Update app/tapas.py

app/tapas.py

@@ -1,48 +1,48 @@
 from transformers import TapasTokenizer, TFTapasForQuestionAnswering
 import pandas as pd
 
-def execute_query(query, csv_file):
-
-    table = pd.read_csv(csv_file.name, delimiter=",")
-    table.fillna(0, inplace=True)
-    table = table.astype(str)
-
-    model_name = "google/tapas-base-finetuned-wtq"
-    model = TFTapasForQuestionAnswering.from_pretrained(model_name)
-    tokenizer = TapasTokenizer.from_pretrained(model_name)
-
-    queries = [query]
-
-    inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
-    outputs = model(**inputs)
-
-    predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
-        inputs, outputs.logits, outputs.logits_aggregation
-    )
-
-    # let's print out the results:
-    id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
-    aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
-
-    answers = []
-    for coordinates in predicted_answer_coordinates:
-        if len(coordinates) == 1:
-            # only a single cell:
-            answers.append(table.iat[coordinates[0]])
-        else:
-            # multiple cells
-            cell_values = []
-            for coordinate in coordinates:
-                cell_values.append(table.iat[coordinate])
-            answers.append(cell_values)
-
-    for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
-        if predicted_agg != "NONE":
-            answers.append(predicted_agg)
-
-    query_result = {
-        "query": query,
-        "result": answers
-    }
+def execute_query(query, table):
+	if table:
+		#table = pd.read_csv(csv_file.name, delimiter=",")
+		table.fillna(0, inplace=True)
+		table = table.astype(str)
+
+		model_name = "google/tapas-base-finetuned-wtq"
+		model = TFTapasForQuestionAnswering.from_pretrained(model_name)
+		tokenizer = TapasTokenizer.from_pretrained(model_name)
+
+		queries = [query]
+
+		inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf")
+		outputs = model(**inputs)
+
+		predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
+			inputs, outputs.logits, outputs.logits_aggregation
+		)
+
+		# let's print out the results:
+		id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
+		aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
+
+		answers = []
+		for coordinates in predicted_answer_coordinates:
+			if len(coordinates) == 1:
+				# only a single cell:
+				answers.append(table.iat[coordinates[0]])
+			else:
+				# multiple cells
+				cell_values = []
+				for coordinate in coordinates:
+					cell_values.append(table.iat[coordinate])
+				answers.append(cell_values)
+
+		for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
+			if predicted_agg != "NONE":
+				answers.append(predicted_agg)
+
+		query_result = {
+			"query": query,
+			"result": answers
+		}
 
     return query_result, table