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import time
import openai
import random
from transformers import pipeline
class RandomAnalyser:
def __init__(self):
self.LABELS = ['negative', 'neutral', 'positive']
def predict(self, X: list):
return [random.choice(self.LABELS) for x in X]
class RoBERTaAnalyser:
def __init__(self):
self.analyser = pipeline(task="sentiment-analysis", model="Cloudy1225/stackoverflow-roberta-base-sentiment")
def predict(self, X: list):
sentiments = []
for x in X:
x = RoBERTaAnalyser.preprocess(x)
prediction = self.analyser(x)
sentiments.append(prediction[0]['label'])
return sentiments
@staticmethod
def preprocess(text):
"""Preprocess text (username and link placeholders, remove line breaks)"""
new_text = []
for t in text.split(' '):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return ' '.join(new_text).strip()
class ChatGPTAnalyser:
def __init__(self):
# import os
# os.environ["http_proxy"] = "http://127.0.0.1:10080"
# os.environ["https_proxy"] = "http://127.0.0.1:10080"
self.MODEL = "gpt-3.5-turbo"
self.KEYs = [
"key1",
"key2",
]
self.TASK_NAME = 'Sentiment Classification'
self.TASK_DEFINITION = 'Given the sentence, assign a sentiment label from [negative, neutral, positive].'
self.OUT_FORMAT = 'Return label only without any other text.'
self.PROMPT_PREFIX = f"Please perform {self.TASK_NAME} task.{self.TASK_DEFINITION}{self.OUT_FORMAT}\nSentence:\n{{}}\nLabel:"
def predict(self, X: list):
sentiments = []
for i in range(len(X)):
prompt = self.PROMPT_PREFIX.format(X[i])
messages = [{"role": "user", "content": prompt}]
# openai.api_key = self.KEYs[i % len(self.KEYs)]
openai.api_key = random.choice(self.KEYs)
while True:
try:
response = openai.ChatCompletion.create(
model=self.MODEL,
messages=messages,
temperature=0,
n=1,
stop=None
)
sentiment = response.choices[0].message.content
sentiments.append(sentiment.strip().lower())
break
except openai.RateLimitError:
sleep_snds = 60
time.sleep(sleep_snds)
continue
except openai.APIError:
sleep_snds = 60
time.sleep(sleep_snds)
continue
return sentiments |