import subprocess
from clingo import Control
import dspy
from dspygen.utils.dspy_tools import init_dspy


# Configuration
DOMAIN_FILE = 'domain.pddl'
PROBLEM_FILE = 'problem.pddl'

# Prompt template and domain rules
prompt_template = """
Translate the following user query to ASP: "{query}"
Steps:
1. Define a set of supported goals.
2. Describe argument types.
3. Describe domain goals.
4. Define instructions.
5. Construct LLM prompt.
"""
domain_rules = """
% Add your domain-specific rules here
"""


# Function to perform logical reasoning with ASP
def reason_with_asp(asp_code, domain_rules):
    ctl = Control()
    ctl.add("base", [], domain_rules)
    ctl.add("base", [], asp_code)
    ctl.ground([("base", [])])

    models = []
    with ctl.solve(yield_=True) as handle:
        for model in handle:
            models.append(model.symbols(atoms=True))
    return models


# Function to convert ASP models to PDDL
def asp_to_pddl(models):
    pddl_objects = ""
    pddl_init = ""
    pddl_goal = ""

    # Convert ASP models to PDDL
    for model in models:
        for symbol in model:
            # Add objects, initial state, and goal state conversion logic here
            # Example placeholder logic (should be replaced with actual logic):
            if symbol.name == "goal":
                goal_var = symbol.arguments[0].name
                goal_name = symbol.arguments[1].name
                pddl_objects += f"{goal_var} - {goal_name}\n"
                pddl_init += f"(has_type {goal_var} {goal_name})\n"
                pddl_goal += f"(goal {goal_var} {goal_name})\n"
            # Add more logic as needed to handle all relevant symbols

    pddl_problem = f"""
    (define (problem example)
      (:domain {DOMAIN_FILE})
      (:objects {pddl_objects})
      (:init {pddl_init})
      (:goal (and {pddl_goal}))
    )
    """
    return pddl_problem


# Function to generate plan using classical planner
def generate_plan(domain_file, problem_file):
    # Run the planner with the provided domain and problem files
    planner = 'fast-downward --alias seq-sat-lama-2011'
    command = f'{planner} {domain_file} {problem_file}'

    # Execute the command and capture the output plan
    plan = subprocess.run(command, shell=True, capture_output=True, text=True)
    return plan.stdout


# Main function to process queries
def process_query(query):
    # Step 1: Translate query to ASP using the DSPy Module
    translate_asp_module = TranslateToASPModule()
    asp_representation = translate_asp_module.forward(query, prompt_template)

    # Step 2: Perform reasoning with ASP
    asp_models = reason_with_asp(asp_representation, domain_rules)

    # Step 3: Convert ASP models to PDDL
    pddl_problem_content = asp_to_pddl(asp_models)

    # Step 4: Generate PDDL problem file using DSPy Module
    generate_problem_pddl_module = GenerateProblemPDDLModule()
    problem_file_path = generate_problem_pddl_module.forward(pddl_problem_content)

    # Step 5: Generate plan using the planner
    plan = generate_plan(DOMAIN_FILE, problem_file_path)

    return plan


# Example usage
if __name__ == "__main__":
    # Example user query
    user_query = "Show me my profit and loss report for the first quarter of 2023."

    # Process the query
    plan = process_query(user_query)
    print(plan)
