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8f60c74 b259f8d 8f60c74 b259f8d 8f60c74 b259f8d 8f60c74 b259f8d 8f60c74 24b955a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import pandas as pd
import streamlit as st
from langchain.agents import AgentType
from langchain_experimental.agents import create_pandas_dataframe_agent
from langchain_groq import ChatGroq
# streamlit web app configuration
st.set_page_config(
page_title="DF Chat",
page_icon="💬",
layout="centered"
)
with st.sidebar:
groq_api_key=st.text_input("Groq API Key",value="",type="password")
def read_data(file):
if file.name.endswith(".csv"):
return pd.read_csv(file)
else:
return pd.read_excel(file)
# streamlit page title
st.title("🤖 DataFrame ChatBot - Ollama")
# initialize chat history in streamlit session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# initiate df iin session state
if "df" not in st.session_state:
st.session_state.df = None
# file upload widget
uploaded_file = st.file_uploader("Choose a file", type=["csv", "xlsx", "xls"])
if uploaded_file:
st.session_state.df = read_data(uploaded_file)
st.write("DataFrame Preview:")
st.dataframe(st.session_state.df.head())
# display chat history
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# input field for user's message
user_prompt = st.chat_input("Ask LLM...")
if user_prompt:
# add user's message to chat history and display it
st.chat_message("user").markdown(user_prompt)
st.session_state.chat_history.append({"role":"user","content": user_prompt})
# loading the LLM
llm =ChatGroq(model="Gemma-7b-It", groq_api_key=groq_api_key)
pandas_df_agent = create_pandas_dataframe_agent(
llm,
st.session_state.df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
allow_dangerous_code=True
)
messages = [
{"role":"system", "content": "You are a helpful assistant"},
*st.session_state.chat_history
]
response = pandas_df_agent.invoke(messages)
assistant_response = response["output"]
st.session_state.chat_history.append({"role":"assistant", "content": assistant_response})
# display LLM response
with st.chat_message("assistant"):
st.markdown(assistant_response) |