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from dotenv import load_dotenv
import streamlit as st
from langchain_experimental.agents import create_csv_agent
from langchain.llms import HuggingFaceHub
from htmlTemplate import css, bot_template, user_template
load_dotenv()
def main():
st.set_page_config(
page_title= "QnA with CSV",
page_icon=":robot_face:",
layout="wide"
)
st.write(css, unsafe_allow_html=True)
st.title("QnA with CSV 🤖")
user_csv = st.file_uploader(label = "", type= "csv")
if not user_csv:
st.warning("Please Upload your CSV file 🚨")
llm = HuggingFaceHub(
repo_id = "mistralai/Mistral-7B-Instruct-v0.2",
model_kwargs = {
'max_new_tokens': 249,
'temperature': 0.3,
}
)
user_input = st.chat_input("Ask your Question about CSV files",
disabled= not user_csv)
with st.sidebar:
st.subheader("Example Questions:")
example_questions = [
"What is the total number of rows in the CSV?",
"Can you show me the first 5 rows of the CSV?",
"What are the column names in the CSV?",
"How many columns does the CSV have?",
"What is the data type of a specific column in the CSV?",
"Can you provide a summary statistics for the numerical columns?",
"Are there any missing values in the CSV?",
"Can you filter the data based on a specific condition?",
"What is the average value of a numerical column?",
]
selected_example = st.selectbox("Select an example question:", example_questions, disabled= not user_csv)
if not user_csv:
st.warning("Please Upload your CSV file 🚨")
if st.button("Use Selected Example"):
user_input = selected_example
if user_csv is not None:
agent = create_csv_agent(
llm = llm,
path = user_csv,
verbose = True,
handle_parsing_errors=True
)
if user_input is not None and user_input != "":
st.write(user_template.replace("{{MSG}}",user_input), unsafe_allow_html= True)
with st.spinner("Processing..."):
response = agent.run(user_input)
st.write(bot_template.replace("{{MSG}}",response), unsafe_allow_html= True)
if __name__ == "__main__":
main() |