import streamlit as st
import pandas as pd
import plotly.express as px
from ydata_profiling import ProfileReport
from statsmodels.stats.outliers_influence import variance_inflation_factor

# 1. Set Page Configuration
st.set_page_config(
    page_title="Enhanced Data Profiling",
    layout="wide",
    page_icon="📊"
)

# 2. Custom CSS for a Clean, White UI
custom_css = """
<style>
/* Make the entire background white */
body {
    background-color: #ffffff !important;
    font-family: 'Roboto', sans-serif;
}

/* Headers and titles */
h1, h2, h3, h4 {
    color: #2c3e50;
    font-weight: 700;
}

/* The main Streamlit container */
[data-testid="stAppViewContainer"] {
    background-color: #ffffff !important;
}

/* Individual content containers */
.css-1d391kg, .css-hxt7ib {
    background-color: #ffffff !important;
    border-radius: 15px;
    padding: 30px;
    margin-bottom: 20px;
    box-shadow: 0 8px 16px rgba(0,0,0,0.1);
}

/* Sidebar styling */
[data-testid="stSidebar"] {
    background-color: #34495e !important;
    color: #ecf0f1 !important;
    font-size: 16px;
}
[data-testid="stSidebar"] .css-1d391kg {
    background-color: #2c3e50 !important;
    border-radius: 10px;
}
</style>
"""
st.markdown(custom_css, unsafe_allow_html=True)

# 3. Title and Description
st.title("Enhanced Data Profiling")
st.markdown("<h4 style='text-align: center; color: #2c3e50;'>Upload your CSV and explore it thoroughly!</h4>", unsafe_allow_html=True)

# 4. Sidebar for File Upload
st.sidebar.header("Upload & Options")
uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type="csv")

# Placeholder for the DataFrame
df = None

if uploaded_file is not None:
    # 4a. Read the CSV
    df = pd.read_csv(uploaded_file)
    st.success("File uploaded successfully!")

    # 5. KPI Metrics / Quick Summary
    st.subheader("Dataset Quick Summary")
    col1, col2, col3, col4 = st.columns(4)
    col1.metric("Rows", f"{df.shape[0]}")
    col2.metric("Columns", f"{df.shape[1]}")
    missing_percentage = (df.isnull().sum().sum() / df.size) * 100
    col3.metric("Missing %", f"{missing_percentage:.2f}%")
    duplicates = df.duplicated().sum()
    col4.metric("Duplicates", f"{duplicates}")

    st.write("---")

    # 6. Optional Data Transformation: Drop columns with > 50% missing
    if st.checkbox("Drop columns with > 50% missing data?"):
        threshold = df.shape[0] * 0.5
        before_cols = df.shape[1]
        df = df.loc[:, df.isnull().sum() < threshold]
        after_cols = df.shape[1]
        st.success(f"Dropped {before_cols - after_cols} columns. Remaining columns: {after_cols}")

    # 7. Optional Quick Histogram
    numeric_cols = df.select_dtypes(include="number").columns.tolist()
    if numeric_cols:
        st.subheader("Optional Quick Histogram")
        selected_col = st.selectbox("Select a numeric column", numeric_cols)
        if selected_col:
            fig_hist = px.histogram(df, x=selected_col, nbins=50, title=f"Histogram of {selected_col}")
            fig_hist.update_traces(opacity=0.8)
            st.plotly_chart(fig_hist, use_container_width=True)

    # 8. Generate ydata-profiling Report
    st.subheader("Comprehensive Profiling Report")
    with st.spinner("Generating profiling report..."):
        profile = ProfileReport(df, title="Profiling Report", explorative=True)
        report_html = profile.to_html()

    # 8a. Display the report in an iframe
    st.components.v1.html(report_html, height=1200, scrolling=True)

    # 8b. Download Button for HTML
    st.write("### Download the Profiling Report")
    st.download_button(
        label="Download HTML",
        data=report_html.encode('utf-8'),
        file_name="profiling_report.html",
        mime="text/html"
    )
else:
    st.info("Awaiting CSV file upload.")

# That's it! 
# Simply copy and paste this into your app.py on Hugging Face Spaces.
# Make sure you have a requirements.txt that includes:
#   streamlit
#   pandas
#   ydata-profiling
#   plotly
#   statsmodels (for VIF, if you need it)