Delete app.py with huggingface_hub

app.py

@@ -1,165 +0,0 @@
-import gradio as gr
-import random
-import matplotlib
-import matplotlib.pyplot as plt
-import pandas as pd
-import shap
-import xgboost as xgb
-from datasets import load_dataset
-
-
-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 = [
-    "workclass",
-    "education",
-    "marital.status",
-    "occupation",
-    "relationship",
-    "sex",
-    "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})
-    pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))
-    return {">50K": float(pos_pred[0]), "<=50K": 1 - float(pos_pred[0])}
-
-
-def interpret(*args):
-    df = pd.DataFrame([args], columns=X_train.columns)
-    df = df.astype({col: "category" for col in categorical_columns})
-    shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))
-    scores_desc = list(zip(shap_values[0], X_train.columns))
-    scores_desc = sorted(scores_desc)
-    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.tight_layout()
-    return fig_m
-
-
-unique_class = sorted(X_train["workclass"].unique())
-unique_education = sorted(X_train["education"].unique())
-unique_marital_status = sorted(X_train["marital.status"].unique())
-unique_relationship = sorted(X_train["relationship"].unique())
-unique_occupation = sorted(X_train["occupation"].unique())
-unique_sex = sorted(X_train["sex"].unique())
-unique_country = sorted(X_train["native.country"].unique())
-
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    **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():
-            age = gr.Slider(label="Age", minimum=17, maximum=90, step=1, randomize=True)
-            work_class = gr.Dropdown(
-                label="Workclass",
-                choices=unique_class,
-                value=lambda: random.choice(unique_class),
-            )
-            education = gr.Dropdown(
-                label="Education Level",
-                choices=unique_education,
-                value=lambda: random.choice(unique_education),
-            )
-            years = gr.Slider(
-                label="Years of schooling",
-                minimum=1,
-                maximum=16,
-                step=1,
-                randomize=True,
-            )
-            marital_status = gr.Dropdown(
-                label="Marital Status",
-                choices=unique_marital_status,
-                value=lambda: random.choice(unique_marital_status),
-            )
-            occupation = gr.Dropdown(
-                label="Occupation",
-                choices=unique_occupation,
-                value=lambda: random.choice(unique_occupation),
-            )
-            relationship = gr.Dropdown(
-                label="Relationship Status",
-                choices=unique_relationship,
-                value=lambda: random.choice(unique_relationship),
-            )
-            sex = gr.Dropdown(
-                label="Sex", choices=unique_sex, value=lambda: random.choice(unique_sex)
-            )
-            capital_gain = gr.Slider(
-                label="Capital Gain",
-                minimum=0,
-                maximum=100000,
-                step=500,
-                randomize=True,
-            )
-            capital_loss = gr.Slider(
-                label="Capital Loss", minimum=0, maximum=10000, step=500, randomize=True
-            )
-            hours_per_week = gr.Slider(
-                label="Hours Per Week Worked", minimum=1, maximum=99, step=1
-            )
-            country = gr.Dropdown(
-                label="Native Country",
-                choices=unique_country,
-                value=lambda: random.choice(unique_country),
-            )
-        with gr.Column():
-            label = gr.Label()
-            plot = gr.Plot()
-            with gr.Row():
-                predict_btn = gr.Button(value="Predict")
-                interpret_btn = gr.Button(value="Explain")
-            predict_btn.click(
-                predict,
-                inputs=[
-                    age,
-                    work_class,
-                    education,
-                    years,
-                    marital_status,
-                    occupation,
-                    relationship,
-                    sex,
-                    capital_gain,
-                    capital_loss,
-                    hours_per_week,
-                    country,
-                ],
-                outputs=[label],
-            )
-            interpret_btn.click(
-                interpret,
-                inputs=[
-                    age,
-                    work_class,
-                    education,
-                    years,
-                    marital_status,
-                    occupation,
-                    relationship,
-                    sex,
-                    capital_gain,
-                    capital_loss,
-                    hours_per_week,
-                    country,
-                ],
-                outputs=[plot],
-            )
-
-demo.launch()