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import streamlit as st
import pandas as pd
from groq import Groq
import os
# Initialize Groq client
client = Groq(api_key=os.getenv("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"))
# 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 user data
def provide_observed_advice(data):
# [Your existing logic]
pass
# Function to fetch health articles from Groq's API
def get_health_articles(query):
response = client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Provide a list of recent health articles about {query} with titles and URLs."}
],
model="llama-3.3-70b-versatile",
)
articles = response.choices[0].message.content
return articles
# 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 user inputs
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 = "mental health anxiety depression isolation stress relief"
articles = get_health_articles(query)
st.write(articles)
if __name__ == "__main__":
main()
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