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| import gradio as gr | |
| import pandas as pd | |
| from transformers import ( | |
| AutoModelForSeq2SeqLM, | |
| AutoModelForTableQuestionAnswering, | |
| AutoTokenizer, | |
| pipeline, | |
| ) | |
| model_tapex = "microsoft/tapex-large-finetuned-wtq" | |
| tokenizer_tapex = AutoTokenizer.from_pretrained(model_tapex) | |
| model_tapex = AutoModelForSeq2SeqLM.from_pretrained(model_tapex) | |
| pipe_tapex = pipeline( | |
| "table-question-answering", model=model_tapex, tokenizer=tokenizer_tapex | |
| ) | |
| model_tapas = "google/tapas-large-finetuned-wtq" | |
| tokenizer_tapas = AutoTokenizer.from_pretrained(model_tapas) | |
| model_tapas = AutoModelForTableQuestionAnswering.from_pretrained(model_tapas) | |
| pipe_tapas = pipeline( | |
| "table-question-answering", model=model_tapas, tokenizer=tokenizer_tapas | |
| ) | |
| def process(query, file, rows=20): | |
| table = pd.read_csv(file.name, header=0).astype(str) | |
| table = table[:rows] | |
| result_tapex = pipe_tapex(table=table, query=query) | |
| result_tapas = pipe_tapas(table=table, query=query) | |
| return result_tapex["answer"], result_tapas["answer"] | |
| # Inputs | |
| query_text = gr.Text(label="Enter a question") | |
| input_file = gr.File(label="Upload a CSV file", type="filepath") | |
| rows_slider = gr.Slider(label="Number of rows") | |
| # Output | |
| answer_text_tapex = gr.Text(label="TAPEX answer") | |
| answer_text_tapas = gr.Text(label="TAPAS answer") | |
| description = ( | |
| "This Space lets you ask questions on CSV documents with Microsoft " | |
| "[TAPEX-Large](https://huggingface.co./microsoft/tapex-large-finetuned-wtq) " | |
| "and Google " | |
| "[TAPAS-Large](https://huggingface.co./google/tapas-large-finetuned-wtq). " | |
| "Both have been fine-tuned on the " | |
| "[WikiTableQuestions](https://huggingface.co./datasets/wikitablequestions) " | |
| "dataset. \n\n" | |
| "A sample file with football statistics is available in the " | |
| "repository: \n\n" | |
| "* Which team has the most wins? Answer: Manchester City FC\n" | |
| "* Which team has the most wins: Chelsea, Liverpool or Everton? Answer: " | |
| "Liverpool\n" | |
| "* Which teams have scored less than 40 goals? Answer: Cardiff City FC, " | |
| "Fulham FC, Brighton & Hove Albion FC, Huddersfield Town FC\n" | |
| "* What is the average number of wins? Answer: 16 (rounded)\n\n" | |
| "You can also upload your own CSV file. Please note that maximum sequence " | |
| "length for both models is 1024 tokens, so you may need to limit the " | |
| "number of rows in your CSV file. Chunking is not implemented yet." | |
| ) | |
| iface = gr.Interface( | |
| description=description, | |
| fn=process, | |
| inputs=[query_text, input_file, rows_slider], | |
| outputs=[answer_text_tapex, answer_text_tapas], | |
| examples=[ | |
| ["Which team has the most wins?", "default_file.csv", 20], | |
| [ | |
| "Which team has the most wins: Chelsea, Liverpool or Everton?", | |
| "default_file.csv", | |
| 20, | |
| ], | |
| [ | |
| "Which teams have scored less than 40 goals?", | |
| "default_file.csv", | |
| 20 | |
| ], | |
| ["What is the average number of wins?", "default_file.csv", 20], | |
| ], | |
| allow_flagging="never", | |
| ) | |
| iface.launch() | |