change model_type to inference_type

app.py

@@ -11,7 +11,7 @@ import json
 from transformers.pipelines import TextClassificationPipeline
 
 from text_classification import check_column_mapping_keys_validity, text_classification_fix_column_mapping
-from utils import read_scanners, write_scanners, read_model_type, write_model_type, convert_column_mapping_to_json
+from utils import read_scanners, write_scanners, read_inference_type, write_inference_type, convert_column_mapping_to_json
 
 HF_REPO_ID = 'HF_REPO_ID'
 HF_SPACE_ID = 'SPACE_ID'
@@ -266,7 +266,7 @@ with gr.Blocks(theme=theme) as iface:
                 ''')
         with gr.Row():
             run_local = gr.Checkbox(value=True, label="Run in this Space")
-            use_inference = read_model_type('./config.yaml')[0] == 'hf_inference_api'
+            use_inference = read_inference_type('./config.yaml')[0] == 'hf_inference_api'
             run_inference = gr.Checkbox(value=use_inference, label="Run with Inference API")
         
         with gr.Row() as advanced_row:
@@ -347,7 +347,7 @@ with gr.Blocks(theme=theme) as iface:
                                 outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
         scanners.change(write_scanners, inputs=scanners)
         run_inference.change(
-            write_model_type,
+            write_inference_type,
             inputs=[run_inference]
         )
 

config.yaml

@@ -6,5 +6,5 @@ detectors:
   - underconfidence
   - overconfidence
   - spurious_correlation
-model_type:
+inference_type:
   - hf_inference_api

utils.py

@@ -26,23 +26,23 @@ def write_scanners(scanners):
         yaml.dump(config, f, Dumper=Dumper)
 
 # read model_type from yaml file
-def read_model_type(path):
-    model_type = ""
+def read_inference_type(path):
+    inference_type = ""
     with open(path, "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
-        model_type = config.get("model_type", None)
-    return model_type
+        inference_type = config.get("inference_type", None)
+    return inference_type
 
 # write model_type to yaml file
-def write_model_type(use_inference):
+def write_inference_type(use_inference):
     with open(YAML_PATH, "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
     if use_inference:
-        config["model_type"] = ['hf_inference_api']
+        config["inference_type"] = ['hf_inference_api']
     else:
-        config["model_type"] = ['hf_pipeline']
+        config["inference_type"] = ['hf_pipeline']
     with open(YAML_PATH, "w") as f:
-        # save model_type to model_type in yaml
+        # save inference_type to inference_type in yaml
         yaml.dump(config, f, Dumper=Dumper)
 
 # convert column mapping dataframe to json