app.py

import streamlit as st
import pandas as pd
import requests
import os
from google.cloud import language_v1
from google.oauth2 import service_account

# Set the API key for Google AI API (if not set in the environment variable)
api_key = "AIzaSyAlvoXLqzqcZgVjhQeCNUsQgk6_SGHQNr8"  # Ensure your credentials are set up

# Initialize Google AI Client
client = language_v1.LanguageServiceClient(credentials=service_account.Credentials.from_service_account_file("path_to_your_service_account_json"))

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

# Function to fetch and analyze text using Google AI's Natural Language API
def analyze_text_with_google_ai(text):
    document = language_v1.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT)
    response = client.analyze_sentiment(document=document)
    sentiment_score = response.document_sentiment.score
    sentiment_magnitude = response.document_sentiment.magnitude
    
    # Example: Based on sentiment, provide advice
    if sentiment_score < -0.5:
        return "You may want to focus on activities that improve your mood, such as physical exercise, talking with a counselor, or engaging in mindfulness practices."
    elif sentiment_score > 0.5:
        return "It seems you're in a positive emotional state. Keep nurturing these positive habits, such as engaging in social activities and continuing to practice stress-relief strategies."
    else:
        return "You are in a neutral emotional state. Consider exploring activities that help enhance your mood, such as engaging in hobbies or relaxation exercises."

# Function to provide health advice based on user data and Google AI analysis
def provide_google_ai_advice(data):
    advice = []

    # Example of analysis based on Google AI's sentiment analysis
    if data['depression'] > 7 or data['anxiety'] > 7:
        advice.append("It seems you're experiencing high levels of depression or anxiety. It might be helpful to talk to a professional or consider engaging in activities that can reduce stress, like mindfulness or physical exercise.")
    
    # Call Google AI for sentiment-based advice
    user_data_summary = f"User's depression: {data['depression']}, anxiety: {data['anxiety']}, isolation: {data['isolation']}, future insecurity: {data['future_insecurity']}, stress-relief activities: {data['stress_relief_activities']}"
    google_ai_advice = analyze_text_with_google_ai(user_data_summary)
    advice.append(google_ai_advice)

    return advice

# Function to fetch related health articles from GROC API (optional, for RAG-style application)
def get_health_articles(query):
    url = f"https://api.groc.com/search?q={query}"
    headers = {"Authorization": f"Bearer {api_key}"}  # Replace with actual Google API key if required

    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()
        if 'results' in data:
            articles = [{"title": item["title"], "url": item["url"]} for item in data['results']]
        else:
            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():
    # Set a background color and style
    st.markdown(
        """
        <style>
        .stApp {
            background-color: #F4F4F9;
        }
        .stButton>button {
            background-color: #6200EE;
            color: white;
            font-size: 18px;
        }
        .stSlider>div>div>span {
            color: #6200EE;
        }
        .stTextInput>div>div>input {
            background-color: #E0E0E0;
        }
        </style>
        """,
        unsafe_allow_html=True
    )

    # Title and header
    st.title("🌟 **Student Health Advisory Assistant** 🌟")
    st.markdown("### **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.markdown("### **Input Your Details**")
        gender = st.selectbox("🔹 Gender", ["Male", "Female"], help="Select your gender.")
        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", key="advice_btn"):
            st.subheader("🔔 **Health Advice Based on Observations** 🔔")
            advice = provide_google_ai_advice(user_data)
            if advice:
                for i, tip in enumerate(advice, 1):
                    st.write(f"📌 {i}. {tip}")
            else:
                st.warning("No advice available based on your inputs.")

            # Fetch related health articles based on user input
            st.subheader("📰 **Related Health Articles** 📰")
            query = "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()