from pydantic import BaseModel
from starfish import data_factory
from starfish.data_template.template_gen import data_gen_template


# Define input schema
class TopicGeneratorInput(BaseModel):
    community_name: str
    seed_topics: list[str]
    num_topics: int
    language: str = "en"


# Define output schema
class TopicGeneratorOutput(BaseModel):
    generated_topics: list[str]
    success: bool
    message: str


@data_gen_template.register(
    name="community/topic_generator_success",
    input_schema=TopicGeneratorInput,
    output_schema=TopicGeneratorOutput,
    description="Generates relevant topics for community discussions using AI models",
    author="Your Name",
    starfish_version="0.1.0",
    dependencies=["posthog>=3.11.0"],
)
# @data_factory(max_concurrency=10)
def topic_generator(input_data: TopicGeneratorInput) -> TopicGeneratorOutput:
    try:
        # Step 1: Generate initial topics
        generated_topics = generate_initial_topics(input_data)

        # Step 2: Process topics in parallel
        @data_factory(max_concurrency=10)
        async def process_topics(topics: list[str]) -> list[str]:
            return [{"ans": refine_topic(topic) for topic in topics}]

        refined_topics = process_topics.run(topics=generated_topics)

        return TopicGeneratorOutput(generated_topics=[topic["ans"] for topic in refined_topics], success=True, message="Topics generated successfully")
    except Exception as e:
        return TopicGeneratorOutput(generated_topics=[], success=False, message=str(e))


# Helper functions
def generate_initial_topics(input_data: TopicGeneratorInput) -> list[str]:
    # Implement your topic generation logic here
    # This could use AI models or other algorithms
    # ... existing code ...
    return [{"topic": "Topic 1"}, {"topic": "Topic 2"}, {"topic": "Topic 3"}]  # Placeholder


def refine_topic(topic: str) -> str:
    # Implement topic refinement logic here
    # ... existing code ...
    return topic.upper()  # Placeholder


# print(result)
