收手吧，华强！我用机器学习帮你挑西瓜

def inputData():
    st.sidebar.subheader("请选择西瓜外观:sunglasses:")
    color = st.sidebar.selectbox("色泽", ("青绿", "乌黑", "浅白"))
    root = st.sidebar.selectbox("根蒂", ("蜷缩", "稍蜷", "硬挺"))
    knocks = st.sidebar.selectbox("敲击", ("浊响", "沉闷", "清脆"))
    texture = st.sidebar.selectbox("纹理", ("清晰", "稍糊", "模糊"))
    navel = st.sidebar.selectbox("脐部", ("凹陷", "稍凹", "平坦"))
    touch = st.sidebar.selectbox("触感", ("硬滑", "软粘"))
    input = [[color, root, knocks, texture, navel, touch]]
    features = ["color", "root", "knocks", "texture", "navel", "touch"]
    np.array(input).reshape(1, 6)
    df_input = pd.DataFrame(input, columns=features, index=None)

    for feature in features[0:6]:
        le = joblib.load("./models/" + feature + "_LabelEncoder.model")
        df_input[feature] = le.transform(df_input[feature])

    return df_input

def dt_param_selector():
    st.sidebar.subheader("请选择模型参数:sunglasses:")
    criterion = st.sidebar.selectbox("criterion", ["gini", "entropy"])
    max_depth = st.sidebar.number_input("max_depth", 1, 50, 5, 1)
    min_samples_split = st.sidebar.number_input(
        "min_samples_split", 1, 20, 2, 1)
    max_features = st.sidebar.selectbox(
        "max_features", [None, "auto", "sqrt", "log2"])

    params = {
        "criterion": criterion,
        "max_depth": max_depth,
        "min_samples_split": min_samples_split,
        "max_features": max_features,
    }

    model = DecisionTreeClassifier(**params)
    df = dataPreprocessing()
    X, y = df[df.columns[:-1]], df["label"]
    model.fit(X, y)
    return modeldef predictor():
    df_input = inputData()
    model = dt_param_selector()
    y_pred = model.predict(df_input)
    if y_pred == 1:
        goodwatermelon = Image.open("./pics/good.png")
        st.image(goodwatermelon,width=705,use_column_width= True)
        st.markdown("<center>🍉🍉🍉这瓜甚甜，买一个🍉🍉🍉</center>", unsafe_allow_html=True)
    else:
        file_ = open("./pics/bad2.gif", "rb")
        contents = file_.read()
        data_url = base64.b64encode(contents).decode("utf-8")
        file_.close()

        st.markdown(
            f'<img src="data:image/gif;base64,{data_url}" width="100%">',
            unsafe_allow_html=True,
        )
        st.markdown('<center>🔪🔪🔪这瓜不甜，买不得🔪🔪🔪</center>', unsafe_allow_html=True)
    return y_pred,model

def decisionTreeViz():
    df,le = getDataSet()
    X, y = df[df.columns[:-1]], df["label"]
    clf = joblib.load('..\watermelonClassifier.pkl')
    viz = dtreeviz(clf, 
                X, 
                y,
                orientation="LR",
                target_name='label',
                feature_names=df.columns[:-1],
                class_names=["good","bad"]
                )  
                
    return viz

def svg_write(svg, center=True):
    """
    Disable center to left-margin align like other objects.
    """
    # Encode as base 64
    b64 = base64.b64encode(svg.encode("utf-8")).decode("utf-8")

    # Add some CSS on top
    css_justify = "center" if center else "left"
    css = f'<p style="text-align:center; display: flex; justify-content: {css_justify};">'
    html = f'{css}<img src="data:image/svg+xml;base64,{b64}"/>'

    # Write the HTML
    st.write(html, unsafe_allow_html=True)

st.title
st.write
st.code
st.table
st.markdown
st.sidebar
st.expander
st.code
st.image
st.pyplot