app.py

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}**")