Update app.py

app.py

@@ -1,41 +1,130 @@
 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
 
-st.set_page_config(page_title="Dynamic Data Profiling", layout="wide", page_icon="📊")
+# 1. Set Page Configuration
+st.set_page_config(
+    page_title="Enhanced Data Profiling",
+    layout="wide",
+    page_icon="📊"
+)
 
-st.title("Dynamic Data Profiling with ydata-profiling")
-st.write("Upload your CSV file and get a complete interactive profiling report!")
+# 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;
+}
 
-uploaded_file = st.file_uploader("Upload a CSV file", type="csv")
+/* 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:
-    try:
-        # Read CSV into DataFrame
-        df = pd.read_csv(uploaded_file)
-        st.success("File uploaded successfully!")
-        
-        # Generate the profile report
-        with st.spinner("Generating profile report..."):
-            profile = ProfileReport(df, title="Profiling Report", explorative=True)
-            # Convert report to HTML
-            report_html = profile.to_html()
-
-        # Show the report in an iframe
-        st.components.v1.html(report_html, height=1200, scrolling=True)
-
-        # Provide a download button for the HTML
-        st.write("### Download the Profiling Report")
-        # Convert HTML string to bytes
-        report_bytes = report_html.encode('utf-8')
-        st.download_button(
-            label="Download HTML",
-            data=report_bytes,
-            file_name="profiling_report.html",
-            mime="text/html"
-        )
-
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
+    # 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)