from libs.flatlib_service import FlatLibService

class HoraryAnalysis:
    def __init__(self, question, datestr, timestr, latitude, longitude, timezone):
        self.question = question
        self.chart_service = FlatLibService(datestr, timestr, latitude, longitude, timezone)
        self.clarified_question = None  # Placeholder for the clarified question

    def clarify_question(self):
        """
        Interacts with the user to refine the question.

        This method uses NLP techniques to evaluate the clarity of the question.
        For simplicity, it assumes a method that asks for user input or clarification.

        Parameters:
            None

        Returns:
            None
        """
        # This method interacts with the user to refine the question.
        # You could implement NLP techniques here to evaluate the clarity of the question.
        # For simplicity, let's assume a method that asks for user input or clarification.
        clarification_needed = self.check_if_clarification_needed(self.question)
        if clarification_needed:
            self.clarified_question = self.request_user_clarification()
        else:
            self.clarified_question = self.question

    def check_if_clarification_needed(self, question):
        """
        Check if clarification is needed for the given question.

        :param self: The object instance
        :param question: The question to be analyzed
        :return: False if the question is clear enough for analysis, otherwise True
        """
        # Implement logic to determine if the question is clear enough for analysis.
        # Placeholder logic:
        return False  # Assume no clarification is needed for this placeholder.

    def request_user_clarification(self):
        """
        Requests user clarification to refine the question.

        This function is responsible for interacting with the user to refine the question. It prompts the user to provide additional information or clarification on the question. The function currently contains a placeholder logic that returns a hardcoded string as the user-provided clarification.

        Returns:
            str: The user-provided clarification of the question.

        """
        # Implement interaction with the user to refine the question.
        # Placeholder logic:
        return "User-provided clarification of the question."

    def generate_preliminary_evaluation(self):
        """
        Generates a preliminary evaluation of the question clarification.

        This function ensures that the question has been clarified before proceeding. If the question has not been clarified, it calls the `clarify_question()` method to clarify it.

        The function then constructs a valid question string by formatting the clarified question. The valid question string is used to assess if the question is clear and valid for horary analysis. It suggests rephrasing if necessary.

        Note: The actual validation and potential rephrasing logic is currently a placeholder and needs to be implemented.

        Returns:
            str: The clarified question.

        """
        # Ensure question clarification before proceeding.
        if not self.clarified_question:
            self.clarify_question()

        valid_question = f"Assess if the question '{self.clarified_question}' is clear and valid for horary analysis. Suggest rephrasing if necessary."
        # Placeholder for actual validation and potential rephrasing logic.
        return self.clarified_question  # Return the original or refined question after clarification.

    # ...keep the rest of your methods unchanged...

    def perform_full_analysis(self):
        """
        Perform a full analysis after ensuring the clarification of the question.
        """
        # Ensure clarification is handled before analysis.
        if not self.clarified_question:
            self.clarify_question()
        # Proceed with the analysis using the clarified question.
        return super().perform_full_analysis()

    def generate_prompt_step1(self):
        """
        Generate prompt for step 1 of the process, including refined question and astrological analysis.
        """
        refined_question = self.generate_preliminary_evaluation()
        sign, ruler = self.chart_service.ascendant()
        house_positions = self.chart_service.house_position_table()
        prompt = (
            f"Refined Horary Question: '{refined_question}'\n"
            f"1. The sign of the Ascendant is {sign}, and its ruler is {ruler}. These are the significators of the querent.\n"
            "2. Analyze the refined question and link it to one of the house cusps and its ruling sign and planets in this list:\n"
            f"{house_positions}\n"
            "Identify the house that best corresponds to the nature of the question. Summarize the significators of the question based on its nature and the related house cusp."
        )
        return prompt

    def generate_prompt_step2(self, step2_summary):
        """
        Generate prompt step 2 summary with planetary dignities and aspects analysis.

        Parameters:
        - self: the instance of the class
        - step2_summary: a summary for step 2

        Returns:
        - step3_summary: a combined summary of step 1, step 2, and planetary analysis result
        """
        # Analyzing planetary dignities and aspects.
        analysis_result = "Analyze planetary dignities and aspects here."
        step3_summary = f"{step1_summary}\n{step2_summary}\nPlanetary Analysis: {analysis_result}"
        return step3_summary


    def generate_prompt_step3(self, step1_summary, step2_summary):
        """
        Analyzing planetary dignities and aspects.
        """
        # Analyzing planetary dignities and aspects.
        analysis_result = "Analyze planetary dignities and aspects here."
        step3_summary = f"{step1_summary}\n{step2_summary}\nPlanetary Analysis: {analysis_result}"
        return step3_summary

    def generate_prompt_step4(self, step1_summary, step2_summary, step3_summary):
        """
        A function to generate the prompt for step 4, using the summaries of steps 1, 2, and 3, and performing a Moon analysis. Returns the combined summary for step 4.
        Parameters:
            - step1_summary: str
                Summary for step 1
            - step2_summary: str
                Summary for step 2
            - step3_summary: str
                Summary for step 3
        Returns:
            str
                Combined summary for step 4 including the Moon analysis result.
        """
        # Moon analysis.
        moon_analysis_result = "Perform Moon analysis here."
        step4_summary = f"{step1_summary}\n{step2_summary}\n{step3_summary}\nMoon Analysis: {moon_analysis_result}"
        return step4_summary

    def generate_prompt_step5(self, step1_summary, step2_summary, step3_summary, step4_summary):
        """
        Generate the final output by combining summaries of different steps along with a final judgment.

        Parameters:
            step1_summary (str): Summary of step 1 analysis.
            step2_summary (str): Summary of step 2 analysis.
            step3_summary (str): Summary of step 3 analysis.
            step4_summary (str): Summary of step 4 analysis.

        Returns:
            str: The final output combining all summaries and the final judgment.
        """
        # Finalizing judgment.
        final_judgment = "Combine all previous analyses to finalize judgment."
        final_output = f"{step1_summary}\n{step2_summary}\n{step3_summary}\n{step4_summary}\nFinal Judgment: {final_judgment}"
        return final_output

    def perform_full_analysis(self):
        """
        Orchestrating the full analysis.
        """
        # Orchestrating the full analysis.
        step1_summary = self.generate_prompt_step1()
        step2_summary = self.generate_prompt_step2(step1_summary)
        step3_summary = self.generate_prompt_step3(step1_summary, step2_summary)
        step4_summary = self.generate_prompt_step4(step1_summary, step2_summary, step3_summary)
        final_output = self.generate_prompt_step5(step1_summary, step2_summary, step3_summary, step4_summary)
        return final_output