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#2022aug31
#import gradio as gr
#def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
# Setting up the Sketch Recognition Model
import torch
from torch import nn
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()
# Defining a predict function
from pathlib import Path
LABELS = Path('class_names.txt').read_text().splitlines()
def predict(img):
x = torch.tensor(img, 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)
confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)}
return confidences
# Creating a Gradio Interface
import gradio as gr
gr.Interface(fn=predict,
inputs="sketchpad",
outputs="label",
live=True).launch()
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