Create app.py

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+import os
+from os import system
+from graphviz import Source
+import dtreeviz
+import base64
+
+
+os.environ["PATH"] += os.pathsep + '/home/adminuser/venv/lib/python3.9/site-packages/graphviz/bin/'
+# Load the Iris dataset from scikit-learn
+iris = load_iris()
+X = iris.data[:, :2]  # Using only two features for visualization purposes
+y = iris.target
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+st.sidebar.markdown("# Decision Tree Classifier")
+criterion = st.sidebar.selectbox(
+   'Criterion',
+   ('gini', 'entropy')
+)
+splitter = st.sidebar.selectbox(
+   'Splitter',
+   ('best', 'random')
+)
+max_depth = st.sidebar.slider('Max Depth', 1, 20)
+min_samples_split = st.sidebar.slider('Min Samples Split', 2, 100, 2)
+min_samples_leaf = st.sidebar.slider('Min Samples Leaf', 1, 100, 1)
+max_features = st.sidebar.slider('Max Features', 1, 4, 2)
+max_leaf_nodes = st.sidebar.slider('Max Leaf Nodes', 2, 50)
+min_impurity_decrease = st.sidebar.number_input('Min Impurity Decrease')
+# Rest of your sidebar inputs...
+# Load initial graph
+fig, ax = plt.subplots()
+# Plot initial graph
+ax.scatter(X[:, 0], X[:, 1], c=y, cmap='rainbow')
+orig = st.pyplot(fig)
+if st.sidebar.button('Run Algorithm'):
+    orig.empty()
+    clf = DecisionTreeClassifier(criterion=criterion, splitter=splitter, max_depth=max_depth, random_state=42,
+                                min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf,
+                                max_features=max_features, max_leaf_nodes=max_leaf_nodes,
+                                min_impurity_decrease=min_impurity_decrease)
+    clf.fit(X_train, y_train)
+    y_pred = clf.predict(X_test)
+    viz_model = dtreeviz.model(clf,
+                           X_train=X, y_train=y,
+                           feature_names=iris.feature_names,
+                           target_name='iris',
+                           class_names=iris.target_names)
+
+    v = viz_model.view()     # render as SVG into internal object 
+    # v.show()                 # pop up window
+    # v.save("/tmp/iris.svg")
+    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.write(v)
+    svg=v.svg()
+    # svg_write(svg)
+    st.subheader("Accuracy for Decision Tree: " + str(round(accuracy_score(y_test, y_pred), 2)))