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# app.py
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import io
from PIL import Image
# 🔹 Main Cleaning Function
def clean_csv(file_path):
if file_path is None:
return "❌ Please upload a CSV.", None, None, None
try:
# Load CSV
df = pd.read_csv(file_path)
# --- Store original stats ---
before_shape = df.shape
before_missing = df.isnull().sum().sum()
before_duplicates = df.duplicated().sum()
# --- Cleaning steps ---
df = df.drop_duplicates() # Remove duplicates
df = df.fillna(df.mean(numeric_only=True)) # Fill numeric NaN
df = df.apply(lambda x: x.fillna(x.mode()[0]) if x.isnull().any() else x) # Fill categorical NaN
# --- Outlier removal (z-score method) ---
for col in df.select_dtypes(include="number").columns:
mean, std = df[col].mean(), df[col].std()
if std > 0: # avoid divide by zero
df = df[(df[col] >= mean - 3*std) & (df[col] <= mean + 3*std)]
# --- Store after stats ---
after_shape = df.shape
after_missing = df.isnull().sum().sum()
after_duplicates = df.duplicated().sum()
# --- Heatmap for missing values (before cleaning) ---
plt.figure(figsize=(6,4))
sns.heatmap(pd.read_csv(file_path).isnull(), cbar=False, cmap="viridis")
plt.title("Missing Values Heatmap (Before Cleaning)")
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
plt.close()
# ✅ Convert buffer to PIL Image
heatmap_img = Image.open(buf)
# --- Save cleaned CSV ---
cleaned_path = "cleaned_data.csv"
df.to_csv(cleaned_path, index=False)
# --- Report ---
report = f"""
## 🧹 Data Cleaning Report
**Before Cleaning**
- Shape: {before_shape}
- Missing values: {before_missing}
- Duplicates: {before_duplicates}
**After Cleaning**
- Shape: {after_shape}
- Missing values: {after_missing}
- Duplicates: {after_duplicates}
"""
# --- Preview (first 5 rows) ---
preview_html = df.head(5).to_html(index=False)
return report, heatmap_img, cleaned_path, preview_html
except Exception as e:
return f"⚠️ Error: {e}", None, None, None
# 🎨 Gradio UI
with gr.Blocks(theme="soft") as demo:
gr.Markdown("## 🧹 AI Data Cleaner\nUpload a CSV → Clean it → Get Report + Heatmap + Download")
file_input = gr.File(label="📂 Upload CSV", file_types=[".csv"], type="filepath")
clean_btn = gr.Button("🚀 Clean Data")
with gr.Row():
report_output = gr.Markdown()
heatmap_output = gr.Image(type="pil", label="Missing Values Heatmap")
with gr.Row():
download_output = gr.File(label="⬇️ Download Cleaned CSV")
preview_output = gr.HTML(label="🔍 Preview (First 5 Rows)")
clean_btn.click(fn=clean_csv,
inputs=file_input,
outputs=[report_output, heatmap_output, download_output, preview_output])
# 🚀 Launch (Hugging Face Spaces will auto-run this)
demo.launch()
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