Create app.py

app.py

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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()