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import streamlit as st
import pandas as pd
import numpy as np
import io
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
import seaborn as sns
import base64
import json
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA
from Information import show_general_data_statistics, describe_data, info_data
from Preprocessing1 import preview_data, data_cleaning, modify_column_names
from Preprocessing2 import handle_categorical_values, missing_values, handle_duplicates, handle_outliers
from RAG import create_doucment, ask_me, load_models_embedding, load_models_llm, create_database
def upload_data():
st.title("Upload Dataset")
file = st.file_uploader("Upload your dataset", type=[
"csv", "xlsx"], key="file_uploader_1")
if file:
try:
if file.name.endswith(".csv"):
data = pd.read_csv(file)
elif file.name.endswith(".xlsx"):
data = pd.read_excel(file)
st.session_state["data"] = data
st.success("Dataset uploaded successfully!")
except Exception as e:
st.error(f"Error loading file: {e}")
return file
def download_data():
"""Downloads the DataFrame as a CSV file."""
if "data" in st.session_state and not st.session_state["data"].empty:
csv = st.session_state["data"].to_csv(index=False).encode('utf-8')
st.download_button(
label="Download Cleaned Dataset",
data=csv,
file_name="cleaned_data.csv",
mime="text/csv"
)
else:
st.warning(
"No data available to download. Please modify or upload a dataset first.")
def rag_chatbot():
st.title("RAG Chatbot")
# Check if data is uploaded
if "data" in st.session_state and isinstance(st.session_state["data"], pd.DataFrame):
df = st.session_state["data"]
# Convert data to documents
st.write("Processing the dataset...")
documents = create_doucment(df)
st.write(f"Created {len(documents)} documents.")
# Load models
st.write("Loading models...")
embedding = load_models_embedding()
llm = load_models_llm()
# Create retriever
retriever = create_database(embedding, documents).as_retriever()
# Ask a question
question = st.text_input("Ask a question about your dataset:")
if question:
response = ask_me(question, retriever, llm)
st.write(f"Answer: {response}")
else:
st.warning("Please upload a dataset to proceed.")
def main():
st.sidebar.title("Navigation")
options = st.sidebar.radio(
"Go to",
[
"Upload",
"Preview",
"Data Cleaning",
"Modify Column Names",
"General Data Statistics",
"Describe",
"Info",
"Handle Categorical",
"Missing Values",
"Handle Duplicates",
"Handle Outliers",
"Download",
"RAG Chatbot"
],
key="unique_navigation_key",
)
if options == "Upload":
upload_data()
elif options == "Preview":
preview_data()
elif options == "Data Cleaning":
data_cleaning()
elif options == "Modify Column Names":
modify_column_names()
elif options == "General Data Statistics":
show_general_data_statistics()
elif options == "Describe":
describe_data()
elif options == "Info":
info_data()
elif options == "Handle Categorical":
handle_categorical_values()
elif options == "Missing Values":
missing_values()
elif options == "Handle Duplicates":
handle_duplicates()
elif options == "Handle Outliers":
handle_outliers()
elif options == "Download":
download_data()
elif options == "RAG Chatbot":
rag_chatbot()
else:
st.warning("Please upload a dataset first.")
if __name__ == "__main__":
main()
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