Daturing.py

import os
from langchain.chat_models import init_chat_model
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.tools import tool
from langchain_core.prompts  import ChatPromptTemplate, MessagesPlaceholder

import json
from IPython.display import display
import ipywidgets as widgets

from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
import pandas as pd

# Widget para upload do arquivo
upload = widgets.FileUpload(accept='.json', multiple=False)
display(upload)




if not os.environ.get("GOOGLE_API_KEY"):
    os.environ["GOOGLE_API_KEY"] = "AIzaSyDPpd_Wed8QgLL29C8TD6jl1kVWGfHzZxo"
llm = init_chat_model("google_genai:gemini-2.0-flash")





def get_dataset_from_upload(upload_widget):
    if upload_widget.value:
        file_info = list(upload_widget.value.values())[0]
        dataset = pd.read_json(file_info['content'])
        return dataset
    else:
        return {}


class State(TypedDict):
    user_queries: Annotated[list, add_messages]
    ai_messages: Annotated[list, add_messages]
    dataset: pd.DataFrame  # Dados do .json
    generated_code: Annotated[list, add_messages]  # Código gerado pela IA

    
def Node_input(state: State):
    # Widget para upload do arquivo
    upload = widgets.FileUpload(accept='.json', multiple=False)
    display(upload)
    state["dataset"] = get_dataset_from_upload(upload)
    state["user_queries"].append(input("Digite sua consulta de análise:"))
    return state



def limpezaDeDados(state: State):
  # Faz uma limpeza nos dados
  df = state["dataset"].copy()
  temp = (base_de_dados.isnull().sum() / base_de_dados.shape[0]) >= 0.9
  variaveis_Faltantes = temp.loc[temp == True].index.tolist()
  for v in variaveis_Faltantes:
      df = df.drop(v, axis = 1)

  return df