Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Pictionary_with_Gradio.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17zR3z7tyss0M9EpMfcM7DSzG-WdE3-Kc
+"""
+
+import torch
+from torch import nn
+from pathlib import Path
+
+!pip3 install gradio
+
+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')
+
+import gradio as gr
+
+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="huggingface",
+    title="Sketch Recognition",
+    description="Sketch something for the model to guess in realtime",
+    article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
+    live=True)
+interface.launch(share=True)
+