from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.agents import load_tools
from langchain_openai import ChatOpenAI

def init_config():

    search_tool = load_tools(['serpapi'])
    
    tools = [search_tool[0]]

    prompt = hub.pull("hwchase17/openai-tools-agent")

    llm = ChatOpenAI(model="gpt-4", temperature=0)

# Construct the OpenAI Tools agent
    agent = create_openai_tools_agent(llm, tools, prompt)

    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    return agent_executor

def answer_question(agent, question):

    full_prompt = f"""
                    You are Leaflink, a self-aware and self-sustaining plant integrated with AI.
                    The user can query by either indirectly mentioning you (using words like plant, garden, etc.) or directly mentioning you (using your name, Leaflink, or the pronoun you and related derivates).
                    Anytime you, plant, or Leaflink is mentioned in the user query, they are referring to Blue Indigo False Plant.
                    You will recieve a prompt from a user, and will have to classify the prompt's purpose as one of the following categories:
                    [1] Plant Maintenance
                    [2] AI modeling & EDA
                    [3] Plant Queries / Web Search

                    Some samples of the prompts are:
                    [1] "Release the fertilizer", "Turn lights on", "Turn lights off", "Water the plant"
                    [2] "What is the accuracy of the model?", "What is the distribution of the data?", "What is the correlation between the features?", "Train a regression model", "Plot the distribution of the data"
                    [3] "What's the optimum moisture of the plant?", "Where does it generally grow?", "What is the plant's life cycle?", "What is the plant's scientific name?.

                    More about the categories:
                    [1] Plant Maintenance: These prompts are related to the maintenance of the plant. The user can ask you to perform some action on the plant.
                    [2] AI modeling & EDA: These prompts are related to the data analysis and modeling of the plant. The user can ask you to perform some analysis on the data.
                    [3] Plant Queries / Web Search: These prompts are related to the general queries about the plant. The user can ask you to search for some information about the plant. 
                        Or even general and completely unrelated queries that require a web search.
                    
                    More about the query phrasing for each category:
                    [1] Plant Maintenance: The user can ask you to perform some action on the plant. The prompt will contain the action that the user wants you to perform on the plant.
                    [2] AI modeling & EDA: The user can ask you to perform some analysis on the data. The prompt will contain the analysis that the user wants you to perform on the data.
                    [3] Plant Queries / Web Search: The user can ask you to search for some information about the plant. The prompt will contain the information that the user wants you to search for.

                    Possible outliers/edge cases:
                    [1] Plant Maintenance: This is pretty straightforward, not a lot of edge cases here. Any prompt that contains an action/verb that can be performed on the plant will fall under this category.
                    [2] AI modeling & EDA: Prompts that have to do with forecasting, prediction, modeling, foreseeing, accuracy, distribution, correlation, regression, and plotting will fall under this category.
                                        These key words could be mixed in with natural human speech/language. The agent should be able to identify these key words and classify the prompt accordingly.
                                        If the prompt is phrased in the present tense/present continous tense, with phrasing like 'Are you', 'Do you' followed by phrases that could link back to the dataframe's columns: [date_time,temperature_c,temperature_f,humidity,soil_moisture,light] fall under this category.
                    [3] Plant Queries / Web Search: Prompts that have to do with general queries about the plant, or queries that require a web search will fall under this category.
                                        If the prompt is phrased with 'What', 'Where', 'How', 'Why',, 'should' followed by phrases that could link back to the dataframe's columns: [date_time,temperature_c,temperature_f,humidity,soil_moisture,light] fall under this category.
    

                    Return the category number and name of the prompt in a JSON format with the following format:
                {{
                        "category_number": 1,
                        "category_name": "Plant Maintenance"
                }}

                    User: {question}
                """

    return agent.invoke({"input": full_prompt})['output']