pages/1_Upload_the_data.py

import streamlit as st
import pandas as pd

st.markdown("""
    <div style="text-align: center; margin-bottom: 20px;">
        <h2 style="color: #c71585; font-size: 28px;">Upload Your Classification Dataset📂</h2>
        <p style="font-size: 18px; color: #4F4F4F;">
            Upload your dataset in CSV, Excel, JSON, XML, or HTML format. Drag and drop your file or click to browse.
        </p>
    </div>
""", unsafe_allow_html=True)

# Upload the file 
uploaded_file = st.file_uploader(
    "Drop your dataset here or click to upload",
    type=["csv", "xlsx", "json", "xml", "html"]
)

if uploaded_file:
    file_extension = uploaded_file.name.split(".")[-1]

    try:
        if file_extension == "csv":
            df = pd.read_csv(uploaded_file)
        elif file_extension == "xlsx":
            df = pd.read_excel(uploaded_file)
        elif file_extension == "json":
            df = pd.read_json(uploaded_file)
        elif file_extension == "xml":
            df = pd.read_xml(uploaded_file)
        elif file_extension == "html":
            df = pd.read_html(uploaded_file)[0]
        else:
            st.error("❌ Unsupported file format!")
            st.stop()

        st.session_state.df = df 

        st.success(f"{uploaded_file.name} uploaded successfully!✅")

        # if the data upload Successfully Display the Data
        st.dataframe(df.head()) 

        st.markdown("#### You're All Set! What’s Next?📌")
        with st.expander("Simple EDA🔍"):
                st.info("Simple EDA helps in understanding the dataset structure, ensuring data quality, and avoiding errors in modeling.")
                if st.button("➡️Proceed to Simple EDA"):
                    st.switch_page("pages/2_Simple_EDA.py")


    except Exception as e:
        st.error(f"⚠️ Error loading the file: {e}")