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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 |