app.py

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

os.environ.get('OPENAI_API_KEY')

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=False,agent_executor_kwargs={"handle_parsing_errors": True}) 
openai = OpenAI(temperature=0.0,model="gpt-3.5-turbo-16k",top_p=1,top_k=0,reduce_k_below_max_tokens=True) 
                             
template = """Answer the question based on the context. If the question is not related to dataframe or not meaningful or a single alphabet then 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()