Duplicate from gradio/pictionary

Co-authored-by: Ali Abdalla <aliabd@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,11 @@
+---
+title: pictionary
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.18.0
+app_file: run.py
+pinned: false
+duplicated_from: gradio/pictionary
+---

class_names.txt

@@ -0,0 +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

requirements.txt

@@ -0,0 +1,2 @@
+torch
+gdown

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: pictionary"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch gdown"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/pictionary/class_names.txt"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from pathlib import Path\n", "\n", "import torch\n", "import gradio as gr\n", "from torch import nn\n", "import gdown \n", "\n", "url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'\n", "output = 'pytorch_model.bin'\n", "gdown.download(url, output, quiet=False)\n", "\n", "LABELS = Path('class_names.txt').read_text().splitlines()\n", "\n", "model = nn.Sequential(\n", "    nn.Conv2d(1, 32, 3, padding='same'),\n", "    nn.ReLU(),\n", "    nn.MaxPool2d(2),\n", "    nn.Conv2d(32, 64, 3, padding='same'),\n", "    nn.ReLU(),\n", "    nn.MaxPool2d(2),\n", "    nn.Conv2d(64, 128, 3, padding='same'),\n", "    nn.ReLU(),\n", "    nn.MaxPool2d(2),\n", "    nn.Flatten(),\n", "    nn.Linear(1152, 256),\n", "    nn.ReLU(),\n", "    nn.Linear(256, len(LABELS)),\n", ")\n", "state_dict = torch.load('pytorch_model.bin', map_location='cpu')\n", "model.load_state_dict(state_dict, strict=False)\n", "model.eval()\n", "\n", "def predict(input):\n", "    im = input\n", "    if im is None:\n", "        return None\n", "        \n", "    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.\n", "\n", "    with torch.no_grad():\n", "        out = model(x)\n", "\n", "    probabilities = torch.nn.functional.softmax(out[0], dim=0)\n", "\n", "    values, indices = torch.topk(probabilities, 5)\n", "\n", "    return {LABELS[i]: v.item() for i, v in zip(indices, values)}\n", "\n", "\n", "interface = gr.Interface(predict, inputs=gr.templates.Sketchpad(label=\"Draw Here\"), outputs=gr.Label(label=\"Guess\"), theme=\"default\", css=\".footer{display:none !important}\", live=True)\n", "interface.launch(enable_queue=False)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -0,0 +1,51 @@
+from pathlib import Path
+
+import torch
+import gradio as gr
+from torch import nn
+import gdown 
+
+url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'
+output = 'pytorch_model.bin'
+gdown.download(url, output, quiet=False)
+
+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(input):
+    im = input
+    if im is None:
+        return None
+        
+    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=gr.templates.Sketchpad(label="Draw Here"), outputs=gr.Label(label="Guess"), theme="default", css=".footer{display:none !important}", live=True)
+interface.launch(enable_queue=False)