Update app.py

app.py

@@ -252,21 +252,11 @@ import torch
 import nltk
 nltk.download('punkt')
 
-#from google.colab import drive  #commented
-#drive.mount("/content/drive")  #commented
-
-# Commented out IPython magic to ensure Python compatibility.
-# %cd "/content/drive/My Drive/Colab Notebooks/NLP/ChatBot/"
-# !ls
-
 import random
 import json
 
 import torch
 
-#from model import NeuralNet
-#from nltk_utils import bag_of_words, tokenize
-
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 with open('intents.json', 'r') as json_data:
@@ -299,13 +289,9 @@ from transformers import MBartForConditionalGeneration, MBart50Tokenizer
 
 
 ################################
-def download_model():
-    model_name = "facebook/mbart-large-50-many-to-many-mmt"
-    model = MBartForConditionalGeneration.from_pretrained(model_name)
-    tokenizer = MBart50Tokenizer.from_pretrained(model_name)
-    return model, tokenizer
-
-model, tokenizer = download_model()
+model_name = "facebook/mbart-large-50-many-to-many-mmt"
+model = MBartForConditionalGeneration.from_pretrained(model_name)
+tokenizer = MBart50Tokenizer.from_pretrained(model_name)
 
 def get_response(input_text):
     model_inputs = tokenizer(input_text, return_tensors="pt")
@@ -348,12 +334,6 @@ def get_response(input_text):
     else:
         return "I do not understand..."
 
-
-
-#def get_chatbot(sentence):
-  
-  #return classifier(sentence)
-
 title = "WeASK: ChatBOT"
 description = "Ask your query here"
 chatbot_demo = gr.Interface(fn=get_response, inputs = 'text',outputs='text',title = title, description = description)