Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+
+def get_user_data():
+    data_points = []
+    labels = []
+
+    for i in range(5):
+        x = st.number_input(f"Enter x-coordinate for data point {i + 1}:")
+        y = st.number_input(f"Enter y-coordinate for data point {i + 1}:")
+        label = st.text_input(f"Enter label for data point {i + 1}:")
+
+        data_points.append([x, y])
+        labels.append(label)
+
+    return np.array(data_points), np.array(labels)
+
+def knn_classification(X, y, k_value):
+    knn_classifier = KNeighborsClassifier(n_neighbors=k_value)
+    knn_classifier.fit(X, y)
+    predictions = knn_classifier.predict(X)
+    return predictions
+
+def main():
+    st.title("K-Nearest Neighbor Classification App")
+
+    # Get user-defined data
+    X, y = get_user_data()
+
+    # Choose the value of k
+    k_value = st.slider("Choose the value of k for k-nearest neighbors:", min_value=1, max_value=10, value=3)
+
+    # Perform k-nearest neighbor classification
+    predictions = knn_classification(X, y, k_value)
+
+    # Display results
+    st.subheader("Results:")
+    st.write("User-defined Data Points:")
+    st.write(X)
+    st.write("User-defined Labels:")
+    st.write(y)
+    st.write(f"\nK-Nearest Neighbor Classification (k={k_value}):")
+    st.write("Predicted Labels:")
+    st.write(predictions)
+
+if __name__ == "__main__":
+    main()