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| 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 | |
| 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() | |