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| from app.tapas import execute_query | |
| import gradio as gr | |
| def main(): | |
| description = "Table query demo app, it runs TAPAS model. You can ask a question about tabular data, TAPAS model " \ | |
| "will produce the result. Think about it as SQL query running against DB table. The advantage of " \ | |
| "TAPAS model - there is no need to upload data to DB or process it in a spreadsheet, data can be " \ | |
| "processed in memory by ML model. Pre-trained TAPAS model runs on max 64 rows and 32 columns data. " \ | |
| "Make sure CSV file data doesn't exceed these dimensions." | |
| article = "<p style='text-align: center'><a href='https://katanaml.io' target='_blank'>Katana ML</a> | <a href='https://github.com/katanaml/table-query-model' target='_blank'>Github repo</a> | <a href='https://huggingface.co/google/tapas-base-finetuned-wtq' target='_blank'>TAPAS Model</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=abaranovskij_tablequery' alt='visitor badge'></center>" | |
| iface = gr.Interface(fn=execute_query, | |
| inputs=[gr.Textbox(label="Search query"), | |
| gr.File(label="CSV file")], | |
| outputs=[gr.JSON(label="Result"), | |
| gr.Dataframe(label="All data")], | |
| examples=[ | |
| ["What are the items with total higher than 8?", "taxables.csv"], | |
| ["What is the cost for Maxwell item?", "taxables.csv"], | |
| ["Show items with cost lower than 2 and tax higher than 0.05", "taxables.csv"] | |
| ], | |
| title="Table Question Answering (TAPAS)", | |
| description=description, | |
| article=article, | |
| allow_flagging='never') | |
| # Use this config when running on Docker | |
| # iface.launch(server_name="0.0.0.0", server_port=7000) | |
| iface.launch(enable_queue=True) | |
| if __name__ == "__main__": | |
| main() | |