Update app.py

app.py

@@ -1,10 +1,3 @@
-#from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-"""
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-"""
-
 import random
 import json
 
@@ -14,6 +7,7 @@ from model import NeuralNet
 from nltk_utils import bag_of_words, tokenize
 
 device = torch.device("cpu")
+
 with open('./intents.json', 'r') as json_data:
     intents = json.load(json_data)
 
@@ -31,25 +25,10 @@ model = NeuralNet(input_size, hidden_size, output_size).to(device)
 model.load_state_dict(model_state)
 model.eval()
 
-def predict(sentence, history):
+def predict(message, history):
     history = history or []
-    """
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-    # convert the tokens to text, and then split the responses into the right format
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    """
-
-    sentence1 = tokenize(sentence)
-    X = bag_of_words(sentence1, all_words)
+    sentence = tokenize(message)
+    X = bag_of_words(sentence, all_words)
     X = X.reshape(1, X.shape[0])
     X = torch.from_numpy(X).to(device)