:construction: continue removing gramformer stuff

app.py

@@ -12,8 +12,6 @@ import time
 import warnings
 from pathlib import Path
 from cleantext import clean
-from transformers import pipeline
-from datetime import datetime
 from ai_single_response import query_gpt_model
 from utils import correct_phrase_load
 
@@ -24,30 +22,6 @@ cwd = Path.cwd()
 my_cwd = str(cwd.resolve())  # string so it can be passed to os.path() objects
 
 
-def gramformer_correct(corrector, qphrase: str):
-    """
-    gramformer_correct - correct a string using a text2textgen pipeline model from transformers
-
-    Args:
-        corrector (transformers.pipeline): [transformers pipeline object, already created w/ relevant model]
-        qphrase (str): [text to be corrected]
-
-    Returns:
-        [str]: [corrected text]
-    """
-
-    try:
-        corrected = corrector(
-            clean(qphrase), return_text=True, clean_up_tokenization_spaces=True
-        )
-        return corrected[0]["generated_text"]
-    except:
-        print("NOTE - failed to correct with gramformer")
-        return clean(
-            qphrase
-        )  # fallback is to return the cleaned up version of the message
-
-
 def ask_gpt(message: str):
     """
     ask_gpt - queries the relevant model with a prompt message and returns the response.
@@ -107,7 +81,6 @@ if __name__ == "__main__":
     default_model = "Ballpark-Trivia-L"
     model_loc = cwd.parent / default_model
     model_loc = str(model_loc.resolve())
-    gram_model = "prithivida/grammar_error_correcter_v1"
 
     # init items for the pipeline
     iface = gr.Interface(
@@ -115,8 +88,9 @@ if __name__ == "__main__":
         inputs=["text"],
         outputs="html",
         title=f"Ballpark Trivia: {default_model} Model",
-        description=f"Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the app for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. \n\n One might say.. the answers are in the right ballpark. Further details can be found in the [model card](https://huggingface.co/pszemraj/Ballpark-Trivia-L), and there is also an XL-size model.",
-        article="**Important Notes & About:**\n"
+        description=f"Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the app for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. \n\n One might say.. the answers are in the right ballpark.",
+        article="Further details can be found in the[model card](https: // huggingface.co/pszemraj/Ballpark-Trivia-L), and there is also an XL-size model.\n\n"
+        "**Important Notes & About:**\n"
         "1. the model can take up to 60 seconds to respond sometimes, patience is a virtue.\n"
         "2. the model started from a pretrained checkpoint, and was trained on several different datasets. Anything it says be fact-checked before being regarded as a true statement.\n ",
         css="""
@@ -137,8 +111,6 @@ if __name__ == "__main__":
         theme="darkhuggingface",
     )
 
-    corrector = pipeline("text2text-generation", model=gram_model, device=-1)
-    print("Finished loading the gramformer model - ", datetime.now())
     print(f"using model stored here: \n {model_loc} \n")
 
     # launch the gradio interface and start the server