Format and remove duplicated file close

io_utils.py

@@ -1,14 +1,18 @@
 import os
 import subprocess
-import pipe
+
 import yaml
 
+import pipe
+
 YAML_PATH = "./configs"
 
+
 class Dumper(yaml.Dumper):
     def increase_indent(self, flow=False, *args, **kwargs):
         return super().increase_indent(flow=flow, indentless=False)
 
+
 def get_yaml_path(uid):
     if not os.path.exists(YAML_PATH):
         os.makedirs(YAML_PATH)
@@ -16,6 +20,7 @@ def get_yaml_path(uid):
         os.system(f"cp {YAML_PATH}/config.yaml {YAML_PATH}/{uid}_config.yaml")
     return f"{YAML_PATH}/{uid}_config.yaml"
 
+
 # read scanners from yaml file
 # return a list of scanners
 def read_scanners(uid):
@@ -23,7 +28,6 @@ def read_scanners(uid):
     with open(get_yaml_path(uid), "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
         scanners = config.get("detectors", [])
-    f.close()
     return scanners
 
 
@@ -35,7 +39,6 @@ def write_scanners(scanners, uid):
             config["detectors"] = scanners
             # save scanners to detectors in yaml
             yaml.dump(config, f, Dumper=Dumper)
-    f.close()
 
 
 # read model_type from yaml file
@@ -44,7 +47,6 @@ def read_inference_type(uid):
     with open(get_yaml_path(uid), "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
         inference_type = config.get("inference_type", "")
-    f.close()
     return inference_type
 
 
@@ -52,13 +54,13 @@ def read_inference_type(uid):
 def write_inference_type(use_inference, uid):
     with open(get_yaml_path(uid), "r+") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
-    if use_inference:
-        config["inference_type"] = "hf_inference_api"
-    else:
-        config["inference_type"] = "hf_pipeline"
-    # save inference_type to inference_type in yaml
-    yaml.dump(config, f, Dumper=Dumper)
-    f.close()
+        if use_inference:
+            config["inference_type"] = "hf_inference_api"
+        else:
+            config["inference_type"] = "hf_pipeline"
+        # save inference_type to inference_type in yaml
+        yaml.dump(config, f, Dumper=Dumper)
+
 
 # read column mapping from yaml file
 def read_column_mapping(uid):
@@ -67,7 +69,6 @@ def read_column_mapping(uid):
         config = yaml.load(f, Loader=yaml.FullLoader)
         if config:
             column_mapping = config.get("column_mapping", dict())
-    f.close()
     return column_mapping
 
 
@@ -75,7 +76,6 @@ def read_column_mapping(uid):
 def write_column_mapping(mapping, uid):
     with open(get_yaml_path(uid), "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
-        f.close()
     if config is None:
         return
     if mapping is None and "column_mapping" in config.keys():
@@ -85,7 +85,6 @@ def write_column_mapping(mapping, uid):
     with open(get_yaml_path(uid), "w") as f:
         # save column_mapping to column_mapping in yaml
         yaml.dump(config, f, Dumper=Dumper)
-    f.close()
 
 
 # convert column mapping dataframe to json
@@ -114,6 +113,7 @@ def save_job_to_pipe(id, job, lock):
     with lock:
         pipe.jobs.append((id, job))
 
+
 def pop_job_from_pipe():
     if len(pipe.jobs) == 0:
         return
@@ -128,5 +128,3 @@ def pop_job_from_pipe():
         stdout=log_file,
         stderr=log_file,
     )
-
-

text_classification_ui_helpers.py

@@ -8,17 +8,10 @@ import datasets
 import gradio as gr
 from transformers.pipelines import TextClassificationPipeline
 
-from io_utils import (
-    read_column_mapping,
-    save_job_to_pipe,
-    write_column_mapping,
-    write_log_to_user_file,
-)
-from text_classification import (
-    check_model,
-    get_example_prediction,
-    get_labels_and_features_from_dataset,
-)
+from io_utils import (read_column_mapping, save_job_to_pipe,
+                      write_column_mapping, write_log_to_user_file)
+from text_classification import (check_model, get_example_prediction,
+                                 get_labels_and_features_from_dataset)
 from wordings import CONFIRM_MAPPING_DETAILS_FAIL_RAW
 
 MAX_LABELS = 20
@@ -28,6 +21,7 @@ HF_REPO_ID = "HF_REPO_ID"
 HF_SPACE_ID = "SPACE_ID"
 HF_WRITE_TOKEN = "HF_WRITE_TOKEN"
 
+
 def check_dataset_and_get_config(dataset_id, uid):
     try:
         write_column_mapping(None, uid)  # reset column mapping
@@ -48,7 +42,9 @@ def check_dataset_and_get_split(dataset_id, dataset_config):
         pass
 
 
-def write_column_mapping_to_config(dataset_id, dataset_config, dataset_split, uid, *labels):
+def write_column_mapping_to_config(
+    dataset_id, dataset_config, dataset_split, uid, *labels
+):
     # TODO: Substitute 'text' with more features for zero-shot
     # we are not using ds features because we only support "text" for now
     ds_labels, _ = get_labels_and_features_from_dataset(