Create app.py

app.py

@@ -0,0 +1,229 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import accuracy_score
+
+from sklearn.ensemble import RandomForestClassifier
+
+from fairlearn.metrics import MetricFrame, selection_rate, demographic_parity_difference
+import shap
+import matplotlib.pyplot as plt
+
+# -----------------------------
+# Core training + metrics logic
+# -----------------------------
+def train_and_evaluate(csv_file, target_col, sensitive_col):
+    if csv_file is None:
+        return "Please upload a CSV.", None, None, None
+
+    # Load data
+    df = pd.read_csv(csv_file.name)
+
+    # Basic validation
+    if target_col not in df.columns:
+        return f"Target column '{target_col}' not found in CSV.", None, None, None
+    if sensitive_col not in df.columns:
+        return f"Sensitive column '{sensitive_col}' not found in CSV.", None, None, None
+
+    # Drop rows with missing target
+    df = df.dropna(subset=[target_col])
+
+    # Separate features/target
+    y = df[target_col]
+    X = df.drop(columns=[target_col])
+
+    # Keep a copy of sensitive feature before encoding
+    sensitive_series = df[sensitive_col]
+
+    # Identify numeric vs categorical
+    numeric_cols = X.select_dtypes(include=["int64", "float64"]).columns.tolist()
+    categorical_cols = [c for c in X.columns if c not in numeric_cols]
+
+    # Preprocess
+    numeric_transformer = "passthrough"
+    categorical_transformer = OneHotEncoder(handle_unknown="ignore")
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_cols),
+            ("cat", categorical_transformer, categorical_cols),
+        ]
+    )
+
+    # Model
+    model = RandomForestClassifier(
+        n_estimators=100,
+        random_state=42
+    )
+
+    clf = Pipeline(
+        steps=[
+            ("preprocessor", preprocessor),
+            ("model", model),
+        ]
+    )
+
+    # Train/test split
+    X_train, X_test, y_train, y_test, sens_train, sens_test = train_test_split(
+        X, y, sensitive_series, test_size=0.3, random_state=42, stratify=y
+    )
+
+    # Fit
+    clf.fit(X_train, y_train)
+
+    # Predictions
+    y_pred = clf.predict(X_test)
+
+    # -----------------
+    # Standard accuracy
+    # -----------------
+    acc = accuracy_score(y_test, y_pred)
+
+    # -------------------------
+    # Fairlearn: Demographic Parity
+    # -------------------------
+    # selection_rate expects y_pred and sensitive features
+    mf = MetricFrame(
+        metrics=selection_rate,
+        y_true=y_test,
+        y_pred=y_pred,
+        sensitive_features=sens_test
+    )
+
+    # Overall selection rate by group
+    group_selection_rates = mf.by_group
+
+    # Demographic parity difference
+    dp_diff = demographic_parity_difference(
+        y_true=y_test,
+        y_pred=y_pred,
+        sensitive_features=sens_test
+    )
+
+    # Governance threshold example
+    governance_threshold = 0.10
+    policy_status = (
+        "Blocked: Demographic parity difference exceeds threshold."
+        if abs(dp_diff) > governance_threshold
+        else "Allowed: Within governance threshold."
+    )
+
+    # -----------------
+    # SHAP explanation
+    # -----------------
+    # Extract trained model and transformed data for SHAP
+    # We use a small sample for speed
+    X_test_sample = X_test.sample(min(200, len(X_test)), random_state=42)
+
+    # Fit a separate preprocessing-only transform to get numeric matrix
+    X_test_transformed = clf.named_steps["preprocessor"].transform(X_test_sample)
+    rf_model = clf.named_steps["model"]
+
+    # SHAP for tree models
+    explainer = shap.TreeExplainer(rf_model)
+    shap_values = explainer.shap_values(X_test_transformed)
+
+    # Get feature names after preprocessing
+    # numeric + one-hot categories
+    feature_names = []
+    feature_names.extend(numeric_cols)
+
+    if categorical_cols:
+        ohe = clf.named_steps["preprocessor"].named_transformers_["cat"]
+        ohe_feature_names = ohe.get_feature_names_out(categorical_cols).tolist()
+        feature_names.extend(ohe_feature_names)
+
+    # Summary plot (global importance)
+    plt.figure(figsize=(8, 6))
+    shap.summary_plot(
+        shap_values[1] if isinstance(shap_values, list) else shap_values,
+        X_test_transformed,
+        feature_names=feature_names,
+        show=False
+    )
+    plt.tight_layout()
+    shap_plot_path = "shap_summary.png"
+    plt.savefig(shap_plot_path, dpi=120)
+    plt.close()
+
+    # -----------------
+    # Build text outputs
+    # -----------------
+    metrics_text = []
+    metrics_text.append(f"Accuracy: {acc:.3f}")
+    metrics_text.append("")
+    metrics_text.append("Selection rate by sensitive group:")
+    metrics_text.append(str(group_selection_rates))
+    metrics_text.append("")
+    metrics_text.append(f"Demographic Parity Difference: {dp_diff:.3f}")
+    metrics_text.append(f"Governance Threshold: {governance_threshold:.3f}")
+    metrics_text.append(f"Policy Status: {policy_status}")
+
+    metrics_text = "\n".join(metrics_text)
+
+    # Also return a small table of group metrics as HTML
+    group_df = group_selection_rates.reset_index()
+    group_df.columns = [sensitive_col, "selection_rate"]
+    group_html = group_df.to_html(index=False)
+
+    return metrics_text, group_html, shap_plot_path, df.head().to_html(index=False)
+
+
+# -----------------------------
+# Gradio interface
+# -----------------------------
+def get_columns(csv_file):
+    if csv_file is None:
+        return gr.update(choices=[]), gr.update(choices=[])
+    df = pd.read_csv(csv_file.name)
+    cols = df.columns.tolist()
+    return gr.update(choices=cols, value=cols[-1]), gr.update(choices=cols, value=cols[0])
+
+
+with gr.Blocks(title="AI Governance Lab - CSV + Fairness + SHAP") as demo:
+    gr.Markdown("# 🧭 AI Governance Lab\nUpload a CSV, pick target and sensitive columns, train, and inspect fairness + SHAP.")
+
+    with gr.Row():
+        csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
+
+    with gr.Row():
+        target_dropdown = gr.Dropdown(
+            label="Target column (label)",
+            choices=[],
+            interactive=True
+        )
+        sensitive_dropdown = gr.Dropdown(
+            label="Sensitive attribute column (e.g., sex, race)",
+            choices=[],
+            interactive=True
+        )
+
+    csv_input.change(
+        fn=get_columns,
+        inputs=csv_input,
+        outputs=[target_dropdown, sensitive_dropdown]
+    )
+
+    run_button = gr.Button("Train & Evaluate")
+
+    metrics_output = gr.Textbox(
+        label="Model & Fairness Metrics",
+        lines=12
+    )
+    group_table_output = gr.HTML(label="Group Selection Rates")
+    shap_image_output = gr.Image(label="SHAP Summary Plot")
+    preview_output = gr.HTML(label="Data Preview (first 5 rows)")
+
+    run_button.click(
+        fn=train_and_evaluate,
+        inputs=[csv_input, target_dropdown, sensitive_dropdown],
+        outputs=[metrics_output, group_table_output, shap_image_output, preview_output]
+    )
+
+demo.launch()
+