Julien Simon commited on
Commit
54c2f4d
·
1 Parent(s): cc07d78
Files changed (2) hide show
  1. app.py +25 -13
  2. requirements.txt +2 -0
app.py CHANGED
@@ -22,32 +22,40 @@ pipe_tapas = pipeline(
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  )
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- def process(query, file, correct_answer, rows=20):
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  table = pd.read_csv(file.name, header=0).astype(str)
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  table = table[:rows]
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  result_tapex = pipe_tapex(table=table, query=query)
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  result_tapas = pipe_tapas(table=table, query=query)
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- return result_tapex["answer"], result_tapas["answer"], correct_answer
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  # Inputs
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  query_text = gr.Text(label="Enter a question")
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- input_file = gr.File(label="Upload a CSV file", type="file")
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  rows_slider = gr.Slider(label="Number of rows")
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  # Output
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  answer_text_tapex = gr.Text(label="TAPEX answer")
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  answer_text_tapas = gr.Text(label="TAPAS answer")
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- 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). \
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- Both have been fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset. \n\n\
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- A sample file with football statistics is available in the repository: \n\n\
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- * Which team has the most wins? Answer: Manchester City FC\n\
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- * Which team has the most wins: Chelsea, Liverpool or Everton? Answer: Liverpool\n\
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- * Which teams have scored less than 40 goals? Answer: Cardiff City FC, Fulham FC, Brighton & Hove Albion FC, Huddersfield Town FC\n\
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- * What is the average number of wins? Answer: 16 (rounded)\n\n\
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- You can also upload your own CSV file. Please note that maximum sequence length for both models is 1024 tokens, \
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- so you may need to limit the number of rows in your CSV file. Chunking is not implemented yet."
 
 
 
 
 
 
 
 
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  iface = gr.Interface(
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  theme="huggingface",
@@ -63,7 +71,11 @@ iface = gr.Interface(
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  "default_file.csv",
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  20,
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  ],
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- ["Which teams have scored less than 40 goals?", "default_file.csv", 20],
 
 
 
 
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  ["What is the average number of wins?", "default_file.csv", 20],
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  ],
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  allow_flagging="never",
 
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  )
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+ def process(query, file, rows=20):
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  table = pd.read_csv(file.name, header=0).astype(str)
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  table = table[:rows]
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  result_tapex = pipe_tapex(table=table, query=query)
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  result_tapas = pipe_tapas(table=table, query=query)
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+ return result_tapex["answer"], result_tapas["answer"]
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  # Inputs
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  query_text = gr.Text(label="Enter a question")
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+ input_file = gr.File(label="Upload a CSV file", type="filepath")
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  rows_slider = gr.Slider(label="Number of rows")
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  # Output
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  answer_text_tapex = gr.Text(label="TAPEX answer")
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  answer_text_tapas = gr.Text(label="TAPAS answer")
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+ description = (
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+ "This Space lets you ask questions on CSV documents with Microsoft "
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+ "[TAPEX-Large](https://huggingface.co/microsoft/tapex-large-finetuned-wtq) "
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+ "and Google [TAPAS-Large](https://huggingface.co/google/tapas-large-finetuned-wtq). "
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+ "Both have been fine-tuned on the "
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+ "[WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset. \n\n"
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+ "A sample file with football statistics is available in the repository: \n\n"
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+ "* Which team has the most wins? Answer: Manchester City FC\n"
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+ "* Which team has the most wins: Chelsea, Liverpool or Everton? Answer: "
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+ "Liverpool\n"
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+ "* Which teams have scored less than 40 goals? Answer: Cardiff City FC, "
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+ "Fulham FC, Brighton & Hove Albion FC, Huddersfield Town FC\n"
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+ "* What is the average number of wins? Answer: 16 (rounded)\n\n"
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+ "You can also upload your own CSV file. Please note that maximum sequence "
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+ "length for both models is 1024 tokens, so you may need to limit the number "
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+ "of rows in your CSV file. Chunking is not implemented yet."
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+ )
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  iface = gr.Interface(
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  theme="huggingface",
 
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  "default_file.csv",
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  20,
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  ],
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+ [
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+ "Which teams have scored less than 40 goals?",
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+ "default_file.csv",
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+ 20
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+ ],
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  ["What is the average number of wins?", "default_file.csv", 20],
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  ],
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  allow_flagging="never",
requirements.txt CHANGED
@@ -1,2 +1,4 @@
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  transformers
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  torch
 
 
 
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  transformers
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  torch
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+ gradio
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+ pandas