Update code for supporting predicting on custom data.

semantic_parsing_in_context/README.md

@@ -22,7 +22,8 @@ If you find our code useful, please consider citing our paper:
 - [Install Requirements](#requirements)
 - [Prepare Dataset](#data)
 - [Train Model](#train)
-- [Predict Model](#predict)
+- [Evaluate Model](#evaluate)
+- [Predict on Custom Data](#predict-on-custom-data)
 - [Demo on Web](#demo)
 - [Pretrained Weights](#experiment)
 - [Fine-grained Analysis](#analysis)
@@ -160,9 +161,9 @@ allennlp train -s %model_file% %config_file% ^
 -o {"""model.serialization_dir""":"""%model_file%""","""random_seed""":"""%seed%""","""numpy_seed""":"""%seed%""","""pytorch_seed""":"""%seed%""","""dataset_reader.tables_file""":"""%tables_file%""","""dataset_reader.database_path""":"""%database_path%""","""train_data_path""":"""%train_data_path%""","""validation_data_path""":"""%validation_data_path%""","""model.text_embedder.tokens.pretrained_file""":"""%pretrained_file%""","""model.dataset_path""":"""%dataset_path%"""}
 ```
 
-## Predict
+## Evaluate
 
-You could predict SQLs using trained model checkpoint file (e.g. `checkpoints_sparc/sparc_concat_model/model.tar.gz`) using the following command:
+You could predict and evaluate SQLs using trained model checkpoint file (e.g. `checkpoints_sparc/sparc_concat_model/model.tar.gz`) using the following command:
 
 - Under Linux
 ```bash
@@ -201,6 +202,10 @@ allennlp predict ^
 %model_file%/model.tar.gz %validation_out_file
 ```
 
+## Predict On Custom Data
+
+Our code also supports function call to predict SQls on custom data. You could find it in `predict.py`. Before running it, you should firstly store your own database into paths corresponding to the arguments of `PredictManager` with the same format as SParC/CoSQL. 
+
 ## Demo
 
 You could also host a demo page using the following command using a well-trained archived model (e.g. `checkpoints_sparc/sparc_concat_model/model.tar.gz`):

semantic_parsing_in_context/predict.py

@@ -0,0 +1,34 @@
+from allennlp.models.archival import load_archive
+from allennlp.predictors.predictor import Predictor
+# WARNING: Do not exclude these imports
+from predictor.sparc_predictor import SparcPredictor
+from dataset_reader.sparc_reader import SparcDatasetReader
+from models.sparc_parser import SparcParser
+
+
+class PredictManager:
+
+    def __init__(self, archive_file, tables_file, database_path):
+        overrides = "{\"dataset_reader.tables_file\":\"" + tables_file + "\",\"dataset_reader.database_path\":" +\
+                    "\"" + database_path + "\"}"
+        archive = load_archive(archive_file,
+                               overrides=overrides)
+        self.predictor = Predictor.from_archive(
+            archive, predictor_name="sparc")
+
+    def predict_result(self, ques_inter: str, ques_database: str):
+        param = {
+            "database_id": ques_database,
+            "question": ques_inter
+        }
+        restate = self.predictor.predict_json(param)["best_predict_sql"]
+        return restate
+
+
+if __name__ == '__main__':
+    manager = PredictManager(archive_file="model.tar.gz",
+                             tables_file="dataset_sparc/tables.json",
+                             database_path="dataset_sparc/database")
+    # the input dialogue is separate by `;`, and the second argument is database_id
+    result = manager.predict_result("What are all the airlines;Of these, which is Jetblue Airways", "flight_2")
+    print(result)