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| import gradio as gr | |
| from src.core import get_table_mapping, transform_source, process_csv_text, generate_mapping_code | |
| MAX_ROWS = 10 | |
| def generate_step_markdown(step_number: int, subtitle: str, description: str = None): | |
| return gr.Markdown(f"# Step {step_number}\n\n ### {subtitle}\n{description}") | |
| # TODO: use tempfile | |
| def export_csv(df, filename): | |
| df.to_csv(filename, index=False) | |
| return gr.File.update(value=filename, visible=True) | |
| # TODO: use tempfile | |
| def export_text(val, filename): | |
| with open(filename, "w") as f: | |
| f.write(val) | |
| return gr.File.update(value=filename, visible=True) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# LLM Data Mapper\nThis is a LacThis is a demo of the LangChain platform. It is a tool for generating python code from natural language prompts. This demo is a simple ETL pipeline, where you upload a source CSV and a template CSV, and then generate python code to transform the source CSV into the template CSV. This is a simple example, but the platform can be used for much more complex tasks, such as generating python code from a natural language specification document.") | |
| # STEP 1 | |
| generate_step_markdown(1, "Upload a Template CSV and a Source CSV.", "The schema will be extracted from the template file and the source file will be transformed to match the schema.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| upload_template_btn = gr.UploadButton(label="Upload Template File", file_types = ['.csv'], live=True, file_count = "single") | |
| template_df = gr.Dataframe(max_rows=MAX_ROWS, interactive=False) | |
| upload_template_btn.upload(fn=process_csv_text, inputs=upload_template_btn, outputs=template_df) | |
| with gr.Column(): | |
| upload_source_button = gr.UploadButton(label="Upload Source File", file_types = ['.csv'], live=True, file_count = "single") | |
| source_df = gr.Dataframe(max_rows=MAX_ROWS, interactive=False) | |
| upload_source_button.upload(fn=process_csv_text, inputs=upload_source_button, outputs=source_df) | |
| # STEP 2 | |
| generate_step_markdown(2, "Generate mapping from Source to Template.", "Once generated, you can edit the values directly in the table below and they will be incorporated into the mapping logic.") | |
| with gr.Row(): | |
| generate_mapping_btn = gr.Button(value="Generate Mapping", variant="primary") | |
| with gr.Row(): | |
| table_mapping_df = gr.DataFrame(max_rows=MAX_ROWS, interactive=True) | |
| generate_mapping_btn.click(fn=get_table_mapping, inputs=[source_df, template_df], outputs=[table_mapping_df]) | |
| with gr.Row(): | |
| save_mapping_btn = gr.Button(value="Save Mapping", variant="secondary") | |
| with gr.Row(): | |
| csv = gr.File(interactive=False, visible=False) | |
| save_mapping_btn.click(lambda df: export_csv(df, "source_template_mapping.csv"), table_mapping_df, csv) | |
| mapping_file = gr.File(label="Downloaded File", visible=False) | |
| mapping_file.change(lambda x: x, mapping_file, table_mapping_df) | |
| # STEP 3 | |
| generate_step_markdown(3, "Generate python code to transform Source to Template, using the generated mapping.", "Once generated, you can edit the code directly in the code block below and it will be incorporated into the transformation logic. And this is re-runnable! Update the mapping logic above to try it out.") | |
| with gr.Row(): | |
| generate_code_btn = gr.Button(value="Generate Code from Mapping", variant="primary") | |
| with gr.Row(): | |
| code_block = gr.Code(language="python") | |
| generate_code_btn.click(fn=generate_mapping_code, inputs=[table_mapping_df], outputs=[code_block]) | |
| with gr.Row(): | |
| save_code_btn = gr.Button(value="Save Code", variant="secondary") | |
| with gr.Row(): | |
| text = gr.File(interactive=False, visible=False) | |
| save_code_btn.click(lambda txt: export_text(txt, "transformation_code.py"), code_block, text) | |
| code_file = gr.File(label="Downloaded File", visible=False) | |
| code_file.change(lambda x: x, code_file, code_block) | |
| # STEP 4 | |
| generate_step_markdown(4, "Transform the Source CSV into the Template CSV using the generated code.", "And this is re-runnable! Update the logic above to try it out.") | |
| with gr.Row(): | |
| transform_btn = gr.Button(value="Transform Source", variant="primary") | |
| with gr.Row(): | |
| source_df_transformed = gr.Dataframe(label="Source (transformed)", max_rows=MAX_ROWS) | |
| transform_btn.click(transform_source, inputs=[source_df, code_block], outputs=[source_df_transformed]) | |
| with gr.Row(): | |
| save_transformed_source_btn = gr.Button(value="Save Transformed Source", variant="secondary") | |
| with gr.Row(): | |
| csv = gr.File(interactive=False, visible=False) | |
| save_transformed_source_btn.click(lambda df: export_csv(df, "transformed_source.csv"), source_df_transformed, csv) | |
| transform_file = gr.File(label="Downloaded File", visible=False) | |
| transform_file.change(lambda x: x, transform_file, source_df_transformed) | |
| demo.launch() | |