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

# Retrieve the GROC API Key from environment variable
GROC_API_KEY = os.getenv("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")

# Check if the API key is missing
if not GROC_API_KEY:
    st.error("API key is missing. Please set the GROC_API_KEY environment variable.")
else:
    # 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 model observation
    def provide_observed_advice(data):
        advice = []

        # High depression, anxiety, and low stress-relief activities may indicate a need for professional help
        if data['depression'] > 7 and data['anxiety'] > 7:
            advice.append("You seem to be experiencing high levels of both depression and anxiety. It is important to consider professional mental health support, such as therapy or counseling. Engage in calming activities like deep breathing, mindfulness, and yoga.")

        # Moderate depression and anxiety levels can indicate stress but still manageable
        elif data['depression'] > 5 or data['anxiety'] > 5:
            advice.append("You are showing moderate levels of depression and/or anxiety. Focus on developing healthy coping strategies such as maintaining a regular sleep schedule, engaging in physical activity, and reaching out to friends or family for support.")

        # High isolation combined with low engagement in stress-relief activities could suggest loneliness
        if data['isolation'] > 7 and data['stress_relief_activities'] < 5:
            advice.append("It seems that you're feeling isolated, and your engagement in stress-relief activities is low. Try connecting with friends or a community group, and incorporate activities that help alleviate stress, such as walking, meditation, or journaling.")

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

        # Overall, low levels of stress-relief activities are a concern
        if data['stress_relief_activities'] < 5:
            advice.append("Your engagement in stress-relief activities is quite low. It is crucial to engage in activities that reduce stress and promote mental wellness, such as hobbies, physical exercise, or relaxation techniques.")

        return advice

    # Function to fetch health articles from the GROC API based on the query
    def get_health_articles(query):
        url = f"https://api.groc.com/search?q={query}"
        headers = {"Authorization": f"Bearer {GROC_API_KEY}"}

        try:
            response = requests.get(url, headers=headers)
            response.raise_for_status()
            data = response.json()  # Assuming the API returns JSON
            articles = [{"title": item["title"], "url": item["url"]} for item in data.get("results", [])]
            return articles
        except requests.exceptions.RequestException as e:
            st.error(f"Error fetching articles: {e}")
            return []

    # 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 model observations
            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}")

            # Fetch related health articles based on user input
            st.subheader("Related Health Articles")
            query = f"mental health anxiety depression isolation stress relief"
            articles = get_health_articles(query)
            if articles:
                for article in articles:
                    st.write(f"- [{article['title']}]({article['url']})")
            else:
                st.write("No articles found. Please check your API key or internet connection.")

    if __name__ == "__main__":
        main()