Renamed module and imported it.

examples/pydantic-models-to-grammar-examples.py

@@ -2,13 +2,13 @@
 
 import json
 from enum import Enum
-from typing import Union
+from typing import Union, Optional
 
 import requests
 from pydantic import BaseModel, Field
 
-from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation
+import importlib
-
+pydantic_models_to_grammar = importlib.import_module("pydantic-models-to-grammar")
 
 # Function to get completion on the llama.cpp server with grammar.
 def create_completion(prompt, grammar):
@@ -70,7 +70,7 @@ class Calculator(BaseModel):
 # outer_object_content is the name of outer object content.
 # model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
 # fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+gbnf_grammar, documentation = pydantic_models_to_grammar.generate_gbnf_grammar_and_documentation(
     pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function",
     outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
 
@@ -115,15 +115,14 @@ class Book(BaseModel):
     """
     title: str = Field(..., description="Title of the book.")
     author: str = Field(..., description="Author of the book.")
-    published_year: int = Field(..., description="Publishing year of the book.")
+    published_year: Optional[int] = Field(..., description="Publishing year of the book.")
     keywords: list[str] = Field(..., description="A list of keywords.")
     category: Category = Field(..., description="Category of the book.")
     summary: str = Field(..., description="Summary of the book.")
 
 
 # We need no additional parameters other than our list of pydantic models.
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book])
+gbnf_grammar, documentation = pydantic_models_to_grammar.generate_gbnf_grammar_and_documentation([Book])
-
 
 system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation