from langchain import hub, callbacks
from langchain_core.output_parsers import JsonOutputParser
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field, UUID4
from typing import List
from pydantic import BaseModel
from typing import List, Optional
import os


class Expectation(BaseModel):
    expectation: str = Field(
        description="The learning outcome that the course designer has identified for the learner to demonstrate upon successful completion of the module.")
    check_question: str = Field(
        description="Targeted question that the course designer have developed to assess the learner's understanding of the learning outcomes.")


class Expectations(BaseModel):
    expectations: List[Expectation]


class Response(BaseModel):
    run_id: UUID4
    expectations: List[Expectation]


class Inputs(BaseModel):
    course: str
    module: str
    concepts: List[str]
    existing_expectations: Optional[List[Expectation]]


class SuggestExpectations:
    def kickoff(self, inputs={}):
        self.course = inputs["course"]
        self.module = inputs["module"]
        self.existing_expectations = inputs["existing_expectations"]
        self.concepts = inputs["concepts"]
        llm_response = self._get_suggestions()
        return {
          "run_id": self.run_id,
          "expectations": llm_response["expectations"]
        }

    def _get_suggestions(self):
        parser = JsonOutputParser(pydantic_object=Expectations)
        chain = self._build_chain()

        # Existing Expectations
        existing_expectations = []
        for expectation in self.existing_expectations:
            existing_expectations.append(f"""
            Learning Outcome: {expectation.expectation}
            Check Question: {expectation.check_question}
                                       """)

        existing_expectations_str = ""
        if len(existing_expectations) > 0:
            existing_expectations_str = "Here are existing Learning Outcomes & Check Questions for the module, don't repeat these learning outcomes:\n ```"
            existing_expectations_str += "\n".join(existing_expectations)
            existing_expectations_str += "\n```"

        with callbacks.collect_runs() as cb:
            llm_response = chain.invoke({
                "course": self.course, "module": self.module, "concepts": "* " + ("\n* ".join(self.concepts)),
                "format_instructions": parser.get_format_instructions(),
                "existing_expectations": existing_expectations_str
            })
            self.run_id = cb.traced_runs[0].id

        return llm_response

    def _build_chain(self):
        parser = JsonOutputParser(pydantic_object=Expectations)
        prompt = hub.pull("course_learn_suggest_expectations")
        llm = ChatOpenAI(model=os.environ['OPENAI_MODEL'], temperature=0.2)
        chain = (prompt | llm | parser).with_config({
            "tags": ["course_learn", "suggest_expectations"], "run_name": "Suggest Module Expectations",
            "metadata": {
                "version": "v1.0.0",
                "growth_activity": "course_learn",
                "env": os.environ["ENV"],
                "model": os.environ["OPENAI_MODEL"],
            }
        })

        return chain


# Example usage
# suggester = SuggestExpectations()
# response = suggester.kickoff(inputs={
#     "course": "SQL",
#     "module": "Query Optimization Techniques",
#     "concepts": [
#         "Watch this video https://youtu.be/BHwzDmr6d7s?si=sfFYnd73y9w0EjGB to understand SQL execution order and some optimization techniques.",
#         "Watch this video https://youtu.be/FoznjTU929c?si=6M3xUIUwAxE6EbKS to understand SQL explain command usage.",
#         "Go over these articles https://intellipaat.com/blog/sql-optimization-techniques/ & https://www.thoughtspot.com/data-trends/data-modeling/optimizing-sql-queries  to understand various query optimization techniques."
#     ]
# })
# print(response)