import google.generativeai as palm
import pandas as pd

import io
from flask import Flask, request, jsonify
from langchain.chains.question_answering import load_qa_chain
from langchain import PromptTemplate, LLMChain
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.chat_models import ChatOpenAI
import pandas as pd
from langchain.llms import OpenAI
from dotenv import load_dotenv
import google.generativeai as palm
from langchain.llms import GooglePalm
import json


from dotenv import load_dotenv

load_dotenv()


app = Flask(__name__)

@app.route("/", methods=["GET"])
def home():
   
    return "Hello Qx!"

@app.route("/predict", methods=["POST"])
def bot():
    load_dotenv()
    #
    json_table = request.json.get("json_table")
    user_question = request.json.get("user_question")
    df = pd.DataFrame(json_table)
    #df['Profit'] = df['Profit'].apply(lambda x: "R{:.1f}".format((x)))
    #df['Revenue'] = df['Revenue'].apply(lambda x: "R{:.1f}".format((x)))
    #llm = ChatOpenAI(model_name='gpt-3.5-turbo-0613', temperature=0, openai_api_key=os.getenv('OPENAI_API_KEY'))
    llm = GooglePalm(temperature=0, google_api_key=os.environ['PALM'])
    #agent = create_pandas_dataframe_agent(llm, df, verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS)
    agent = create_pandas_dataframe_agent(llm, df, agent="structured_chat-zero-shot-react-description", verbose=True)
    response = agent.run(user_question)
    return jsonify(response)


if __name__ == "__main__":
    app.run(debug=True,host="0.0.0.0", port=7860)