from pydantic import BaseModel, Field
from transformers import AutoModelForCausalLM, AutoTokenizer

from swarms import ToolAgent
from swarms.tools.json_utils import base_model_to_json

# Load the pre-trained model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "databricks/dolly-v2-12b",
    load_in_4bit=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")


# Initialize the schema for the person's information
class Schema(BaseModel):
    name: str = Field(..., title="Name of the person")
    agent: int = Field(..., title="Age of the person")
    is_student: bool = Field(..., title="Whether the person is a student")
    courses: list[str] = Field(
        ..., title="List of courses the person is taking"
    )


# Convert the schema to a JSON string
tool_schema = base_model_to_json(Schema)

# Define the task to generate a person's information
task = "Generate a person's information based on the following schema:"

# Create an instance of the ToolAgent class
agent = ToolAgent(
    name="dolly-function-agent",
    description="Ana gent to create a child data",
    model=model,
    tokenizer=tokenizer,
    json_schema=tool_schema,
)

# Run the agent to generate the person's information
generated_data = agent.run(task)

# Print the generated data
print(f"Generated data: {generated_data}")