Jay

README.md

@@ -4,7 +4,7 @@ emoji: 🤸
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.48.0
+sdk_version: 3.40.1
 app_file: app.py
 pinned: false
 ---

create_games/data/game_ideas.json

@@ -1,7 +1,7 @@
 [
     {
-        "Name": "Sunburn Showdown",
-        "Description": "Players take turns drawing a sunburn-related scene. The other player must guess what the scene is. The first player to guess correctly wins the round.",
-        "Rationale": "This game is simple and easy to play, and encourages creativity and imagination."
+        "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."
     }
 ]

home_view/ui_home.py

@@ -25,7 +25,7 @@ def FetchData():
     return startDF, slackDF, embeddingsDF, playersDF, gamesDF
 
 def Init():
-    global startDF, slackDF, embeddingsDF, gamesDF,playersDF,playersValues,logoHtml
+    global startDF, slackDF, embeddingsDF, gamesDF,playersDF,playersValues
     startDF=FetchSlack()
     slackDF=ProcessSlack()
     embeddingsDF=CreateEmbeddings()
@@ -72,12 +72,12 @@ df3 =gr.Dataframe(type="pandas", value=embeddingsDF)
 df4 =gr.Dataframe(type="pandas", value= playersDF)
 df5 =gr.Dataframe(type="pandas", value=gamesDF)
 
-with gr.Blocks() as ui:    
-    with gr.Column():
-        logo=gr.Image(value="home_view/art/logo.png", interactive=False, container=False,show_download_button=False, show_label=False)
+with gr.Blocks() as ui:
+    with gr.Row():
+        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 ([], label=constants.PLAYERS, info=constants.PLAYERS_INFO )
+        playersCB = gr.CheckboxGroup ([], type="value", 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,
@@ -88,15 +88,14 @@ with gr.Blocks() as ui:
                         brush_radius=.5, type="numpy", label="Sketch", show_label=True, show_download_button=False) 
         label=gr.Label()
     sketch.change(Predict, inputs=[sketch], outputs=[label])
-    playersCB.change(SetSelected, inputs=[playersCB], outputs=[])
-    createBtn.click(CreateGame, inputs=[], outputs=[df4,desc,gameTitle])
-    playBtn.click(PlayGame, inputs=[], outputs=[holder])
 
 with gr.Blocks() as dfs:
     fetchBtn=gr.Button(value=constants.FETCH_DATA, variant="primary")    
     gr.TabbedInterface([df1,df2,df3,df4,df5], (constants.UI1, constants.UI2, constants.UI3, constants.UI4, constants.UI5), theme=SoftBlue())
     fetchBtn.click(FetchData, inputs=[], outputs=[df1,df2,df3,df4,df5])
-
+    playersCB.change(SetSelected, inputs=[playersCB], outputs=[])
+    createBtn.click(CreateGame, inputs=[], outputs=[df4,desc,gameTitle])
+    playBtn.click(PlayGame, inputs=[], outputs=[holder])
 
 combo=gr.TabbedInterface([ui,dfs], ("BuildPlay", "DataFrames"),theme=SoftBlue())
 

requirements.txt

@@ -1,4 +1,4 @@
-pip>=23.3
+pip>=21.3
 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==3.48.0
-gradio_client==0.6.1
+gradio==3.39.0
+gradio_client==0.3.0
 GitPython==3.1.31
 elevenlabs==0.2.18
 python-dotenv==1.0.0

sketch/sketch.py

@@ -27,34 +27,23 @@ 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.shape[-1] == 3:
-        img = np.mean(img, axis=-1)
-
+    if img is not None and img.any():
+        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
 

utilities/constants.py

@@ -10,11 +10,10 @@ NO_GAME="No game created"
 NO_GAME_TITLE="Game Title: No game created"
 PLAY_GAME="Play Game"
 BUILDPLAY_LOGO="home_view/art/buildPlay.svg"
-
 FETCH_DATA="Fetch data"
 
 UI1="Slack"
-UI2="Embeddings"
-UI3="Players"
-UI4="Games"
-UI5="Game Ideas"
+UI2="Themes"
+UI3="Embeddings"
+UI4="Topics by Player"
+UI5="Games"