Update app.py

app.py

@@ -1,14 +1,9 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 # Load the model and tokenizer
-model_name = "vennify/t5-base-grammar-correction"
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained("vennify/t5-base-grammar-correction")
+tokenizer = AutoTokenizer.from_pretrained("vennify/t5-base-grammar-correction")
 
 def correct_text(text, max_length, max_new_tokens, min_length, num_beams, temperature, top_p):
     inputs = tokenizer.encode("grammar: " + text, return_tensors="pt")
@@ -37,7 +32,6 @@ def correct_text(text, max_length, max_new_tokens, min_length, num_beams, temper
     corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return corrected_text
 
-
 def respond(message, history, max_length, min_length, max_new_tokens, num_beams, temperature, top_p):
     response = correct_text(message, max_length, max_new_tokens, min_length, num_beams, temperature, top_p)
     yield response
@@ -48,7 +42,6 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        #gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1,   maximum=256, value=100,  step=1,    label="Max Length"),
         gr.Slider(minimum=1,   maximum=256, value=0,    step=1,    label="Min Length"),
         gr.Slider(minimum=0,   maximum=256, value=0,    step=1,    label="Max New Tokens (optional)"),