Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier, export_graphviz
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, confusion_matrix
+import graphviz
+
+def main():
+    st.title("Decision Tree Classifier Demo")
+    st.write("An example using the Iris dataset with a Streamlit interface.")
+
+    # Load the Iris dataset
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    feature_names = iris.feature_names
+    target_names = iris.target_names
+
+    # Sidebar: Hyperparameters
+    st.sidebar.header("Model Hyperparameters")
+    criterion = st.sidebar.selectbox("Criterion", ("gini", "entropy", "log_loss"))
+    max_depth = st.sidebar.slider("Max Depth", 1, 10, 3)
+    min_samples_split = st.sidebar.slider("Min Samples Split", 2, 10, 2)
+
+    # Split data into train and test sets
+    test_size = st.sidebar.slider("Test Size (fraction)", 0.1, 0.9, 0.3, 0.1)
+    random_state = st.sidebar.number_input("Random State", 0, 100, 42, 1)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=test_size, random_state=random_state
+    )
+
+    # Train the Decision Tree model
+    clf = DecisionTreeClassifier(
+        criterion=criterion,
+        max_depth=max_depth,
+        min_samples_split=min_samples_split,
+        random_state=random_state
+    )
+    clf.fit(X_train, y_train)
+
+    # Make predictions
+    y_pred = clf.predict(X_test)
+
+    # Show metrics
+    accuracy = accuracy_score(y_test, y_pred)
+    cm = confusion_matrix(y_test, y_pred)
+
+    st.subheader("Model Performance")
+    st.write(f"**Accuracy**: {accuracy:.2f}")
+
+    st.write("**Confusion Matrix**:")
+    st.write(cm)
+
+    # Visualize the decision tree
+    st.subheader("Decision Tree Visualization")
+    dot_data = export_graphviz(
+        clf,
+        out_file=None,
+        feature_names=feature_names,
+        class_names=target_names,
+        filled=True,
+        rounded=True,
+        special_characters=True
+    )
+    st.graphviz_chart(dot_data)
+
+    # Predict on user input
+    st.subheader("Make a Prediction")
+    user_inputs = []
+    for i, feature in enumerate(feature_names):
+        val = st.number_input(f"{feature}", float(X[:, i].min()), float(X[:, i].max()), float(X[:, i].mean()))
+        user_inputs.append(val)
+
+    if st.button("Predict Class"):
+        user_pred = clf.predict([user_inputs])
+        st.write(f"**Predicted Class**: {target_names[user_pred][0]}")
+
+if __name__ == "__main__":
+    main()