more LLM load for home windows

app.py

@@ -6,8 +6,13 @@ import torch
 from huggingface_hub import login
 from transformers import pipeline
 from transformers import GPT2Tokenizer, GPT2LMHeadModel,set_seed
+from transformers import AutoTokenizer, AutoModelWithLMHead,AutoModelForSeq2SeqLM
 import datetime
-
+import nltk
+nltk.download('stopwords')
+nltk.download('punctuation')
+nltk.download('punkt')
+from rake_nltk import Rake
 
 login(os.environ["HF_TOKEN"])
 
@@ -28,7 +33,10 @@ print("loading models")
 tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\language_models_windows")
 original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
 untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500',cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
-
+question_generation_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
+question_generation_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
+paraphrase_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
+paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws",cache_dir="C:\\Users\\zmbfeng\\Google Drive\\Avatar\\language_models_windows")
 default_temperature=0.01
 default_seed=43
 def create_response(input_str,

requirements.txt

@@ -1,4 +1,9 @@
 gradio
 transformers
 gTTS
-torch
+torch
+rake-nltk
+versions
+gensim
+sentencepiece
+protobuf