Add Streamlit app for predicting Iris flower species

Author: mijanr

app/pages/4_Classification.py

@@ -0,0 +1,71 @@
+"""
+Create a Streamlit app that takes in features about the iris flower and returns the species of the flower.
+"""
+import streamlit as st
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.datasets import load_iris
+import requests
+
+def load_data():
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    return X, y
+
+
+def app():
+    
+    # Add title and description
+    st.title("Predicting Iris Flower Species")
+
+    # Sidebar with input fields
+    sepal_length = st.slider("Sepal Length", 0.0, 10.0, 5.0)
+    sepal_width = st.slider("Sepal Width", 0.0, 10.0, 5.0)
+    petal_length = st.slider("Petal Length", 0.0, 10.0, 5.0)
+    petal_width = st.slider("Petal Width", 0.0, 10.0, 5.0)
+
+    # Create input data (dictionary)
+    input_data = {
+        "sepal_length": sepal_length,
+        "sepal_width": sepal_width,
+        "petal_length": petal_length,
+        "petal_width": petal_width
+    }
+
+    # interact with FastAPI endpoint
+    response = requests.post("http://127.0.0.1:8000/predict", json=input_data)
+
+    target_names = ['Setosa', 'Versicolor', 'Virginica']
+    if st.button("Predict"):
+        # make a post request to the FastAPI endpoint
+        prediction = int(response.json()[0])
+        pred_prob = response.json()[1]
+
+        # print results
+        st.write(f"Species predicted: {target_names[prediction]} with {pred_prob:.2f}% confidence")
+
+    # We will plot how the train data clusters in 2D space and then see how the test data fits in it.
+    # First apply PCA to reduce the dimensionality of the data to 2D
+    X, y = load_data()
+    pca = PCA(n_components=2)
+    X_pca = pca.fit_transform(X)
+
+    data_test = [[sepal_length, sepal_width, petal_length, petal_width]]
+    data_test = pca.transform(data_test)
+
+    # add class labels as legend
+    fig, ax = plt.subplots()
+    for i in range(3):
+        ax.scatter(X_pca[y==i, 0], X_pca[y==i, 1], label=target_names[i])
+        
+    # Use "test_data" as label for the test data
+    ax.scatter(data_test[:, 0], data_test[:, 1], c='red', marker='x', s=100, label='test_data')
+    ax.set_xlabel('First Principal Component')
+    ax.set_ylabel('Second Principal Component')
+    ax.set_title('Train data')
+    ax.legend()
+    st.pyplot(fig)
+
+if __name__ == "__main__":
+    app()