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# !pip install streamlit
# !pip install pandas
import pandas as pd
import streamlit as st
import base64
import io
import base64
# Functions
def map_data_to_template(mapping_df, template_df, data_df):
# Initialize the final output dataframe with the template columns, filled with NaN
final_output_df = pd.DataFrame(columns=template_df.columns)
# Prepare a dictionary to hold the mapping from MEDLab to NDA variables
variable_mapping = mapping_df.set_index('MEDLab Variable')['NDA Variable'].to_dict()
# Iterate over each NDA variable to map the data
for nda_var in final_output_df.columns:
medlab_vars = [medlab_var for medlab_var, nda_mapped_var in variable_mapping.items() if nda_mapped_var == nda_var]
# Initialize the column with None
final_output_df[nda_var] = [None] * len(data_df)
# Go through each potential MEDLab variable until we find one that's present and has data
for medlab_var in medlab_vars:
if medlab_var in data_df.columns and not data_df[medlab_var].isnull().all():
# If a date column, convert to the specified format
if 'date' in medlab_var:
final_output_df[nda_var] = pd.to_datetime(data_df[medlab_var], errors='coerce').dt.strftime('%m/%d/%Y')
else:
final_output_df[nda_var] = data_df[medlab_var]
break # Stop checking once we've mapped one
return final_output_df
# Streamlit app
def main():
st.markdown("<h1 style='text-align: center; color: #E694FF;'>Data Transformer</h1>", unsafe_allow_html=True)
# File Uploader for each CSV
st.subheader("Upload Files")
nimh_template_file = st.file_uploader("Choose NIMH Template CSV", type=['csv'])
redcap_data_file = st.file_uploader("Choose REDCap Data CSV", type=['csv'])
conversion_key_file = st.file_uploader("Choose Conversion Key CSV", type=['csv'])
if nimh_template_file and redcap_data_file and conversion_key_file:
# Convert the file objects to DataFrames
nimh_template_df = pd.read_csv(io.StringIO(nimh_template_file.getvalue().decode('utf-8')), skiprows=1)
redcap_data_df = pd.read_csv(io.StringIO(redcap_data_file.getvalue().decode('utf-8')))
conversion_key_df = pd.read_csv(io.StringIO(conversion_key_file.getvalue().decode('utf-8')))
transformed_data_df = map_data_to_template(
conversion_key_df,
nimh_template_df,
redcap_data_df
)
# Display transformed data
st.subheader("Transformed Data")
st.write(transformed_data_df)
# Download button for transformed data
st.subheader("Download Transformed Data")
csv = transformed_data_df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here
href = f'<a href="data:file/csv;base64,{b64}" download="transformed_data.csv">Download CSV File</a>'
st.markdown(href, unsafe_allow_html=True)
if __name__ == '__main__':
main()
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