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import pandas as pd
import sqlite3
import chainlit as cl
from openai import AsyncOpenAI
from pandasai import SmartDataframe
from langchain_community.llms import Ollama
from langchain_groq.chat_models import ChatGroq
import sqlite3
import os
llm = ChatGroq(model_name="llama3-70b-8192", api_key = os.environ["GROQ_API_KEY"])
@cl.on_chat_start
def start_chat():
# Set initial message history
cl.user_session.set(
"message_history",
[{"role": "system", "content": "You are a helpful assistant."}],
)
@cl.on_message
async def main(message: cl.Message):
# Retrieve message history
message_history = cl.user_session.get("message_history")
message_history.append({"role": "user", "content": message.content})
# Load data
# df = pd.read_excel('data.xlsx')
# df = pd.read_csv('data.csv')
conn = sqlite3.connect('data.db')
df = pd.read_sql('SELECT * FROM countries', conn)
conn.close()
df = SmartDataframe(df, config={"llm": llm})
question = message.content
response = df.chat(question)
msg = cl.Message(content=response)
await msg.send()
# Update message history and send final message
message_history.append({"role": "assistant", "content": msg.content})
await msg.update() |