import streamlit as st
import pandas as pd
import requests

# Function to analyze stress level based on various factors
def analyze_stress_level(df, anxiety_level, self_esteem, academic_performance, study_load, depression):
    filtered_df = df[
        (df['anxiety_level'] == anxiety_level) &
        (df['self_esteem'] == self_esteem) &
        (df['academic_performance'] == academic_performance) &
        (df['study_load'] == study_load) &
        (df['depression'] == depression)
    ]
    if not filtered_df.empty:
        return filtered_df.iloc[0]['stress_level']
    return "No matching data found."

# Function to fetch related health articles from GROC API
def get_health_documents_from_groc(query):
    api_key = "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"  # Replace with your actual GROC API key
    url = "https://api.groc.com/v1/search"
    params = {
        "query": query,
        "api_key": api_key,
        "type": "article"
    }
    response = requests.get(url, params=params)
    if response.status_code == 200:
        return response.json().get("results", [])
    else:
        st.error(f"Error {response.status_code}: {response.text}")
        return []

# Main Streamlit app
def main():
    st.title("Student Stress Analysis and Health Advisory")

    # Upload dataset
    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type="csv")
    if uploaded_file is not None:
        df = pd.read_csv(uploaded_file)
        st.write("Dataset Preview:")
        st.dataframe(df.head())

        # Input fields
        anxiety_level = st.selectbox("Anxiety Level", df['anxiety_level'].unique())
        self_esteem = st.selectbox("Self Esteem", df['self_esteem'].unique())
        academic_performance = st.selectbox("Academic Performance", df['academic_performance'].unique())
        study_load = st.selectbox("Study Load", df['study_load'].unique())
        depression = st.selectbox("Depression", df['depression'].unique())

        # Analyze stress level
        if st.button("Analyze Stress Level"):
            stress_level = analyze_stress_level(df, anxiety_level, self_esteem, academic_performance, study_load, depression)
            st.write(f"Stress Level: {stress_level}")

            # Fetch related health articles
            query = f"Stress management articles for stress level: {stress_level}"
            articles = get_health_documents_from_groc(query)
            st.write("Related Health Articles:")
            for article in articles:
                st.markdown(f"- [{article['title']}]({article['url']})")

if __name__ == "__main__":
    main()