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Update app.py
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app.py
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import streamlit as st
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import pandas as pd
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import
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#
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# Function to load and preprocess data
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@st.cache_data
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# Function to provide detailed health advice based on user data
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def provide_observed_advice(data):
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if data['depression'] > 7 and data['anxiety'] > 7:
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advice.append("You seem to be experiencing high levels of both depression and anxiety. It's important to consider professional mental health support. You might also benefit from engaging in calming activities like deep breathing, mindfulness, or yoga.")
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# Moderate depression or anxiety
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elif data['depression'] > 5 or data['anxiety'] > 5:
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advice.append("You are showing moderate levels of depression or anxiety. It would be helpful to develop healthy coping strategies like maintaining a regular sleep schedule, engaging in physical activity, and reaching out to friends or family for support.")
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# High isolation and low stress-relief activities
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if data['isolation'] > 7 and data['stress_relief_activities'] < 5:
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advice.append("It seems you are feeling isolated, and your engagement in stress-relief activities is low. It's important to connect with friends or join community groups. Incorporate activities that help alleviate stress, such as walking, journaling, or meditation.")
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# High future insecurity
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if data['future_insecurity'] > 7:
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advice.append("You are feeling a significant amount of insecurity about the future. It can be helpful to break down your larger goals into smaller, manageable tasks. Seeking career counseling or mentorship could provide valuable guidance and reduce anxiety about the future.")
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# Overall low engagement in stress-relief activities
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if data['stress_relief_activities'] < 5:
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advice.append("Your engagement in stress-relief activities is quite low. It's essential to engage in activities that reduce stress and promote mental wellness, such as hobbies, physical exercise, and relaxation techniques like deep breathing or yoga.")
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return advice
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# Function to fetch health articles from the GROC API based on the query
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def get_health_articles(query):
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except requests.exceptions.RequestException as e:
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st.error(f"Error fetching articles: {e}")
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return []
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# Streamlit app layout
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def main():
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st.subheader("Related Health Articles")
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query = "mental health anxiety depression isolation stress relief"
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articles = get_health_articles(query)
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for article in articles:
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st.write(f"- [{article['title']}]({article['url']})")
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else:
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st.write("No articles found. Please check your API key or internet connection.")
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if __name__ == "__main__":
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main()
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import streamlit as st
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import pandas as pd
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from groq import Groq
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import os
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# Initialize Groq client
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client = Groq(api_key=os.getenv("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"))
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# Function to load and preprocess data
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@st.cache_data
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# Function to provide detailed health advice based on user data
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def provide_observed_advice(data):
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# [Your existing logic]
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pass
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# Function to fetch health articles from Groq's API
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def get_health_articles(query):
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response = client.chat.completions.create(
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": f"Provide a list of recent health articles about {query} with titles and URLs."}
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],
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model="llama-3.3-70b-versatile",
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)
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articles = response.choices[0].message.content
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return articles
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# Streamlit app layout
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def main():
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st.subheader("Related Health Articles")
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query = "mental health anxiety depression isolation stress relief"
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articles = get_health_articles(query)
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st.write(articles)
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if __name__ == "__main__":
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main()
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