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from langchain import hub, LLMChain
from langchain.chat_models import ChatOpenAI
from keys import OPENAI_API_KEY
import os

os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

prompt_maker_template = hub.pull("hardkothari/prompt-maker")

llm = ChatOpenAI()

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from langchain.utilities import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain import hub
from keys import OPENAI_API_KEY
import os

os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

# Initialize database

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import spacy
import networkx as nx
import matplotlib.pyplot as plt

# Initialize spaCy model
nlp = spacy.load("en_core_web_sm")

# Define the function for simple Semantic Role Labeling (SRL)
def simple_srl(sentence, nlp):
    doc = nlp(sentence)

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class Tool():

    _all_tools = []

    def __init__(self, name, description, call_function: Callable[..., Any]) -> None:
        self.name = name
        self.description = description
        self._call_function = call_function
        Tool._all_tools.append(self)

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class Agent():
    def __init__(self) -> None:
        self.llm = OpenaiLanguageModel(anonymize=False)
        self.memory = {}

        # Define standard tools
        calc_tool = Tool("Calculator", "Performs basic mathematical operations", Agent.calculator) # noqa

        self.tools = Tool.all()

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agent = Agent()

response = agent("What is the square root of 44494949494949494")

# Response
""" What is the square root of 44494949494949494 

Thinking... 

The first step is to use the Calculator tool to calculate the square root of 44494949494949494. 

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from __future__ import annotations
from typing import Any, List, Callable, Optional, Union
from mytoken import apikey
import os
from anonLLM.llm import OpenaiLanguageModel
from pydantic import BaseModel, create_model
import inspect
import json

os.environ["OPENAI_API_KEY"] = apikey

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from __future__ import annotations
from typing import Any, List, Callable, Optional, Union
from mytoken import apikey
import os
from anonLLM.llm import OpenaiLanguageModel
from pydantic import BaseModel, create_model
import inspect
import json
import wikipedia
from datetime import datetime

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import openpyxl
import pandas as pd

# Load the Excel file
workbook = openpyxl.load_workbook('online_retail_II.xlsx')

# Create an empty list to store data from all sheets
all_data = []

# Iterate through all sheets in the workbook

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import pyfpgrowth
import pandas as pd

# Assuming df is your existing DataFrame
transactions = combined_data.groupby('Invoice')['Description'].apply(list).reset_index(name='items')

# Convert the 'items' column into a list of itemsets
transactions['items'] = transactions['items'].apply(lambda x: list(set(x)))

# Create a list of transactions as lists