Update app.py

app.py

@@ -24,11 +24,34 @@ from transformers import CLIPProcessor, CLIPModel
 static_dir = Path('./static')
 static_dir.mkdir(parents=True, exist_ok=True)
 
-client = Client("http://ec2-54-220-125-165.eu-west-1.compute.amazonaws.com:8882")
+# client = Client("http://ec2-54-220-125-165.eu-west-1.compute.amazonaws.com:8882")
 # client = Client()
-index_name = "new_look_expanded_dresses"
+# index_name = "new_look_expanded_dresses"
+# device = "cpu"
+
+class Client_Settings():
+    def __init__(self):
+        self.client = Client()
+        self.index_name = "new_look_expanded_dresses"
+        self.device = "cpu"
+    
+    def conn_to_local(self):
+        self.client = Client()
+    
+    def conn_to_server(self, url):
+        self.client = Client(url)
+
+    def set_index_name(self, new_index_name):
+        self.index_name = new_index_name
+    
+    def set_device(self, new_device):
+        self.device = new_device
 
-# sys.path.insert(1, 'C:/Users/Alexandre/Documents/University/5_Ano/Estagio/repos_1')
+client_obj = Client_Settings()
+# client_obj.conn_to_local()
+client_obj.conn_to_server("http://ec2-54-220-125-165.eu-west-1.compute.amazonaws.com:8882")
+client_obj.set_index_name("new_look_expanded_dresses")
+client_obj.set_device("cuda")
 
 # Create custom Color objects for our primary, secondary, and neutral colors
 primary_color = gr.themes.colors.slate
@@ -85,9 +108,9 @@ def get_items_from_dataset(start_index=0, end_index=50, dataset=pd.read_json('{}
 #     return dataset_to_gallery(dedup_by(df, 'colour_code'))
 
 def start_page(num_results=50):
-    result = client.index(index_name).search("Dress", score_modifiers = {
+    result = client_obj.client.index(client_obj.index_name).search("Dress", score_modifiers = {
         "add_to_score": [{"field_name": "best_seller_score","weight": 5}],
-    }, searchable_attributes=['image'], device="cpu", limit=num_results)
+    }, searchable_attributes=['image'], device=client_obj.device, limit=num_results)
     imgs = [r for r in result["hits"]]
     return return_results_page(imgs)
 
@@ -99,14 +122,14 @@ def return_results_page(results_list: list):
 
 def return_item(combined) -> list:
     colour_code = combined.split("@@")[2]
-    result = client.index(index_name).search("", filter_string = "colour_code:" + str(colour_code), searchable_attributes=['image'], device="cpu")
+    result = client_obj.client.index(client_obj.index_name).search("", filter_string = "colour_code:" + str(colour_code), searchable_attributes=['image'], device=client_obj.device)
     imgs = [r for r in result["hits"]]
     df = pd.DataFrame(imgs)
     return dataset_to_gallery(df), imgs[0]["description_total"], imgs[0]["url"]
 
 def return_specific_item(combined) -> list:
     _id = combined.split("@@")[3]
-    result = client.index(index_name).search("", filter_string = "_id:" + str(_id), searchable_attributes=['image'], device="cpu")
+    result = client_obj.client.index(client_obj.index_name).search("", filter_string = "_id:" + str(_id), searchable_attributes=['image'], device=client_obj.device)
     imgs = [r for r in result["hits"]]
     print(imgs)
     df = pd.DataFrame(imgs)
@@ -117,24 +140,13 @@ def load_image(image_input):
     image_input.save("../../../Documents/images/img_path.jpg")
     os.system('docker cp "../../../Documents/images/img_path.jpg" marqo:"/images/images/"')
 
-### Search local
 def search_images(query, best_seller_score_weight):
-    result = client.index(index_name).search(query, score_modifiers = {
+    result = client_obj.client.index(client_obj.index_name).search(query, score_modifiers = {
         "add_to_score": [{"field_name": "best_seller_score","weight": best_seller_score_weight/1000}],
-    }, searchable_attributes=['image'], device="cpu", limit=40)
+    }, searchable_attributes=['image'], device=client_obj.device, limit=40)
     imgs = [r for r in result["hits"]]
     return imgs
 
-### Search AWS
-# def search_images(query, best_seller_score_weight):
-#     client = Client("http://ec2-54-220-125-165.eu-west-1.compute.amazonaws.com:8882")
-#     result = client.index("new_look_expanded_dresses").search(query, score_modifiers = {
-#         "add_to_score": [{"field_name": "best_seller_score","weight": best_seller_score_weight/1000}],
-#     }, searchable_attributes=['primary_image'], device="cpu", limit=40)
-#     imgs = [r for r in result["hits"]]
-
-#     return imgs
-
 # def get_labels_probs(labels, image):
 #     inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
 
@@ -183,7 +195,6 @@ with gr.Blocks(theme=theme, title="New Look", css=css) as demo:
             with gr.Column(scale=3):
                 best_seller_score_weight = gr.Slider(label = "Best seller relevance", minimum=-1, maximum=1, value=0, step=0.01)
                 search_button = gr.Button(value="Search")
-                index_name_state = gr.State(value=index_name)
             with gr.Column(scale=2):
                 image_input = gr.Image(type="pil", label="Search with image")
                 image_path = gr.State(visible=False)
@@ -198,7 +209,6 @@ with gr.Blocks(theme=theme, title="New Look", css=css) as demo:
                                             height="400",object_fit="contain")
                 image_description = gr.Text(label="Description")
                 product_link = gr.State()
-                # button_go_to_page = gr.Button(value="Go to product page")
                 page = gr.HTML()
 
         
@@ -255,15 +265,21 @@ with gr.Blocks(theme=theme, title="New Look", css=css) as demo:
             gr.Markdown()
             gr.Markdown()
 
-        def on_change_dataset(choice):
-            index_name_state = ""
-            if choice == "New Look Dresses":
-                index_name_state = "new_look_expanded_dresses"
-            elif choice == "New Look All":
-                index_name_state = "new_look_expanded_all"
-            return index_name_state
-        
-        select_dataset_button.click(on_change_dataset, list_datasets, index_name_state)
+    def on_change_dataset(choice):
+        index_name = ""
+        if choice == "New Look Dresses":
+            index_name = "new_look_expanded_dresses"
+        elif choice == "New Look All":
+            index_name = "new_look_expanded_all"
+
+        print("Dataset selected: " + index_name)
+        client_obj.set_index_name(index_name)
+
+        time.sleep(0.5)
+
+        return choice
+    
+    select_dataset_button.click(on_change_dataset, list_datasets, list_datasets)
 
     def load(image_input):
         if image_input != None: