import pandas as pd
import langchain
import os 
import pandas as pd 
from langchain.agents import create_pandas_dataframe_agent 
from langchain.llms import OpenAI
import gradio as gr
from langchain import PromptTemplate

os.environ['REQUESTS_CA_BUNDLE'] = 'caadmin.netskope.com.crt'

os.environ['OPENAI_API_KEY']="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"

df_holdings = pd.read_csv("holdings.csv")

df_trades = pd.read_csv("trades.csv")

agent = create_pandas_dataframe_agent(OpenAI(temperature=0.0), [df_holdings,df_trades], verbose=True,agent_executor_kwargs={"handle_parsing_errors": True}) 
openai = OpenAI(temperature=0.0,model="gpt-3.5-turbo",max_tokens=1000,top_p=1,top_k=0) 
                             
template = """Answer the question based on the context. Dont make your own questions. If the question is not related to dataframe strictly generate Final Answer as "Sorry can not find the answer".Question: {query}"""

prompt_template = PromptTemplate(
    input_variables=["query"],
    template=template
)

def chatbot(message,history):
    output = agent(prompt_template.format(query=message))['output']
    print(output)
    return output
          
gr.ChatInterface(chatbot).launch()