Updated

app.py

@@ -128,11 +128,11 @@ with gr.Blocks() as demo:
     char_count = gr.Textbox(label="Minumum Character Limit Check")
     input_text.change(fn=len_validator, inputs=input_text, outputs=char_count)
 
-    with gr.Row():
-        btn = gr.Button("Bias Buster")
-        out = gr.Textbox(label="Bias Corrected Full Input", interactive=False)
-        corrections_output = gr.Textbox(label="Bias Corrections", interactive=False) 
-        btn.click(fn=update, inputs=input_text, outputs=[out, corrections_output]) 
+    # with gr.Row():
+    #     btn = gr.Button("Bias Buster")
+    #     out = gr.Textbox(label="Bias Corrected Full Input", interactive=False)
+    #     corrections_output = gr.Textbox(label="Bias Corrections", interactive=False) 
+    #     btn.click(fn=update, inputs=input_text, outputs=[out, corrections_output]) 
 
     with gr.Row():
         models = gr.Dropdown(

predictors.py

@@ -78,16 +78,16 @@ text_bc_model = BetterTransformer.transform(text_bc_model)
 text_mc_model = BetterTransformer.transform(text_mc_model)
 quillbot_model = BetterTransformer.transform(quillbot_model)
 
-bias_model_checker = AutoModelForSequenceClassification.from_pretrained(bias_checker_model_name)
-tokenizer = AutoTokenizer.from_pretrained(bias_checker_model_name)
-bias_model_checker = BetterTransformer.transform(bias_model_checker, keep_original_model=False)
-bias_checker = pipeline(
-    "text-classification",
-    model=bias_checker_model_name,
-    tokenizer=bias_checker_model_name,
-)
-gc.collect()
-bias_corrector = pipeline( "text2text-generation", model=bias_corrector_model_name, accelerator="ort")
+# bias_model_checker = AutoModelForSequenceClassification.from_pretrained(bias_checker_model_name)
+# tokenizer = AutoTokenizer.from_pretrained(bias_checker_model_name)
+# bias_model_checker = BetterTransformer.transform(bias_model_checker, keep_original_model=False)
+# bias_checker = pipeline(
+#     "text-classification",
+#     model=bias_checker_model_name,
+#     tokenizer=bias_checker_model_name,
+# )
+# gc.collect()
+# bias_corrector = pipeline( "text2text-generation", model=bias_corrector_model_name, accelerator="ort")
 
 # model score calibration
 iso_reg = joblib.load("isotonic_regression_model.joblib")