Upload DecisionTree.py

DecisionTree.py

@@ -0,0 +1,49 @@
+import streamlit as st
+from sklearn.tree import DecisionTreeClassifier
+from  data import dataPreprocessing,inputData
+import base64
+from PIL import Image
+
+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 model
+
+
+def 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