update model name

app.py

@@ -107,21 +107,21 @@ def get_model(model_name):
                         backbone_class=tf.keras.applications.ResNet50V2,
                         nb_classes = n_classes,load_weights=False,finer_model=True,backbone_name ='Resnet50v2')
         model.load_weights('model_classification/rock-170.h5')
+    # elif model_name == 'Fossils 142':
+    #     n_classes = 142
+    #     model = get_triplet_model_beit(input_shape = (384, 384, 3),
+    #                               embedding_units = 256,
+    #                               embedding_depth = 2,
+    #                               n_classes = n_classes)
+    #     model.load_weights('model_classification/fossil-142.h5')
+    # elif model_name == 'Fossils new':
+    #     n_classes = 142
+    #     model = get_triplet_model_beit(input_shape = (384, 384, 3),
+    #                               embedding_units = 256,
+    #                               embedding_depth = 2,
+    #                               n_classes = n_classes)
+    #     model.load_weights('model_classification/fossil-new.h5')
     elif model_name == 'Fossils 142':
-        n_classes = 142
-        model = get_triplet_model_beit(input_shape = (384, 384, 3),
-                                  embedding_units = 256,
-                                  embedding_depth = 2,
-                                  n_classes = n_classes)
-        model.load_weights('model_classification/fossil-142.h5')
-    elif model_name == 'Fossils new':
-        n_classes = 142
-        model = get_triplet_model_beit(input_shape = (384, 384, 3),
-                                  embedding_units = 256,
-                                  embedding_depth = 2,
-                                  n_classes = n_classes)
-        model.load_weights('model_classification/fossil-new.h5')
-    elif model_name == 'Fossils':
         n_classes = 142
         model,_,_ = get_resnet_model('model_classification/fossil-model.h5')
     else:
@@ -145,17 +145,17 @@ def classify_image(input_image, model_name):
         model, n_classes= get_model(model_name)
         result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes)
         return result 
-    elif 'Fossils 142' ==model_name:
-        from inference_beit import inference_resnet_finer_beit
-        model,n_classes = get_model(model_name)
-        result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes)
-        return result
-    elif 'Fossils new' ==model_name:
+    elif 'Fossils BEiT' ==model_name:
         from inference_beit import inference_resnet_finer_beit
         model,n_classes = get_model(model_name)
         result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes)
         return result
-    elif 'Fossils' ==model_name:
+    # elif 'Fossils new' ==model_name:
+    #     from inference_beit import inference_resnet_finer_beit
+    #     model,n_classes = get_model(model_name)
+    #     result = inference_resnet_finer_beit(input_image,model,size=384,n_classes=n_classes)
+    #     return result
+    elif 'Fossils 142' ==model_name:
         from inference_beit import inference_resnet_finer_beit
         model,n_classes = get_model(model_name)
         result = inference_resnet_finer_v2(input_image,model,size=384,n_classes=n_classes)
@@ -173,17 +173,17 @@ def get_embeddings(input_image,model_name):
         model, n_classes= get_model(model_name)
         result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes)
         return result 
-    elif 'Fossils 142' ==model_name:
+    elif 'Fossils BEiT' ==model_name:
         from inference_beit import inference_resnet_embedding_beit
         model,n_classes = get_model(model_name)
         result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes)
         return result
-    elif 'Fossils new' ==model_name:
-        from inference_beit import inference_resnet_embedding_beit
-        model,n_classes = get_model(model_name)
-        result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes)
-        return result
-    elif 'Fossils' ==model_name:
+    # elif 'Fossils new' ==model_name:
+    #     from inference_beit import inference_resnet_embedding_beit
+    #     model,n_classes = get_model(model_name)
+    #     result = inference_resnet_embedding_beit(input_image,model,size=384,n_classes=n_classes)
+    #     return result
+    elif 'Fossils 142' ==model_name:
         from inference_beit import inference_resnet_embedding_beit
         model,n_classes = get_model(model_name)
         result = inference_resnet_embedding_v2(input_image,model,size=384,n_classes=n_classes)
@@ -204,7 +204,7 @@ def generate_diagram_closest(input_image,model_name,top_k):
 
 def explain_image(input_image,model_name,explain_method,nb_samples):
     model,n_classes= get_model(model_name)
-    if model_name=='Fossils 142' or 'Fossils new':
+    if model_name=='Fossils BEiT' or 'Fossils 142':
         size = 384
     else:
         size = 600
@@ -244,7 +244,7 @@ def update_display(image):
     original_image = image
     processed_image = preprocess_image(image) 
     instruction = "Image ready. Please switch to the 'Specimen Workbench' tab to check out further analysis and outputs."
-    model_name = "Fossils"
+    model_name = "Fossils 142"
     
     # gr.Dropdown(
     #                 ["Mummified 170", "Rock 170","Fossils 142","Fossils new"],
@@ -315,9 +315,9 @@ with gr.Blocks(theme='sudeepshouche/minimalist') as demo:
                 
             with gr.Column():
                 model_name = gr.Dropdown(
-                    ["Mummified 170", "Rock 170","Fossils 142","Fossils new","Fossils"],
+                    ["Mummified 170", "Rock 170","Fossils BEiT","Fossils 142"],
                     multiselect=False,
-                    value="Fossils", # default option
+                    value="Fossils 142", # default option
                     label="Model",
                     interactive=True,
                     info="Choose the model you'd like to use"