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| import os | |
| import sys | |
| from pathlib import Path | |
| PROJECT_ROOT = Path(__file__).resolve().parent | |
| SRC_DIR = PROJECT_ROOT / "src" | |
| if str(SRC_DIR) not in sys.path: | |
| sys.path.insert(0, str(SRC_DIR)) | |
| import gradio as gr | |
| import pandas as pd | |
| import xgboost as xgb | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score, roc_auc_score | |
| from my_useful_tool import HighAccuracyMLPrep | |
| def process_and_train(file_obj, target_column, max_features): | |
| if file_obj is None: | |
| return "Please upload a CSV file.", "" | |
| file_path = file_obj.name | |
| # 1. Run Data Prep Engine | |
| processor = HighAccuracyMLPrep(task_type="classification", max_features=int(max_features)) | |
| X, y, diagnostics = processor.fit_transform(file_path=file_path, target_column=target_column) | |
| # 2. Train Validation XGBoost Model | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| params = diagnostics.get("recommended_hyperparameters", {}) | |
| params["objective"] = "binary:logistic" | |
| params["eval_metric"] = "logloss" | |
| model = xgb.XGBClassifier(**params) | |
| model.fit(X_train, y_train) | |
| y_pred = model.predict(X_test) | |
| y_proba = model.predict_proba(X_test)[:, 1] if hasattr(model, "predict_proba") else y_pred | |
| acc = accuracy_score(y_test, y_pred) | |
| try: | |
| auc = roc_auc_score(y_test, y_proba) | |
| auc_str = f"{auc:.4f}" | |
| except Exception: | |
| auc_str = "N/A" | |
| # 3. Output Processed Data | |
| processed_df = pd.DataFrame(X) | |
| processed_df["target"] = y | |
| output_path = "processed_dataset.csv" | |
| processed_df.to_csv(output_path, index=False) | |
| report = f""" | |
| ### 🚀 Processing Complete! | |
| - **Original Shape:** {pd.read_csv(file_path).shape} | |
| - **Processed Matrix:** {X.shape} | |
| - **Selected Features:** {len(diagnostics.get('selected_features', []))} | |
| ### 📊 Validation Results | |
| - **Accuracy:** {acc * 100:.2f}% | |
| - **ROC-AUC Score:** {auc_str} | |
| ### ⚙️ Recommended Hyperparameters | |
| ```json | |
| {diagnostics.get('recommended_hyperparameters', {})} | |
| ``` | |
| """ | |
| return report, output_path | |
| # Gradio Interface Setup | |
| with gr.Blocks(title="HighAccuracyMLPrep") as demo: | |
| gr.Markdown("# ⚡ HighAccuracyMLPrep Engine") | |
| gr.Markdown("Upload a raw CSV dataset to automatically clean, handle missing values, prune features, and extract model hyperparameters.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| file_input = gr.File(label="Upload Raw CSV Dataset", file_types=[".csv"]) | |
| target_col = gr.Textbox(label="Target Column Name", value="Churned") | |
| max_feats = gr.Slider(minimum=5, maximum=50, value=20, step=1, label="Max Features") | |
| submit_btn = gr.Button("Process Dataset & Train Model", variant="primary") | |
| with gr.Column(): | |
| status_output = gr.Markdown(label="Diagnostics Report") | |
| file_output = gr.Textbox(label="Processed CSV Path", interactive=False) | |
| submit_btn.click( | |
| fn=process_and_train, | |
| inputs=[file_input, target_col, max_feats], | |
| outputs=[status_output, file_output] | |
| ) | |
| app = demo | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860, share=True) |