import os
from dotenv import load_dotenv
import openai
import json
import re
from .base_agent import BaseAgent
from curriculum import Curriculum, Chapter, Module

class CurriculumPlannerAgent(BaseAgent):
    def __init__(self):
        super().__init__("CurriculumPlannerAgent")
        load_dotenv()
        api_key = os.getenv("OPENAI_API_KEY")
        self.client = openai.OpenAI(api_key=api_key)

    def process(self, topic, level):
        system_prompt = (
            "You are an Expert Instructional Architect. Design a detailed, academically rigorous curriculum.\n\n"

            "### Rules:\n"
            "1. Ensure academic depth and avoid trivial content.\n"
            "2. Respect the level:\n"
            "- Novice: fundamentals, 4–6 modules per chapter.\n"
            "- Intermediate: problem-solving & applications, 5–7 modules per chapter.\n"
            "- Advanced: theory, research, edge cases, 6–8 modules per chapter.\n"
            "3. Each module must have:\n"
            "- `module_name`\n"
            "- `learning_objective` (with knowledge, skills, applications, examples)\n"
            "4. Use progressive complexity.\n\n"

            "### Example Curriculum (Novice, Topic: Python Programming)\n"
            "{\n"
            "  \"chapters\": [\n"
            "    {\n"
            "      \"chapter_name\": \"Introduction to Python\",\n"
            "      \"modules\": [\n"
            "        {\"module_name\": \"What is Python?\", \"learning_objective\": \"Understand Python’s role as a programming language, its history, and its everyday applications.\"},\n"
            "        {\"module_name\": \"Setting Up Python\", \"learning_objective\": \"Learn how to install Python and write your first basic script.\"},\n"
            "        {\"module_name\": \"Variables and Data Types\", \"learning_objective\": \"Understand how to store data in variables and use types such as strings, numbers, and booleans.\"}\n"
            "      ]\n"
            "    },\n"
            "    {\n"
            "      \"chapter_name\": \"Control Structures\",\n"
            "      \"modules\": [\n"
            "        {\"module_name\": \"If Statements\", \"learning_objective\": \"Learn decision-making in programs with if/else statements and simple examples.\"},\n"
            "        {\"module_name\": \"Loops\", \"learning_objective\": \"Understand repetition using for and while loops with practical use cases.\"}\n"
            "      ]\n"
            "    }\n"
            "  ]\n"
            "}\n\n"

            "### Instructions:\n"
            "- Follow the same style for the requested topic.\n"
            "- Do NOT output explanations outside JSON.\n"
        )

        user_prompt = f"Generate a curriculum for:\nTopic: {topic}\nLevel: {level}"

        response = self.client.chat.completions.create(
            model="gpt-4o-mini",
            response_format={"type": "json_object"},
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
        )

        try:
            curriculum_json = response.choices[0].message.content.strip()
            match = re.search(r'\{.*\}', curriculum_json, re.DOTALL)
            if match:
                json_str = match.group(0)
            else:
                raise ValueError("No JSON found in the response")

            chapters_obj = json.loads(json_str)
            chapters_data = chapters_obj["chapters"]

            chapters = []
            for ch in chapters_data:
                modules = [Module(m["module_name"], m["learning_objective"]) for m in ch["modules"]]
                chapters.append(Chapter(ch["chapter_name"], modules))

            return Curriculum(topic, chapters)

        except Exception as e:
            print("[CurriculumPlannerAgent] Error parsing curriculum:", e)
            return Curriculum(topic, [Chapter("General Introduction", [Module("Overview")])])