Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import numpy as np
 from sklearn.neighbors import KNeighborsClassifier
 
-def get_user_data():
+def get_user_data_train():
     data_points = []
     labels = []
 
@@ -15,6 +15,21 @@ def get_user_data():
         labels.append(label)
 
     return np.array(data_points), np.array(labels)
+    
+def get_user_data_test():
+    data_points = []
+    labels = []
+
+    for i in range(1):
+        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}:")
+        y='a'
+        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)
@@ -25,21 +40,23 @@ def knn_classification(X, y, k_value):
 def main():
     st.title("K-Nearest Neighbor Classification App")
 
-    # Get user-defined data
-    X, y = get_user_data()
+    # Get user-defined data train
+    X, y = get_user_data_train()
 
     # 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)
+    
+    # Get user-defined data test
+    X_test, y_test = get_user_data_test()
+    
 
     # Perform k-nearest neighbor classification
-    predictions = knn_classification(X, y, k_value)
+    predictions = knn_classification(X_test, 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(X_test)
     st.write(f"\nK-Nearest Neighbor Classification (k={k_value}):")
     st.write("Predicted Labels:")
     st.write(predictions)