updated files

README.md

@@ -1,37 +1,37 @@
----
-title: Quickdraw
-emoji: 💻
-colorFrom: blue
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
+---
+title: Quickdraw
+emoji: 💻
+colorFrom: blue
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

@@ -1,43 +1,43 @@
-from pathlib import Path
-
-import torch
-import gradio as gr
-from torch import nn
-
-
-LABELS = Path('class_names.txt').read_text().splitlines()
-
-model = nn.Sequential(
-    nn.Conv2d(1, 32, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Conv2d(32, 64, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Conv2d(64, 128, 3, padding='same'),
-    nn.ReLU(),
-    nn.MaxPool2d(2),
-    nn.Flatten(),
-    nn.Linear(1152, 256),
-    nn.ReLU(),
-    nn.Linear(256, len(LABELS)),
-)
-state_dict = torch.load('pytorch_model.bin', map_location='cpu')
-model.load_state_dict(state_dict, strict=False)
-model.eval()
-
-def predict(im):
-    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
-
-    with torch.no_grad():
-        out = model(x)
-
-    probabilities = torch.nn.functional.softmax(out[0], dim=0)
-
-    values, indices = torch.topk(probabilities, 5)
-
-    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
-
-
-interface = gr.Interface(predict, inputs='sketchpad', outputs='label', theme="default", description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", live=True)
-interface.launch(debug=True)
+from pathlib import Path
+
+import torch
+import gradio as gr
+from torch import nn
+
+
+LABELS = Path('class_names.txt').read_text().splitlines()
+
+model = nn.Sequential(
+    nn.Conv2d(1, 32, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(32, 64, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Conv2d(64, 128, 3, padding='same'),
+    nn.ReLU(),
+    nn.MaxPool2d(2),
+    nn.Flatten(),
+    nn.Linear(1152, 256),
+    nn.ReLU(),
+    nn.Linear(256, len(LABELS)),
+)
+state_dict = torch.load('pytorch_model.bin', map_location='cpu')
+model.load_state_dict(state_dict, strict=False)
+model.eval()
+
+def predict(im):
+    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
+
+    with torch.no_grad():
+        out = model(x)
+
+    probabilities = torch.nn.functional.softmax(out[0], dim=0)
+
+    values, indices = torch.topk(probabilities, 5)
+
+    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+
+
+interface = gr.Interface(predict, inputs='sketchpad', outputs='label', theme="default", description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", live=True)
+interface.launch(debug=True)

class_names.txt

@@ -1,100 +1,100 @@
-airplane
-alarm_clock
-anvil
-apple
-axe
-baseball
-baseball_bat
-basketball
-beard
-bed
-bench
-bicycle
-bird
-book
-bread
-bridge
-broom
-butterfly
-camera
-candle
-car
-cat
-ceiling_fan
-cell_phone
-chair
-circle
-clock
-cloud
-coffee_cup
-cookie
-cup
-diving_board
-donut
-door
-drums
-dumbbell
-envelope
-eye
-eyeglasses
-face
-fan
-flower
-frying_pan
-grapes
-hammer
-hat
-headphones
-helmet
-hot_dog
-ice_cream
-key
-knife
-ladder
-laptop
-light_bulb
-lightning
-line
-lollipop
-microphone
-moon
-mountain
-moustache
-mushroom
-pants
-paper_clip
-pencil
-pillow
-pizza
-power_outlet
-radio
-rainbow
-rifle
-saw
-scissors
-screwdriver
-shorts
-shovel
-smiley_face
-snake
-sock
-spider
-spoon
-square
-star
-stop_sign
-suitcase
-sun
-sword
-syringe
-t-shirt
-table
-tennis_racquet
-tent
-tooth
-traffic_light
-tree
-triangle
-umbrella
-wheel
-wristwatch
+airplane
+alarm_clock
+anvil
+apple
+axe
+baseball
+baseball_bat
+basketball
+beard
+bed
+bench
+bicycle
+bird
+book
+bread
+bridge
+broom
+butterfly
+camera
+candle
+car
+cat
+ceiling_fan
+cell_phone
+chair
+circle
+clock
+cloud
+coffee_cup
+cookie
+cup
+diving_board
+donut
+door
+drums
+dumbbell
+envelope
+eye
+eyeglasses
+face
+fan
+flower
+frying_pan
+grapes
+hammer
+hat
+headphones
+helmet
+hot_dog
+ice_cream
+key
+knife
+ladder
+laptop
+light_bulb
+lightning
+line
+lollipop
+microphone
+moon
+mountain
+moustache
+mushroom
+pants
+paper_clip
+pencil
+pillow
+pizza
+power_outlet
+radio
+rainbow
+rifle
+saw
+scissors
+screwdriver
+shorts
+shovel
+smiley_face
+snake
+sock
+spider
+spoon
+square
+star
+stop_sign
+suitcase
+sun
+sword
+syringe
+t-shirt
+table
+tennis_racquet
+tent
+tooth
+traffic_light
+tree
+triangle
+umbrella
+wheel
+wristwatch