small shifts

create_games/data/game_ideas.json

@@ -1,7 +1,7 @@
 [
     {
-        "Name": "Family Portraits",
-        "Description": "Each player takes turns drawing a portrait of their family. The other players must guess who is in the portrait. The player with the most correct guesses wins.",
-        "Rationale": "This game encourages creativity and allows players to learn more about each other's families."
+        "Name": "Food Frenzy",
+        "Description": "Players take turns drawing a food item on a piece of paper. The other player must guess what the food item is. The player who guesses correctly gets a point. The player with the most points at the end of the game wins.",
+        "Rationale": "This game is simple and easy to play, and encourages creativity and imagination. It also allows for a lot of fun and friendly competition between the two players."
     }
 ]

home_view/ui_home.py

@@ -77,7 +77,7 @@ with gr.Blocks() as ui:
         logo=gr.Image(value=constants.BUILDPLAY_LOGO, width=200, height=200, show_download_button=False, container=False) 
     directions= gr.Markdown(constants.DIRECTIONS, container=False)    
     with gr.Column(): 
-        playersCB = gr.CheckboxGroup ([], type="value", label=constants.PLAYERS, info=constants.PLAYERS_INFO )
+        playersCB = gr.CheckboxGroup ([], label=constants.PLAYERS, info=constants.PLAYERS_INFO )
     createBtn = gr.Button(value=constants.CREATE_GAME, variant="primary")
     gameTitle=gr.Markdown(constants.GAME_TITLE, container=False)
     desc=gr.TextArea(info=constants.GAME_DESC, interactive=False, show_label=False,

requirements.txt

@@ -1,4 +1,4 @@
-pip>=21.3
+pip>=23.3
 requests==2.31.0
 pylance==0.5.10
 pandas==2.0.3
@@ -9,8 +9,8 @@ scikit-learn==1.2.2
 plotly.express==0.4.1 
 openai==0.27.8
 openapi-schema-pydantic==1.2.4
-gradio==3.39.0
-gradio_client==0.3.0
+gradio==3.48.0
+gradio_client==0.6.1
 GitPython==3.1.31
 elevenlabs==0.2.18
 python-dotenv==1.0.0

sketch/sketch.py

@@ -27,23 +27,34 @@ model.load_state_dict(state_dict, strict=False)
 model.eval()
 
 def Predict(img):
+    if img is None or not img.any():
+        print("img is None or empty")
+        # Handle the error appropriately
+        return
+
     # Convert to grayscale if the image is RGB
-    if img is not None and img.any():
-        if img.shape[-1] == 3:
-            img = np.mean(img, axis=-1)
+    if img.shape[-1] == 3:
+        img = np.mean(img, axis=-1)
+
     # Convert the NumPy array to a PIL image
     img_pil = Image.fromarray(img.astype('uint8'))
+
     # Resize the image
-    img_resized = img_pil.resize((28, 28), Image.ANTIALIAS) 
+    img_resized = img_pil.resize((28, 28), Image.ANTIALIAS)
+
     # Convert the PIL image back to a NumPy array
     img_np = np.array(img_resized)
+
     # Convert to tensor and normalize
     x = torch.tensor(img_np, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0
+
     # Model prediction
     with torch.no_grad():
         out = model(x)
+
     probabilities = torch.nn.functional.softmax(out[0], dim=0)
     values, indices = torch.topk(probabilities, 5)
     confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)}
+
     return confidences