Create Main.py

Main.py

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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.")
+
+# Upload, download, main ==> project
+# Preview , Data cleaning Modify ==> Preprocessing1
+# Data , Describe, info ==> Information
+# Handle, Missing ==> Preprocessing2
+
+
+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()