Create llmware_module.py

llmware_module.py

@@ -0,0 +1,47 @@
+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