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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| # Function to get health advice based on inputs | |
| def get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp): | |
| filtered_df = df[ | |
| (df['Age'].between(age - 2, age + 2)) & # Allow ±2 years | |
| (df['Heart_Rate'].between(heart_rate - 5, heart_rate + 5)) & # Allow ±5 bpm | |
| (df['Blood_Pressure_Systolic'].between(systolic_bp - 10, systolic_bp + 10)) & # Allow ±10 | |
| (df['Blood_Pressure_Diastolic'].between(diastolic_bp - 10, diastolic_bp + 10)) # Allow ±10 | |
| ] | |
| if not filtered_df.empty: | |
| return filtered_df.iloc[0]['Health_Risk_Level'] | |
| return "No matching health data found." | |
| # Function to get health articles from GROC API | |
| def get_health_documents_from_groc(query): | |
| api_key = "YOUR_GROC_API_KEY" # Replace with your actual GROC API key | |
| url = f"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: | |
| data = response.json() | |
| return data.get("results", []) | |
| else: | |
| st.error(f"Error {response.status_code}: {response.text}") | |
| return [{"title": f"Error: {response.status_code}", "url": ""}] | |
| # Main Streamlit app | |
| def main(): | |
| st.title("Health Risk Level and Advisory Assistant") | |
| # File upload | |
| 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()) | |
| # User input | |
| age = st.number_input("Enter Age", min_value=1, max_value=100, step=1) | |
| heart_rate = st.number_input("Enter Heart Rate (bpm)", min_value=30, max_value=200, step=1) | |
| systolic_bp = st.number_input("Enter Systolic Blood Pressure", min_value=80, max_value=200, step=1) | |
| diastolic_bp = st.number_input("Enter Diastolic Blood Pressure", min_value=40, max_value=120, step=1) | |
| # Predict health risk level | |
| if st.button("Get Health Risk Level"): | |
| risk_level = get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp) | |
| st.write(f"Health Risk Level: {risk_level}") | |
| # Retrieve related health articles | |
| query = f"Health risk for age {age}, heart rate {heart_rate}, BP {systolic_bp}/{diastolic_bp}" | |
| st.write("Related Health Articles:") | |
| articles = get_health_documents_from_groc(query) | |
| for article in articles: | |
| st.markdown(f"- [{article['title']}]({article['url']})") | |
| if __name__ == "__main__": | |
| main() | |