feat(tableQA): add huggingface table-QA

multi-agents-analysis/table-QA-huggingface.py

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+from transformers import pipeline
+import pandas as pd
+
+# prepare table + question
+data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio",
+                   "George Clooney"], "Number of movies": ["87", "53", "69"]}
+# table = pd.DataFrame.from_dict(data)
+query = "how many movies does Leonardo Di Caprio have?"
+# data = {
+#     "year": [1896, 1900, 1904, 2004, 2008, 2012],
+#     "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
+# }
+tablle = pd.DataFrame.from_dict(data)
+# query = "when london had the olympics game?"
+
+
+# pipeline model
+# Note: you must to install torch-scatter first.
+tqa = pipeline(task="table-question-answering",
+               model="google/tapas-large-finetuned-wtq")
+
+# result
+result = tqa(table=tablle, query=query)
+print(f"Result: {result}")
+
+print(f"\nResponse: {result['cells'][0]}")
+# 53