removed 3rd example image and only load models at startup

app.py

@@ -19,8 +19,22 @@ IMG_SIZE=256
 tf.experimental.numpy.experimental_enable_numpy_behavior()
 #np.warnings.filterwarnings('error', category=np.VisibleDeprecationWarning) 
 
-model_M1 = None
-model_M2 = None
+# function to load models
+#@st.session_state  better for hugging face?
+@st.cache_resource
+def load_models():
+    #load models
+    model_path1 = "IMVIP_Supplementary_Material/models/model1/"
+    model_path2 = "IMVIP_Supplementary_Material/models/model2/"
+
+    model_M1 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/") #tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/")
+    model_M2 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model2/")
+
+    return model_M1, model_M2
+
+
+model_M1, model_M2 = load_models()
+
 
 def check_forgery_df(img):
     shape_original = img.shape
@@ -30,7 +44,6 @@ def check_forgery_df(img):
     pred1 = model_M1.predict(x, verbose=0)
     pred2= model_M2.predict(x, verbose=0)   
 
-
 #    # Ensure pred1 and pred2 are numpy arrays before proceeding
 #    if isinstance(pred1, dict):
 #        print("pred1 is dict!")
@@ -38,7 +51,6 @@ def check_forgery_df(img):
 #    if isinstance(pred2, dict):
 #        pred2 = pred2[next(iter(pred2))]
 
-    
     pred = np.max([pred1,pred2], axis=0)   
     
     pred = dfutils.create_mask(pred)
@@ -52,26 +64,8 @@ def evaluate(img):
     pre_t = check_forgery_df(img)
     st.image(pre_t, caption="White area indicates potential image manipulations.")
 
+
 def start_evaluation(uploaded_file):    
-    #load models
-    model_path1 = "IMVIP_Supplementary_Material/models/model1/"
-    model_path2 = "IMVIP_Supplementary_Material/models/model2/"
-    
-    #tfsm_layer1 = tf.keras.layers.TFSMLayer(model_path1, call_endpoint='serving_default')
-    #tfsm_layer2 = tf.keras.layers.TFSMLayer(model_path2, call_endpoint='serving_default')
-    #
-    #input_shape = (256, 256, 3)
-    #inputs = Input(shape=input_shape)
-
-    ##create the model
-    #outputs1 = tfsm_layer1(inputs)
-    #model_M1 = Model(inputs, outputs1)
-
-    #outputs2 = tfsm_layer2(inputs)
-    #model_M2 = Model(inputs, outputs2)
-    
-    model_M1 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/") #tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/")
-    model_M2 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model2/")
 
     # Convert the file to an opencv image.
     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
@@ -86,9 +80,9 @@ st.markdown(DESCRIPTION)
 
 img_path1 = "example_images/Sp_D_NRD_A_nat0095_art0058_0582"
 img_path2 = "example_images/Sp_D_NRN_A_nat0083_arc0080_0445"
-img_path3 = "example_images/Sp_D_NRN_A_ani0088_cha0044_0441"
-image_paths = [img_path1+".jpg", img_path2+".jpg", img_path3+".jpg"]
-gt_paths = [img_path1+"_gt.png", img_path2+"_gt.png", img_path3+"_gt.png"]
+#img_path3 = "example_images/Sp_D_NRN_A_ani0088_cha0044_0441"
+image_paths = [img_path1+".jpg", img_path2+".jpg"] #, img_path3+".jpg"]
+gt_paths = [img_path1+"_gt.png", img_path2+"_gt.png"] #, img_path3+"_gt.png"]
 # Display images in a table format
 img = None
 for idx, image_path in enumerate(image_paths):
@@ -113,37 +107,6 @@ def reset_image_select():
     img = None
 
 
-
-
-def start_evaluation(uploaded_file):    
-    #load models
-    model_path1 = "IMVIP_Supplementary_Material/models/model1/"
-    model_path2 = "IMVIP_Supplementary_Material/models/model2/"
-    
-    #tfsm_layer1 = tf.keras.layers.TFSMLayer(model_path1, call_endpoint='serving_default')
-    #tfsm_layer2 = tf.keras.layers.TFSMLayer(model_path2, call_endpoint='serving_default')
-    #
-    #input_shape = (256, 256, 3)
-    #inputs = Input(shape=input_shape)
-
-    ##create the model
-    #outputs1 = tfsm_layer1(inputs)
-    #model_M1 = Model(inputs, outputs1)
-
-    #outputs2 = tfsm_layer2(inputs)
-    #model_M2 = Model(inputs, outputs2)
-    
-    model_M1 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/") #tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/")
-    model_M2 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model2/")
-
-    # Convert the file to an opencv image.
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    opencv_image = cv2.imdecode(file_bytes, 1)
-    reversed_image = opencv_image[:, :, ::-1]
-    st.image(reversed_image, caption="Input Image")
-    evaluate(reversed_image)
-
-
 uploaded_file= None
 uploaded_file = st.file_uploader("Please upload an image", type=["jpeg", "jpg", "png"], on_change=reset_image_select)
 if (uploaded_file is not None) and (img is None):