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from llmware.models import ModelCatalog
from llmware.prompts import Prompt
def classify_sentiment(text):
sentiment_model = ModelCatalog().load_model("slim-sentiment-tool")
response_sentiment = sentiment_model.function_call(text, get_logits=False)
return response_sentiment
def detect_emotions(text):
emotions_model = ModelCatalog().load_model("slim-emotions-tool")
response_emotions = emotions_model.function_call(text, get_logits=False)
return response_emotions
def generate_tags(text):
tags_model = ModelCatalog().load_model("slim-tags-tool")
response_tags = tags_model.function_call(text, get_logits=False)
return response_tags
def identify_topics(text):
topics_model = ModelCatalog().load_model("slim-topics-tool")
response_topics = topics_model.function_call(text, get_logits=False)
return response_topics
def perform_intent(text):
intent_model = ModelCatalog().load_model("slim-intent-tool")
response_intent = intent_model.function_call(text)
return response_intent
def get_ratings(text):
ratings_model = ModelCatalog().load_model("slim-ratings-tool")
response_ratings = ratings_model.function_call(text)
return response_ratings
def get_category(text):
category_model = ModelCatalog().load_model("slim-category-tool")
response_category = category_model.function_call(text)
return response_category
def perform_ner(text):
ner_model = ModelCatalog().load_model("slim-ner-tool")
response_ner = ner_model.function_call(text)
return response_ner
def perform_nli(text):
nli_model = ModelCatalog().load_model("slim-nli-tool")
response_nli = nli_model.function_call(text)
return response_nli |