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import streamlit as st
import pandas as pd
import base64
# Define function to read CSV file
def read_data():
data = pd.read_csv("health_conditions.csv")
return data
# Define function to update CSV file
def update_data(data):
data.to_csv("health_conditions.csv", index=False)
# Define function to display data
def display_data(data):
st.write(data)
# Define function to add new data
def add_data(data, new_data):
data = pd.concat([data, new_data], ignore_index=True)
return data
# Define function to delete data
def delete_data(data, indices):
data = data.drop(indices, axis=0)
data = data.reset_index(drop=True)
return data
# Define function to download data as CSV file
def download_data(data):
csv = data.to_csv(index=False)
href = f'<a href="data:file/csv;base64,{base64.b64encode(csv.encode()).decode()}" download="health_conditions.csv">Download CSV file</a>'
return href
def create_phq9_questions():
questions = [
{"question": "Little interest or pleasure in doing things", "icd10": "F32.1"},
{"question": "Feeling down, depressed, or hopeless", "icd10": "F32.1"},
{"question": "Trouble falling or staying asleep, or sleeping too much", "icd10": "F32.1"},
{"question": "Feeling tired or having little energy", "icd10": "F32.1"},
{"question": "Poor appetite or overeating", "icd10": "F32.1"},
{"question": "Feeling bad about yourself — or that you are a failure or have let yourself or your family down", "icd10": "F32.1"},
{"question": "Trouble concentrating on things, such as reading the newspaper or watching television", "icd10": "F32.1"},
{"question": "Moving or speaking so slowly that other people could have noticed? Or the opposite — being so fidgety or restless that you have been moving around a lot more than usual", "icd10": "F32.1"},
{"question": "Thoughts that you would be better off dead or of hurting yourself in some way", "icd10": "F32.1"}
]
return questions
# Define main function
def main():
st.title("Health Condition Costs Visualization")
data = read_data()
display_data(data)
st.write("To add new data, please fill in the form below:")
condition = st.text_input("Condition")
icd10_code = st.text_input("ICD10 Code")
snomed_code = st.text_input("SNOMED Code")
loinc_code = st.text_input("LOINC Code")
assessment = st.text_input("Assessment")
if st.button("Add"):
new_data = pd.DataFrame({
"condition": [condition],
"ICD10_code": [icd10_code],
"SNOMED_code": [snomed_code],
"LOINC_code": [loinc_code],
"assessment": [assessment]
})
data = add_data(data, new_data)
update_data(data)
indices = []
for i, row in data.iterrows():
if st.checkbox(f"Delete row {i+1}"):
indices.append(i)
if len(indices) > 0:
data = delete_data(data, indices)
update_data(data)
st.markdown(download_data(data), unsafe_allow_html=True)
# Get the PHQ9 questions
questions = create_phq9_questions()
# Display each question and an input text field for the answer
for question in questions:
st.markdown(question["question"])
answer = st.text_input("Your Answer:")
if __name__ == "__main__":
main()
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