add app

app.py

@@ -8,33 +8,39 @@ MODEL_PATH = 'llongpre/DialoGPT-small-miles'
 tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
 
-def predict(input, history=[]):
-    # 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,
-                            no_repeat_ngram_size=3,
-                            top_p = 0.92,
-                            top_k = 50
-                            ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    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
+# def predict(input, history=[]):
+#     # 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,
+#                             no_repeat_ngram_size=3,
+#                             top_p = 0.92,
+#                             top_k = 50
+#                             ).tolist()
+#
+#     # convert the tokens to text, and then split the responses into lines
+#     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
+#
+#     return response, history
+#
+# from transformers.utils import logging
 
-    return response, history
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers")
+logger.info("INFO")
 
 def generate_answer(input, history=[]):
     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
     history = history.append(input)
-    print(history)
+    logger.info(history)
     if len(history) > MAX_HISTORY:
         history = history[-MAX_HISTORY:]
     bot_input_ids = torch.cat(history, dim=-1)