Update app.py

app.py

@@ -14,9 +14,9 @@ from skimage.feature import local_binary_pattern
 def plot_features(features, title):
     plt.figure(figsize=(10, 6))
     for i, feature in enumerate(features.T):  # Transpose to plot feature by feature
-        plt.plot(feature, label=f"Feature {i+1}")
+        plt.plot(feature)  # Remove label if no legend is needed
     plt.title(title)
-    plt.legend()
+    # plt.legend()  # Removed the legend to avoid warning
     plt.show()
 
 # Define directories for grass and wood images
@@ -163,6 +163,10 @@ for train_index, test_index in kf.split(lbp_features):
     x_train, x_test = lbp_features[train_index], lbp_features[test_index]
     y_train, y_test = y[train_index], y[test_index]
 
+    lbp_classifier = KNeighborsClassifier(
+        n_neighbors=lbp_grid_search.best_params_["n_neighbors"],
+        p=lbp_grid_search.best_params_["p"]
+    )
     lbp_classifier.fit(x_train, y_train)
     y_pred = lbp_classifier.predict(x_test)
 
@@ -181,7 +185,7 @@ def classify_uploaded_image(image, algorithm):
         prediction = glcm_knn.predict(features)
     elif algorithm == "LBP":
         features = extract_lbp_features([image])
-        prediction = lbp_knn.predict(features)
+        prediction = lbp_classifier.predict(features)
     else:
         raise ValueError(f"Algorithm '{algorithm}' is not recognized.")