Standard_Intelligence_Dev / code_df_custom.py
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Update code_df_custom.py
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import pandas as pd
import traceback
def load_excel(file):
df = pd.read_excel(file)
return file, df
def run_code(file, code):
scope = {'pd': pd}
if file:
print('file ok')
df = pd.read_excel(file)
scope['df'] = df
try:
# Attempt to execute the user's code
exec(code, scope, scope)
except Exception as e:
# Catch any exceptions that occur and print the error message
error_msg = traceback.format_exc()
print(f"Error executing user code: {error_msg}")
scope['new_df'] = df # Use the original df if error occurs
return scope['new_df'], error_msg # Return the error message along with the df
print(scope.keys())
if 'new_df' not in scope:
print("new_df not defined")
scope['new_df'] = df.copy()
new_df = scope['new_df']
return new_df, None # Return None as the error message when execution is successful
else:
print("No file provided")
return pd.DataFrame(), "No file provided"
def run_code_and_update_ui(file, code):
df, error_msg = run_code(file, code) # This is your updated run_code function.
# Now, check if there's an error message and handle it appropriately.
if error_msg:
# You can update some error display component in Gradio here to show the error_msg.
# Assuming you have a gr.Textbox to display errors:
error_display.update(value=f"Error in code execution: {error_msg}")
# Ensure you still return the original DataFrame or an empty one to satisfy the expected output type.
return df
else:
# If no error, clear the error display and return the DataFrame as usual.
error_display.update(value="")
return df
def export_df(df, filename):
filename = filename.replace('.xlsx', '_coded.xlsx')
df.to_excel(filename, index=False)
return filename