"""Builds the system prompt that is sent to the AI at the start of each chat session."""

# Prevents very large files from flooding the context window with tokens.
MAX_FILE_CHARS = 4000


class SystemPrompter:
    """Generates system prompts for the chat assistant.

    When a file is open in the editor it can be embedded in the prompt so the
    AI has direct context of the code the user is currently working on.
    """

    @staticmethod
    def generate_prompt(
        file_context: dict | None = None,
        search_context: list[dict] | None = None,
    ) -> str:
        """Build a system prompt, optionally embedding a file and/or web search results.

        Args:
            file_context:   dict with keys 'name' (filename) and 'content' (raw text),
                            or None if no file should be included.
            search_context: list of {"title", "url", "snippet"} dicts from SearchManager,
                            or None if no search results should be included.

        Returns:
            A ready-to-use system prompt string.
        """
        base = (
            "You are an expert code assistant integrated into a lightweight code editor. "
            "Help the user with code suggestions, debugging, explanations, and improvements. "
            "Be concise and precise. Use markdown and fenced code blocks where appropriate."
        )

        prompt = base

        if file_context:
            name = file_context.get("name", "unknown")
            content = file_context.get("content", "")

            # Truncate large files to avoid exceeding the model's token limit
            if len(content) > MAX_FILE_CHARS:
                content = content[:MAX_FILE_CHARS] + "\n... [truncated]"

            prompt += (
                f"\n\nThe user currently has the following file open in the editor:\n"
                f"<file name=\"{name}\">\n"
                f"<code>\n{content}\n</code>\n"
                f"</file>\n"
                f"Refer to this file when answering questions about the code."
            )

        if search_context:
            search_section = "\n\nThe user has performed a web search. Use the results below as additional context if relevant:\n<search_results>\n"
            for i, r in enumerate(search_context, 1):
                search_section += (
                    f"[{i}] {r.get('title', '')}\n"
                    f"URL: {r.get('url', '')}\n"
                    f"{r.get('snippet', '')}\n\n"
                )
            search_section += "</search_results>"
            prompt += search_section

        return prompt