import json
import os
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain, SequentialChain

llm = ChatOpenAI(temperature=0.0, openai_api_key=os.environ["OPENAI"])


def create_intro(vacancy, resume):

    template_vacancy_get_skills = """
    Can you generate me a list of the skills that a candidate is supposed to have for the below vacancy delimited by three backticks. 
    If you do not know if skills are available mention that you do not know and do not make up an answer. 
    Mention the skills in 1 to maximum three words for each skill. Return the skills as a JSON list.

    ```
    {vacancy}
    ```
    """

    prompt_vacancy_get_skills = ChatPromptTemplate.from_template(
        template=template_vacancy_get_skills
    )
    vacancy_skills = LLMChain(
        llm=llm, prompt=prompt_vacancy_get_skills, output_key="vacancy_skills"
    )

    template_resume_check_skills = """
    ```
    {vacancy_skills}
    ```

    Based on the above list of skills required by a vacancy delimited by backticks,
    Can you create a JSON object based on the below keys each starting with '-', with respect to the resume below delimited by three backticks?

    - "skills_present": <list the skills present. If no skills are present return an empty list, do not make up an answer. >
    - "skills_not_present": <list the skills not present. If all skills are present return an empty list, do not make up an answer.>
    - "score": <calculate a percentage of the number of skills present with respect to the total skills requested>

    ```
    {resume}
    ```
    """

    prompt_resume_check_skills = ChatPromptTemplate.from_template(
        template=template_resume_check_skills
    )
    resume_skills = LLMChain(
        llm=llm, prompt=prompt_resume_check_skills, output_key="resume_skills"
    )

    template_resume_past_experiences = """
    Can you generate me a list of the past work experiences that the candidate has based on the resume below enclosed by three backticks.
    Mention the experiences in one sentence of medium length. Return the experiences as a JSON list.

    ```
    {resume}
    ```
    """

    prompt_resume_past_experiences = ChatPromptTemplate.from_template(
        template=template_resume_past_experiences
    )
    past_experiences = LLMChain(
        llm=llm, prompt=prompt_resume_past_experiences, output_key="past_experiences"
    )

    template_vacancy_check_past_experiences = """
    ```
    {past_experiences}
    ```

    Based on the above list of past experiences by a vacancy delimited by backticks,
    Can you create a JSON object based on the below keys each starting with '-', with respect to the vacancy below delimited by three backticks?

    - "relevant_experiences": <list the relevant experiences. If no experiences are relevant return an empty list, do not make up an answer. >
    - "irrelevant_experiences": <list the irrelevant experiences. If all experiences are relevant return an empty list, do not make up an answer.>
    - "score": <calculate a percentage of the number of skills present with respect to the total skills requested>

    ```
    {resume}
    ```
    """

    prompt_vacancy_check_past_experiences = ChatPromptTemplate.from_template(
        template=template_vacancy_check_past_experiences
    )
    check_past_experiences = LLMChain(
        llm=llm,
        prompt=prompt_vacancy_check_past_experiences,
        output_key="check_past_experiences",
    )

    template_introduction_email = """
       You are a recruitment specialist that tries to place the right profiles for the right job.
       I have a vacancy below the delimiter <VACANCY> and ends with </VACANCY> 
       and I have a candidate its resume below the delimiter <RESUME> and it ends with </RESUME>.

       <VACANCY>
       {vacancy}
       </VACANCY>

       <RESUME>
       {resume}
       </RESUME>

       Can you fill in the introduction below and only return as answer this introduction?

       - Role: < the role of the vacancy >
       - Candidate: < name of the candidate >
       - Education: < name the education of the candidate >
       - Experience: < name the 2 most relevant experiences from the candidate for this vacancy. Get them from the "relevant_experiences" key of the JSON object {past_experiences}. If there us no relevant experience, leave this empty. Do not make up an answer or get them from the irrelevant experiences. >
       - Skills: print here a comma seperated list of the "skills_present" key of the JSON object {resume_skills}
       """

    prompt_introduction_email = ChatPromptTemplate.from_template(
        template=template_introduction_email
    )
    introduction_email = LLMChain(
        llm=llm, prompt=prompt_introduction_email, output_key="introduction_email"
    )

    match_resume_vacancy_skills_chain = SequentialChain(
        chains=[
            vacancy_skills,
            resume_skills,
            past_experiences,
            check_past_experiences,
            introduction_email,
        ],
        input_variables=["vacancy", "resume"],
        output_variables=[
            "vacancy_skills",
            "resume_skills",
            "past_experiences",
            "check_past_experiences",
            "introduction_email",
        ],
        verbose=False,
    )

    result = match_resume_vacancy_skills_chain({"vacancy": vacancy, "resume": resume})
    print(result)

    resume_skills = json.loads(result["resume_skills"])
    relevant_skills = len(resume_skills["skills_present"])
    total_skills = len(
        resume_skills["skills_present"] + resume_skills["skills_not_present"]
    )
    score_skills = round(100.0 * (relevant_skills / total_skills), 2)

    check_past_experiences = json.loads(result["check_past_experiences"])
    relevant_experiences = len(check_past_experiences["relevant_experiences"])
    total_experiences = len(
        check_past_experiences["relevant_experiences"]
        + check_past_experiences["irrelevant_experiences"]
    )
    score_experiences = round(100.0 * (relevant_experiences / total_experiences), 2)

    new_line = "\n"

    score = f"""
    Skills (Score: {score_skills}%)
    Relevant Skills: {",".join(resume_skills["skills_present"])}
    Not Relevant Skills: {",".join(resume_skills["skills_not_present"])}    
    """
    return result["introduction_email"], score