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from openai import AzureOpenAI
client = AzureOpenAI()
class SentimentAnalyzer:
def __init__(self):
pass
def analyze_sentiment(self, text):
conversation = [
{"role": "system", "content": """You are a Sentiment Analyser.Your task is to analyze and predict the sentiment using scores. Sentiments are categorized into the following list: Positive,Negative,Neutral. You need to provide the sentiment with the highest score. The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
Please analyze the text and provide the output in the following format: Sentiment: score [with one result having the highest score]."""},
{"role": "user", "content": f"""
input text{text}
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=1,
max_tokens=60
)
message = response.choices[0].message.content
return message
def emotion_analysis(self,text):
conversation = [
{"role": "system", "content": """You are a Emotion Analyser.Your task is to analyze and predict the emotion using scores. Emotions are categorized into the following list: Sadness, Happiness, Joy, Fear, Disgust, and Anger. You need to provide the emotion with the highest score. The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
Please analyze the text and provide the output in the following format: emotion: score [with one result having the highest score]."""},
{"role": "user", "content": f"""
input text{text}
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=1,
max_tokens=60
)
message = response.choices[0].message.content
return message
class Summarizer:
def __init__(self):
# self.client = OpenAI()
pass
def generate_summary(self, text):
conversation = [
{"role": "system", "content": "You are a Summarizer"},
{"role": "user", "content": f"""summarize the following conversation delimited by triple backticks.
```{text}```
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=1,
max_tokens=500
)
message = response.choices[0].message.content
return message
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