Create app.py

app.py

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+import streamlit as st
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+feature_names = iris.feature_names
+target_names = iris.target_names
+
+# Train a Decision Tree model
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+clf = DecisionTreeClassifier(max_depth=3, random_state=42)
+clf.fit(X_train, y_train)
+
+# Streamlit interface
+st.title("🌸 Iris Flower Predictor with Decision Tree")
+st.write("This app uses a Decision Tree Classifier to predict the type of Iris flower.")
+
+# Sidebar for user input
+st.sidebar.header("Input Features")
+sepal_length = st.sidebar.slider('Sepal length (cm)', 4.0, 8.0, 5.1)
+sepal_width = st.sidebar.slider('Sepal width (cm)', 2.0, 4.5, 3.5)
+petal_length = st.sidebar.slider('Petal length (cm)', 1.0, 7.0, 1.4)
+petal_width = st.sidebar.slider('Petal width (cm)', 0.1, 2.5, 0.2)
+
+# Make prediction
+input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
+prediction = clf.predict(input_data)[0]
+predicted_class = target_names[prediction]
+
+st.subheader("🌼 Predicted Iris Species")
+st.success(f"The model predicts: **{predicted_class}**")
+
+# Show decision tree
+st.subheader("🧠 Decision Tree Visualization")
+fig, ax = plt.subplots(figsize=(12, 6))
+plot_tree(clf, feature_names=feature_names, class_names=target_names, filled=True, rounded=True)
+st.pyplot(fig)
+
+# Show model accuracy
+accuracy = clf.score(X_test, y_test)
+st.subheader("📈 Model Accuracy")
+st.write(f"The model accuracy on the test set is **{accuracy:.2f}**")