Update app/tapas.py

app/tapas.py

@@ -0,0 +1,99 @@
+from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
+import pandas as pd
+import re
+
+p = re.compile('\d+(\.\d+)?')
+
+def load_model_and_tokenizer():
+  """
+  Load
+  """
+  tokenizer = AutoTokenizer.from_pretrained("Meena/table-question-answering-tapas")
+  model = AutoModelForTableQuestionAnswering.from_pretrained("Meena/table-question-answering-tapas")
+
+  # Return tokenizer and model
+  return tokenizer, model
+
+
+def prepare_inputs(table, queries, tokenizer):
+  """
+  Convert dictionary into data frame and tokenize inputs given queries.
+  """
+  table = table.astype('str').head(100)
+  inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
+  return table, inputs
+
+
+def generate_predictions(inputs, model, tokenizer):
+  """
+  Generate predictions for some tokenized input.
+  """
+  # Generate model results
+  outputs = model(**inputs)
+
+  # Convert logit outputs into predictions for table cells and aggregation operators
+  predicted_table_cell_coords, predicted_aggregation_operators = tokenizer.convert_logits_to_predictions(
+        inputs,
+        outputs.logits.detach(),
+        outputs.logits_aggregation.detach()
+  )
+
+  # Return values
+  return predicted_table_cell_coords, predicted_aggregation_operators
+
+def postprocess_predictions(predicted_aggregation_operators, predicted_table_cell_coords, table):
+  """
+  Compute the predicted operation and nicely structure the answers.
+  """
+  # Process predicted aggregation operators
+  aggregation_operators = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
+  aggregation_predictions_string = [aggregation_operators[x] for x in predicted_aggregation_operators]
+  # Process predicted table cell coordinates
+  answers = []
+  for agg, coordinates in zip(predicted_aggregation_operators, predicted_table_cell_coords):
+      if len(coordinates) == 1:
+        # 1 cell
+        answers.append(table.iat[coordinates[0]])
+      else:
+        # > 1 cell
+        cell_values = []
+        for coordinate in coordinates:
+          cell_values.append(table.iat[coordinate])
+        answers.append(", ".join(cell_values))
+         
+  # Return values
+  return aggregation_predictions_string, answers
+
+
+def show_answers(queries, answers, aggregation_predictions_string):
+  """
+    Visualize the postprocessed answers.
+  """
+  agg = {"NONE": lambda x: x, "SUM" : lambda x: sum(x), "AVERAGE": lambda x: (sum(x) / len(x)), "COUNT": lambda x: len(x)}
+  results = []
+  for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
+      print(query)
+      if predicted_agg == "NONE":
+          print("Predicted answer: " + answer)
+      else:
+          if all([not p.match(val) == None for val in answer.split(', ')]):
+              # print("Predicted answer: " + predicted_agg + "(" + answer + ") = " + str(agg[predicted_agg](list(map(float, answer.split(','))))))
+              result = str(agg[predicted_agg](list(map(float, answer.split(',')))))
+          elif predicted_agg == "COUNT":
+              # print("Predicted answer: " + predicted_agg + "(" + answer + ") = " + str(agg[predicted_agg](answer.split(','))))
+              result = str(agg[predicted_agg](answer.split(',')))
+          else:
+              result = predicted_agg + " > " + answer
+          results.append(result)
+  return results
+
+def execute_query(query, table):
+  """
+	  Invoke the TAPAS model.
+	"""
+  queries = [query]
+  tokenizer, model = load_model_and_tokenizer()
+  table, inputs = prepare_inputs(table, queries, tokenizer)
+  predicted_table_cell_coords, predicted_aggregation_operators = generate_predictions(inputs, model, tokenizer)
+  aggregation_predictions_string, answers = postprocess_predictions(predicted_aggregation_operators, predicted_table_cell_coords, table)
+  return show_answers(queries, answers, aggregation_predictions_string)