upload app.py

app.py

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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()

requirements.txt

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+streamlit
+pandas