-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathchatbot.py
184 lines (139 loc) · 6.72 KB
/
chatbot.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
import openai
import tiktoken
#load system messages
def load_sys_message(sys_message_path):
file1 = open(sys_message_path, 'r')
Lines = file1.readlines()
sys_content = ' '.join([item[:-1] for item in Lines])
sys_message = [{'role':'system',
'content':sys_content}]
return sys_message
#load QA messages
def QA_messages(QA_path):
file1 = open(QA_path, 'r')
Lines = file1.readlines()
examples = [item[:-1] for item in Lines]
Q_contents = []
A_contents = []
for i in range(len(examples)):
example = examples[i]
if example.startswith('Q'):
Q_contents.append(example)
elif example.startswith('A'):
A_contents.append(example)
messages = []
for i in range(len(Q_contents)):
Q_content = Q_contents[i]
A_content = A_contents[i]
messages += [{'role': 'system', 'name': 'user', 'content': Q_content},
{'role': 'system', 'name': 'assistant', 'content': A_content}]
return messages
def post_process_resonpse(resonpse):
if resonpse.startswith('A: Counselor: '):
resonpse = resonpse[14:]
if ' (Explanation: ' in resonpse:
resonpse = resonpse.split(' (Explanation: ')[0]
return resonpse
def post_process_resonpse_open_question(resonpse):
if resonpse.startswith('A: Counselor: '):
resonpse = resonpse[14:]
if ' (Background investigation: ' in resonpse:
resonpse = resonpse.split(' (Background investigation: ')[0]
return resonpse
#模块一：开场白
class OpenStatementChat:
def __init__(self, system_chat_example, temperature):
self.messages = system_chat_example
self.temperature = temperature
def chat(self):
prompt = "Could you please provide an open-ended question as an opening statement for the following psychological counseling?"
messages = self.messages+[{ "role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages = messages,
temperature = self.temperature
)
answer = response.choices[0]['message']['content']
return answer
#模块四：心理技术判断
class StrategyExplainedClassifier:
def __init__(self, system_chat_example, temperature):
self.messages = system_chat_example
self.temperature = temperature
def answer_and_save(self, input, explain_txt, save_txt):
input_details = input+ explain_txt
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages = [{ "role": "user", "content": input_details}],
temperature = self.temperature
)
answer_details = response.choices[0]['message']['content']
return save_txt+answer_details
def chat(self, input):
messages = self.messages+[{ "role": "user", "content": input}]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages = messages,
temperature = self.temperature
)
answer = response.choices[0]['message']['content']
save_infos = []
if 'Specific techniques' in answer or 'Content and emotional response' in answer:
if 'Specific techniques' in answer:
save_infos.append(self.answer_and_save(input, '(Summarize the problem the person is experiencing in this sentence and keep it to one sentence.)', 'Specific techniques and save：'))
if 'Content and emotional response' in answer:
save_infos.append(self.answer_and_save(input, '(Please summarize what kind of emotion the person is experiencing in this sentence, and if there is a reason for this emotion, summarize it, and keep it to one sentence.)', 'Content and Emotional Response and save: '))
else:
save_infos.append(self.answer_and_save(input, '(Record the important information in this sentence, keeping it to one sentence.)', 'Information save： '))
return answer, save_infos
class ChatAnswer:
def __init__(self, system_chat_example, temperature):
self.messages = system_chat_example
self.temperature = temperature
def chat(self, input):
prompt = input
messages = self.messages+[{ "role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages = messages,
temperature = self.temperature
)
answer = response.choices[0]['message']['content']
#print(answer)
return answer
class ChatBot:
def __init__(self, system_chat_example, temperature):
self.messages = system_chat_example
self.temperature = temperature
def chat(self, input):
prompt = input
messages = self.messages+[{ "role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages = messages,
temperature = self.temperature
)
answer = response.choices[0]['message']['content']
print(answer)
tokens = self.num_tokens_from_messages(messages)
print(f"Total tokens: {tokens}")
if tokens > 4000:
print("WARNING: Number of tokens exceeds 4000. Truncating messages.")
messages = messages[2:]
def num_tokens_from_messages(self, messages, model="gpt-3.5-turbo"):
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo": # note: future models may deviate from this
num_tokens = 0
for message in messages:
num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name": # if there's a name, the role is omitted
num_tokens += -1 # role is always required and always 1 token
num_tokens += 2 # every reply is primed with <im_start>assistant
return num_tokens
else:
raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.""")