Add new examples

app.py

@@ -6,7 +6,7 @@ current_dir = pathlib.Path(__file__).parent
 
 # images = [str(current_dir / "cheetah1.jpeg"), str(current_dir / "cheetah1.jpg"), str(current_dir / "lion.jpg")]
 
-images = [str(current_dir / "data/benign/benign_1.jpg"), str(current_dir / "data/benign/benign_2.jpg"), str(current_dir / "data/malignant/malignant_1.jpg"), str(current_dir / "data/malignant/malignant_2.jpg")]
+images = [str(current_dir / "data/benign/benign_4.jpg"), str(current_dir / "data/benign/benign_5.jpg"), str(current_dir / "data/benign/benign_6.jpg"), str(current_dir / "data/malignant/malignant_4.jpg"), str(current_dir / "data/malignant/malignant_5.jpg"), str(current_dir / "data/malignant/malignant_6.jpg")]
 
 
 # img_classifier = gr.Interface.load(
@@ -62,11 +62,11 @@ from skimage.transform import resize
 # oc_resnet50_model1 = keras.models.load_model('./models/oc_model.h5')
 print("current_dir", current_dir)
 oc_resnet50_model2 = keras.models.load_model(f"{current_dir}/models/mendeley_oc_model_v2.h5")
-labels = ['Malignant Lesion', 'Benign Lesion']
+labels = ['Benign Lesion', 'Malignant Lesion']
 
 def classify_image(inp):
 
-  inp =resize(inp, (300, 300, 3))
+  # inp =resize(inp, (300, 300, 3))
   inp = inp.reshape((-1, 300, 300, 3))
   # inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
   inp = tf.keras.applications.resnet50.preprocess_input(inp)
@@ -78,5 +78,5 @@ gr.Interface(fn=classify_image,
              inputs=gr.Image(shape=(300, 300)),
              outputs=gr.Label(num_top_classes=2),
              examples=images, cache_examples=False,
-             interpretation="shap", num_shap=5
+            #  interpretation="shap", num_shap=5
              ).launch()