Upload Gradio credit risk dashboard

README.md

@@ -1,13 +1,18 @@
----
-title: Credit Model
-emoji: 🐠
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
-short_description: credit model
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Credit Risk XAI Dashboard (Gradio)
+
+This Space hosts a **polished mock-dashboard** for a credit risk model.
+
+## Files you must upload
+- `app.py`
+- `requirements.txt`
+- `xgb_credit_model.pkl` (your saved model)
+- `german_credit_data.csv` (dataset used for input ranges + fairness + DiCE)
+
+## Run locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Notes
+- SHAP and DiCE are optional. If they fail, the app will show an error message in the tables.

app.py

@@ -0,0 +1,446 @@
+import os
+import joblib
+import pandas as pd
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# Optional deps
+try:
+    import shap
+    SHAP_OK = True
+except Exception:
+    shap = None
+    SHAP_OK = False
+
+try:
+    import dice_ml
+    from dice_ml import Dice
+    DICE_OK = True
+except Exception:
+    dice_ml = None
+    Dice = None
+    DICE_OK = False
+
+# -----------------------------
+# Feature name mapping (DE -> EN)
+# -----------------------------
+FEATURE_NAME_MAP = {
+    "laufkont": "Checking account status",
+    "laufzeit": "Loan duration (months)",
+    "moral": "Credit history",
+    "verw": "Loan purpose",
+    "hoehe": "Credit amount",
+    "sparkont": "Savings account status",
+    "beszeit": "Employment duration",
+    "rate": "Installment rate",
+    "famges": "Personal status / sex",
+    "buerge": "Other debtors / guarantors",
+    "wohnzeit": "Present residence (years)",
+    "verm": "Property",
+    "alter": "Age",
+    "weitkred": "Other installment plans",
+    "wohn": "Housing",
+    "bishkred": "Number of existing credits",
+    "beruf": "Job",
+    "pers": "Number of people liable",
+    "telef": "Telephone",
+    "gastarb": "Foreign worker",
+}
+
+# Your label column (change if needed)
+LABEL_CANDIDATES = ["label", "target", "class", "risk", "credit_risk"]
+
+MODEL_PATH_ENV = os.getenv("MODEL_PATH")
+DATA_PATH = os.getenv("DATA_PATH", "german_credit_data.csv")
+
+POSSIBLE_MODEL_FILES = [
+    MODEL_PATH_ENV,
+    "xgb_credit_model.pkl",
+    "xgb_credit_model.pickle",
+    "model.pkl",
+]
+POSSIBLE_MODEL_FILES = [p for p in POSSIBLE_MODEL_FILES if p]
+
+
+def pretty_name(col: str) -> str:
+    return FEATURE_NAME_MAP.get(col, col)
+
+
+def load_model():
+    for p in POSSIBLE_MODEL_FILES:
+        if os.path.exists(p):
+            return joblib.load(p), p
+    raise FileNotFoundError(
+        "Model file not found. Upload xgb_credit_model.pkl to the Space (root folder), "
+        "or set MODEL_PATH env var."
+    )
+
+
+def load_data():
+    if os.path.exists(DATA_PATH):
+        return pd.read_csv(DATA_PATH)
+    return None
+
+
+model, model_loaded_from = load_model()
+df = load_data()
+
+# Infer features
+if df is not None:
+    label_col = next((c for c in LABEL_CANDIDATES if c in df.columns), None)
+    feature_cols = [c for c in df.columns if c != label_col]
+else:
+    feature_cols = list(getattr(model, "feature_names_in_", []))
+
+# Build input metadata from df
+col_minmax = {}
+col_options = {}
+if df is not None and feature_cols:
+    for c in feature_cols:
+        s = df[c]
+        # If few unique values, show dropdown even if numeric
+        uniq = s.dropna().unique()
+        if len(uniq) <= 8:
+            col_options[c] = sorted(uniq.tolist())
+        elif pd.api.types.is_numeric_dtype(s):
+            col_minmax[c] = (float(s.min()), float(s.max()), float(s.median()))
+        else:
+            col_options[c] = sorted(s.dropna().unique().tolist())
+
+
+def predict_proba(input_row: pd.DataFrame) -> float:
+    if hasattr(model, "predict_proba"):
+        return float(model.predict_proba(input_row)[0, 1])
+    return float(model.predict(input_row)[0])
+
+
+def decision_from_threshold(prob: float, threshold: float) -> str:
+    return "Risky ❌" if prob >= threshold else "Good ✅"
+
+
+def make_input_row(values: dict) -> pd.DataFrame:
+    row = {c: values.get(c) for c in feature_cols}
+    return pd.DataFrame([row])
+
+
+def local_shap_explain(input_row: pd.DataFrame, top_k: int = 8):
+    if not SHAP_OK:
+        return None, pd.DataFrame({"message": ["SHAP not installed."]})
+
+    try:
+        explainer = shap.TreeExplainer(model)
+        shap_values = explainer.shap_values(input_row)
+
+        # Handle binary output formats
+        if isinstance(shap_values, list):
+            sv = shap_values[1]
+        else:
+            sv = shap_values
+
+        sv = np.array(sv).reshape(-1)
+        cols = list(input_row.columns)
+        df_shap = pd.DataFrame({
+            "Feature": [pretty_name(c) for c in cols],
+            "SHAP value": sv,
+            "Input value": input_row.iloc[0].values,
+        })
+        df_shap["abs"] = df_shap["SHAP value"].abs()
+        df_shap = df_shap.sort_values("abs", ascending=False).drop(columns=["abs"]).head(top_k)
+
+        # Plot
+        fig = plt.figure(figsize=(7, 4))
+        y = df_shap["Feature"].tolist()[::-1]
+        x = df_shap["SHAP value"].tolist()[::-1]
+        plt.barh(y, x)
+        plt.title("Top local SHAP factors")
+        plt.xlabel("SHAP contribution")
+        plt.tight_layout()
+        return fig, df_shap.reset_index(drop=True)
+    except Exception as e:
+        return None, pd.DataFrame({"message": [f"SHAP failed: {e}"]})
+
+
+def generate_adverse_action(df_shap: pd.DataFrame, decision: str) -> str:
+    if "SHAP value" not in df_shap.columns:
+        return ""
+    if "Risky" not in decision:
+        return "✅ Applicant is approved under the current policy."
+
+    # Positive SHAP values push towards Risky
+    pos = df_shap.sort_values("SHAP value", ascending=False).head(3)
+    reasons = ", ".join(pos["Feature"].astype(str).tolist())
+    return (
+        "Adverse Action Notice (summary):\n"
+        f"Your application is likely to be declined because the following factors contributed most to the risk score: {reasons}.\n"
+        "You can view actionable suggestions in the Recourse tab."
+    )
+
+
+def dice_counterfactuals(input_row: pd.DataFrame, total_CFs: int = 3):
+    if not DICE_OK:
+        return pd.DataFrame({"message": ["DiCE not installed."]})
+    if df is None:
+        return pd.DataFrame({"message": ["Upload german_credit_data.csv to enable DiCE recourse."]})
+
+    try:
+        label_col = next((c for c in LABEL_CANDIDATES if c in df.columns), None)
+        # If label missing, we still can create Data object without outcome
+        outcome_name = label_col if label_col else None
+
+        data_dice = dice_ml.Data(
+            dataframe=df if label_col else df.assign(_dummy_outcome=0),
+            continuous_features=[c for c in feature_cols if c in col_minmax],
+            outcome_name=label_col if label_col else "_dummy_outcome",
+        )
+
+        model_dice = dice_ml.Model(model=model, backend="sklearn")
+        dice = Dice(data_dice, model_dice, method="random")
+
+        # We want the opposite class (Good = 0, Risky = 1). If current is risky, aim for good.
+        current_prob = predict_proba(input_row)
+        current_class = 1 if current_prob >= 0.5 else 0
+        desired_class = 0 if current_class == 1 else 1
+
+        cf = dice.generate_counterfactuals(input_row, total_CFs=total_CFs, desired_class=desired_class)
+        cf_df = cf.cf_examples_list[0].final_cfs_df.copy()
+
+        # Add predicted risk on CFs
+        if hasattr(model, "predict_proba"):
+            cf_df["risk_probability"] = model.predict_proba(cf_df[feature_cols])[:, 1]
+        else:
+            cf_df["risk_probability"] = model.predict(cf_df[feature_cols])
+
+        # Rename columns for display
+        display_df = cf_df[[*feature_cols, "risk_probability"]].copy()
+        display_df = display_df.rename(columns={c: pretty_name(c) for c in feature_cols})
+        return display_df
+
+    except Exception as e:
+        return pd.DataFrame({"message": [f"DiCE failed: {e}"]})
+
+
+def fairness_snapshot(threshold: float):
+    if df is None:
+        return pd.DataFrame({"message": ["Upload german_credit_data.csv to enable fairness snapshot."]})
+
+    try:
+        X = df[feature_cols].copy()
+        probs = model.predict_proba(X)[:, 1] if hasattr(model, "predict_proba") else model.predict(X)
+        decision = (probs < threshold).astype(int)  # 1=approved
+
+        out_rows = []
+
+        # Age groups if present
+        if "alter" in feature_cols:
+            age = df["alter"].astype(float)
+            bins = pd.cut(age, bins=[0, 25, 35, 45, 55, 100], right=True)
+            grp = pd.DataFrame({"group": bins.astype(str), "approved": decision})
+            rate = grp.groupby("group")["approved"].mean().reset_index()
+            rate["metric"] = "Approval rate by Age group"
+            out_rows.append(rate)
+
+        # Job if present
+        if "beruf" in feature_cols:
+            grp = pd.DataFrame({"group": df["beruf"].astype(str), "approved": decision})
+            rate = grp.groupby("group")["approved"].mean().reset_index()
+            rate["metric"] = "Approval rate by Job"
+            out_rows.append(rate)
+
+        if not out_rows:
+            return pd.DataFrame({"message": ["No fairness-sensitive columns found (alter/beruf missing)."]})
+
+        out = pd.concat(out_rows, ignore_index=True)
+        out["approved"] = (out["approved"] * 100).round(1).astype(str) + "%"
+        out = out.rename(columns={"group": "Group", "approved": "Approval rate"})
+        return out[["metric", "Group", "Approval rate"]]
+
+    except Exception as e:
+        return pd.DataFrame({"message": [f"Fairness snapshot failed: {e}"]})
+
+
+# -----------------------------
+# UI helpers
+# -----------------------------
+CARD_CSS = """
+<style>
+  .card {border: 1px solid rgba(0,0,0,0.08); border-radius: 16px; padding: 14px 16px; background: rgba(255,255,255,0.75);}
+  .metric {font-size: 28px; font-weight: 700; margin: 0;}
+  .label {font-size: 12px; opacity: 0.75; margin: 0;}
+  .subtle {font-size: 13px; opacity: 0.8;}
+</style>
+"""
+
+
+def build_inputs():
+    components = {}
+
+    for c in feature_cols:
+        label = pretty_name(c)
+
+        if c in col_options:
+            components[c] = gr.Dropdown(
+                choices=col_options[c],
+                value=col_options[c][0] if len(col_options[c]) else None,
+                label=label,
+                interactive=True,
+            )
+        elif c in col_minmax:
+            mn, mx, med = col_minmax[c]
+            # Friendly step for sliders
+            step = 1 if float(mn).is_integer() and float(mx).is_integer() else 0.1
+            components[c] = gr.Slider(
+                minimum=mn,
+                maximum=mx,
+                value=med,
+                step=step,
+                label=label,
+                interactive=True,
+            )
+        else:
+            components[c] = gr.Number(value=0.0, label=label, interactive=True)
+
+    return components
+
+
+inputs = build_inputs()
+
+
+def run_dashboard(threshold: float, **kwargs):
+    input_row = make_input_row(kwargs)
+
+    prob = predict_proba(input_row)
+    decision = decision_from_threshold(prob, threshold)
+
+    # SHAP
+    shap_fig, shap_table = local_shap_explain(input_row)
+    adverse = generate_adverse_action(shap_table, decision)
+
+    # DiCE
+    cf_table = dice_counterfactuals(input_row, total_CFs=3)
+
+    # Fairness
+    fair_table = fairness_snapshot(threshold)
+
+    # Pretty metrics
+    prob_pct = f"{prob*100:.1f}%"
+    return (
+        decision,
+        prob_pct,
+        shap_fig,
+        shap_table,
+        adverse,
+        cf_table,
+        fair_table,
+    )
+
+
+with gr.Blocks(theme=gr.themes.Soft(), title="Credit Risk XAI Dashboard") as demo:
+    gr.HTML(CARD_CSS)
+
+    gr.Markdown(
+        "# Credit Risk Explainable AI Dashboard\n"
+        "A polished mock-dashboard (Gradio) for **prediction + SHAP explanations + DiCE recourse + fairness snapshot**."
+    )
+    gr.Markdown(f"**Model loaded from:** `{model_loaded_from}`")
+
+    with gr.Tab("Applicant Input"):
+        gr.Markdown("### Enter applicant features")
+        with gr.Row():
+            with gr.Column(scale=2):
+                # Put inputs in two columns for better UX
+                left_cols = feature_cols[: len(feature_cols)//2]
+                right_cols = feature_cols[len(feature_cols)//2 :]
+                with gr.Row():
+                    with gr.Column():
+                        for c in left_cols:
+                            inputs[c].render()
+                    with gr.Column():
+                        for c in right_cols:
+                            inputs[c].render()
+
+            with gr.Column(scale=1):
+                gr.Markdown("### Decision Policy")
+                threshold = gr.Slider(0.1, 0.9, value=0.6, step=0.01, label="Decision threshold")
+                gr.Markdown(
+                    "- Higher threshold → fewer **Risky** decisions\n"
+                    "- Lower threshold → stricter approval policy"
+                )
+
+                run_btn = gr.Button("Run Dashboard", variant="primary")
+
+    with gr.Tab("Prediction"):
+        with gr.Row():
+            decision_out = gr.Markdown("")
+        with gr.Row():
+            with gr.Column():
+                prob_out = gr.Markdown("")
+            with gr.Column():
+                gr.Markdown("### Notes")
+                gr.Markdown(
+                    "This section summarizes the model output under the selected policy (threshold)."
+                )
+
+        gr.Markdown("---")
+        adverse_out = gr.Textbox(label="Adverse Action Notice (auto-summary)", lines=5)
+
+    with gr.Tab("Explain (SHAP)"):
+        if SHAP_OK:
+            gr.Markdown("### Local Explanation (Top SHAP factors)")
+            shap_plot = gr.Plot(label="SHAP local bar plot")
+            shap_df = gr.Dataframe(label="Top SHAP table", interactive=False)
+            gr.Markdown(
+                "**How to read it:** positive SHAP values push the prediction towards *Risky*, and negative values push towards *Good*."
+            )
+        else:
+            shap_plot = gr.Plot(label="SHAP plot")
+            shap_df = gr.Dataframe(label="SHAP table")
+            gr.Markdown("SHAP is not installed. Add `shap` to requirements.txt.")
+
+    with gr.Tab("Recourse (DiCE)"):
+        gr.Markdown("### Actionable Counterfactuals (DiCE)")
+        cf_df = gr.Dataframe(label="Counterfactual suggestions", interactive=False)
+        gr.Markdown(
+            "These are minimal changes to the applicant profile that could flip the decision to the desired class (e.g., from Risky → Good)."
+        )
+
+    with gr.Tab("Fairness Snapshot"):
+        gr.Markdown("### Quick Fairness Check")
+        fair_df = gr.Dataframe(label="Approval rate across groups", interactive=False)
+        gr.Markdown(
+            "This is a lightweight monitoring view for *potential* disparities across sensitive groups (Age/Job)."
+        )
+
+    # Wire outputs
+    def format_decision_md(decision: str):
+        if "Risky" in decision:
+            return f"## Decision: **{decision}**"
+        return f"## Decision: **{decision}**"
+
+    def format_prob_md(prob_pct: str):
+        return f"### Risk probability: **{prob_pct}**"
+
+    # Run action
+    outputs = [decision_out, prob_out, shap_plot, shap_df, adverse_out, cf_df, fair_df]
+
+    run_btn.click(
+        fn=run_dashboard,
+        inputs=[threshold, *list(inputs.values())],
+        outputs=outputs,
+    )
+
+    # Post-format decision/prob
+    run_btn.click(
+        fn=lambda d, p, *rest: (format_decision_md(d), format_prob_md(p), *rest),
+        inputs=outputs,
+        outputs=outputs,
+    )
+
+
+def _launch():
+    # Spaces-friendly
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+
+
+if __name__ == "__main__":
+    _launch()

german_credit_data.csv

@@ -0,0 +1,1001 @@
+,Age,Sex,Job,Housing,Saving accounts,Checking account,Credit amount,Duration,Purpose
+0,67,male,2,own,NA,little,1169,6,radio/TV
+1,22,female,2,own,little,moderate,5951,48,radio/TV
+2,49,male,1,own,little,NA,2096,12,education
+3,45,male,2,free,little,little,7882,42,furniture/equipment
+4,53,male,2,free,little,little,4870,24,car
+5,35,male,1,free,NA,NA,9055,36,education
+6,53,male,2,own,quite rich,NA,2835,24,furniture/equipment
+7,35,male,3,rent,little,moderate,6948,36,car
+8,61,male,1,own,rich,NA,3059,12,radio/TV
+9,28,male,3,own,little,moderate,5234,30,car
+10,25,female,2,rent,little,moderate,1295,12,car
+11,24,female,2,rent,little,little,4308,48,business
+12,22,female,2,own,little,moderate,1567,12,radio/TV
+13,60,male,1,own,little,little,1199,24,car
+14,28,female,2,rent,little,little,1403,15,car
+15,32,female,1,own,moderate,little,1282,24,radio/TV
+16,53,male,2,own,NA,NA,2424,24,radio/TV
+17,25,male,2,own,NA,little,8072,30,business
+18,44,female,3,free,little,moderate,12579,24,car
+19,31,male,2,own,quite rich,NA,3430,24,radio/TV
+20,48,male,2,own,little,NA,2134,9,car
+21,44,male,2,rent,quite rich,little,2647,6,radio/TV
+22,48,male,1,rent,little,little,2241,10,car
+23,44,male,2,own,moderate,moderate,1804,12,car
+24,26,male,2,own,NA,NA,2069,10,furniture/equipment
+25,36,male,1,own,little,little,1374,6,furniture/equipment
+26,39,male,1,own,little,NA,426,6,radio/TV
+27,42,female,2,rent,rich,rich,409,12,radio/TV
+28,34,male,2,own,little,moderate,2415,7,radio/TV
+29,63,male,2,own,little,little,6836,60,business
+30,36,male,2,own,rich,moderate,1913,18,business
+31,27,male,2,own,little,little,4020,24,furniture/equipment
+32,30,male,2,own,moderate,moderate,5866,18,car
+33,57,male,1,rent,NA,NA,1264,12,business
+34,33,female,3,own,little,rich,1474,12,furniture/equipment
+35,25,male,1,own,little,moderate,4746,45,radio/TV
+36,31,male,2,free,little,NA,6110,48,education
+37,37,male,2,own,little,rich,2100,18,radio/TV
+38,37,male,2,own,little,rich,1225,10,domestic appliances
+39,24,male,2,own,little,moderate,458,9,radio/TV
+40,30,male,3,own,quite rich,NA,2333,30,radio/TV
+41,26,male,2,own,quite rich,moderate,1158,12,radio/TV
+42,44,male,1,own,little,moderate,6204,18,repairs
+43,24,male,2,rent,moderate,little,6187,30,car
+44,58,female,1,free,little,little,6143,48,car
+45,35,female,3,own,little,NA,1393,11,car
+46,39,male,2,own,quite rich,NA,2299,36,radio/TV
+47,23,female,0,rent,quite rich,little,1352,6,car
+48,39,male,1,own,little,NA,7228,11,car
+49,28,female,2,own,moderate,NA,2073,12,radio/TV
+50,29,male,1,own,NA,moderate,2333,24,furniture/equipment
+51,30,male,3,own,little,moderate,5965,27,car
+52,25,male,2,own,little,NA,1262,12,radio/TV
+53,31,male,2,own,NA,NA,3378,18,car
+54,57,male,2,free,little,moderate,2225,36,car
+55,26,male,1,own,NA,NA,783,6,car
+56,52,male,3,own,NA,moderate,6468,12,radio/TV
+57,31,female,2,own,little,NA,9566,36,radio/TV
+58,23,female,3,own,little,rich,1961,18,car
+59,23,female,1,rent,little,little,6229,36,furniture/equipment
+60,27,male,2,own,little,moderate,1391,9,business
+61,50,male,2,own,NA,moderate,1537,15,radio/TV
+62,61,male,3,free,little,moderate,1953,36,business
+63,25,male,2,own,little,moderate,14421,48,business
+64,26,female,2,own,little,NA,3181,24,radio/TV
+65,48,male,2,own,NA,NA,5190,27,repairs
+66,29,female,2,own,little,NA,2171,12,radio/TV
+67,22,male,2,own,rich,moderate,1007,12,car
+68,37,male,2,free,little,NA,1819,36,education
+69,25,female,2,own,NA,NA,2394,36,radio/TV
+70,30,female,2,own,little,NA,8133,36,car
+71,46,male,1,rent,NA,NA,730,7,radio/TV
+72,51,male,3,free,little,little,1164,8,vacation/others
+73,41,female,1,own,little,moderate,5954,42,business
+74,40,male,3,own,NA,little,1977,36,education
+75,66,male,3,free,little,little,1526,12,car
+76,34,male,2,own,little,little,3965,42,radio/TV
+77,51,male,2,own,little,moderate,4771,11,radio/TV
+78,39,male,1,own,NA,NA,9436,54,car
+79,22,male,2,own,little,moderate,3832,30,furniture/equipment
+80,44,female,2,own,NA,NA,5943,24,radio/TV
+81,47,male,2,own,quite rich,NA,1213,15,radio/TV
+82,24,female,1,rent,moderate,NA,1568,18,business
+83,58,female,1,own,little,little,1755,24,vacation/others
+84,52,male,1,own,little,little,2315,10,radio/TV
+85,29,female,3,own,little,NA,1412,12,business
+86,27,female,2,own,little,moderate,1295,18,furniture/equipment
+87,47,male,2,free,moderate,moderate,12612,36,education
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+998,23,male,2,free,little,little,1845,45,radio/TV
+999,27,male,2,own,moderate,moderate,4576,45,car

requirements.txt

@@ -0,0 +1,9 @@
+gradio
+pandas
+numpy
+scikit-learn
+xgboost
+matplotlib
+joblib
+shap
+dice-ml

xgb_credit_model.pkl

@@ -0,0 +1,3 @@
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+size 380634