swap out grammar model

app.py

@@ -1,8 +1,8 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration, T5Tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-grammar_tokenizer = AutoTokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
-grammar_model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/grammar_error_correcter_v1")
+grammar_tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
+grammar_model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector')
 import torch
 import gradio as gr
 
@@ -22,14 +22,17 @@ def chat(message, history):
 
 
 def feedback(text):
-    tokenized_phrases = grammar_tokenizer([text], return_tensors='pt', padding=True)
-    corrections = grammar_model.generate(**tokenized_phrases)
-    corrections = grammar_tokenizer.batch_decode(corrections, skip_special_tokens=True)
+    # tokenized_phrases = grammar_tokenizer([text], return_tensors='pt', padding=True)
+    # corrections = grammar_model.generate(**tokenized_phrases)
+    # corrections = grammar_tokenizer.batch_decode(corrections, skip_special_tokens=True)
+    batch =  grammar_tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device)
+    corrections= grammar_model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
     print("The corrections are: ", corrections)
-    if corrections[0] == text:
+    if len(corrections) == 0:
         feedback = f'Looks good! Keep up the good work'
     else:
-        feedback = f'\'{corrections[0]}\' might be a little better'
+        suggestion = tokenizer.batch_decode(corrections[0], skip_special_tokens=True)
+        feedback = f'\'{suggestion}\' might be a little better'
     return f'FEEDBACK:  {feedback}'
 
 iface = gr.Interface(