core/evolution_engine.py

# algoforge_prime/core/evolution_engine.py
from core.llm_clients import call_huggingface_api, call_gemini_api, LLMResponse # Absolute
from prompts.system_prompts import get_system_prompt # Absolute
# from ..prompts.prompt_templates import format_evolution_user_prompt # If you create one

def evolve_solution(
    original_solution_text: str,
    comprehensive_critique_text: str, # This includes LLM critique + test summary
    original_combined_score: int,
    problem_description: str,
    problem_type: str,
    llm_client_config: dict # {"type": ..., "model_id": ..., "temp": ..., "max_tokens": ...}
) -> str: # Returns evolved solution text or an error string
    """
    Attempts to evolve a solution based on its critique and score.
    """
    system_p_evolve = get_system_prompt("evolution_general") # problem_type can be used for specialization
    
    user_p_evolve = (
        f"Original Problem Context: \"{problem_description}\"\n\n"
        f"The solution to be evolved achieved a score of {original_combined_score}/10.\n"
        f"Here is the solution text:\n```python\n{original_solution_text}\n```\n\n"
        f"Here is the comprehensive evaluation and critique it received (including any automated test feedback):\n'''\n{comprehensive_critique_text}\n'''\n\n"
        f"Your Task: Based on the above, evolve the provided solution to make it demonstrably superior. "
        f"Address any flaws, incompleteness, or inefficiencies mentioned in the critique or highlighted by test failures. "
        f"If the solution was good, make it even better (e.g., more robust, more efficient, clearer). "
        f"Clearly explain the key improvements you've made as an integral part of your evolved response (e.g., in comments or a concluding summary)."
    )

    llm_response_obj = None # type: LLMResponse
    if llm_client_config["type"] == "hf":
        llm_response_obj = call_huggingface_api(
            user_p_evolve, llm_client_config["model_id"],
            temperature=llm_client_config["temp"], max_new_tokens=llm_client_config["max_tokens"],
            system_prompt_text=system_p_evolve
        )
    elif llm_client_config["type"] == "google_gemini":
        llm_response_obj = call_gemini_api(
            user_p_evolve, llm_client_config["model_id"],
            temperature=llm_client_config["temp"], max_new_tokens=llm_client_config["max_tokens"],
            system_prompt_text=system_p_evolve
        )
    else:
        return f"ERROR (Evolution): Unknown LLM client type '{llm_client_config['type']}'"

    if llm_response_obj.success:
        return llm_response_obj.text
    else:
        return f"ERROR (Evolution with {llm_response_obj.model_id_used}): {llm_response_obj.error}"