Merge branch 'main' of https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability

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DESCRIPTION.md

@@ -0,0 +1 @@
+This demo takes in 12 inputs from the user in dropdowns and sliders and predicts income. It also has a separate button for explaining the prediction.

README.md

@@ -1,13 +1,12 @@
+
 ---
-title: Xgboost Income Prediction With Explainability
+title: xgboost-income-prediction-with-explainability 
 emoji: 🔥
-colorFrom: blue
-colorTo: green
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 3.1.7
+sdk_version: 3.4
+
 app_file: app.py
 pinned: false
-license: mit
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,6 +1,5 @@
-import random
-
 import gradio as gr
+import random
 import matplotlib
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -8,14 +7,12 @@ import shap
 import xgboost as xgb
 from datasets import load_dataset
 
-matplotlib.use("Agg")
 
+matplotlib.use("Agg")
 dataset = load_dataset("scikit-learn/adult-census-income")
-
 X_train = dataset["train"].to_pandas()
 _ = X_train.pop("fnlwgt")
 _ = X_train.pop("race")
-
 y_train = X_train.pop("income")
 y_train = (y_train == ">50K").astype(int)
 categorical_columns = [
@@ -28,13 +25,10 @@ categorical_columns = [
     "native.country",
 ]
 X_train = X_train.astype({col: "category" for col in categorical_columns})
-
-
 data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)
 model = xgb.train(params={"objective": "binary:logistic"}, dtrain=data)
 explainer = shap.TreeExplainer(model)
 
-
 def predict(*args):
     df = pd.DataFrame([args], columns=X_train.columns)
     df = df.astype({col: "category" for col in categorical_columns})
@@ -51,8 +45,8 @@ def interpret(*args):
     fig_m = plt.figure(tight_layout=True)
     plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
     plt.title("Feature Shap Values")
-    plt.ylabel("Shap Value")
-    plt.xlabel("Feature")
+    plt.ylabel("Feature")
+    plt.xlabel("Shap Value")
     plt.tight_layout()
     return fig_m
 
@@ -67,12 +61,7 @@ unique_country = sorted(X_train["native.country"].unique())
 
 with gr.Blocks() as demo:
     gr.Markdown("""
-    ## Income Classification with XGBoost 💰
-    
-    This example shows how to load data from the hugging face hub to train an XGBoost classifier and
-    demo the predictions with gradio.
-
-    The source is [here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability).
+    **Income Classification with XGBoost 💰**:  This demo uses an XGBoost classifier predicts income based on demographic factors, along with Shapley value-based *explanations*. The [source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability/blob/main/app.py).
     """)
     with gr.Row():
         with gr.Column():
@@ -136,7 +125,7 @@ with gr.Blocks() as demo:
             plot = gr.Plot()
             with gr.Row():
                 predict_btn = gr.Button(value="Predict")
-                interpret_btn = gr.Button(value="Interpret")
+                interpret_btn = gr.Button(value="Explain")
             predict_btn.click(
                 predict,
                 inputs=[