
            advice = provide_observed_advice(row_data)
            if advice:
                for i, tip in enumerate(advice, 1):
                    st.write(f"📌 **{i}.** {tip}")
            else:
                st.warning("No specific advice available based on this user's data.")

    # Health Articles
    elif option == "Health Articles":
        st.title("📰 Browse Health Articles")
        st.markdown("Get the latest updates and tips on mental health and well-being.")
        query = st.text_input("Search for health topics (e.g., anxiety, stress relief)", value="mental health")
        if st.button("Search Articles"):
            articles = get_health_articles(query)
            if articles:
                st.write("### Found Articles:")
                for article in articles:
                    st.markdown(f"🌐 [{article['title']}]({article['url']})")
            else:
                st.warning("No articles found for the given topic. Try a different query.")

if __name__ == "__main__":
    main()