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import gradio as gr | |
import torch | |
from torchvision import transforms | |
import numpy as np | |
import os | |
import huggingface_hub | |
from net import Net | |
if torch.cuda.is_available(): | |
device = torch.device("cuda:0") | |
else: | |
device = torch.device("cpu") | |
net = Net() | |
net.to(device) | |
HF_Token = os.environ['HF_Token'] | |
model = huggingface_hub.cached_download(huggingface_hub.hf_hub_url( | |
'danyalmalik/sceneryclassifier', '1655684183.7481008_Acc0.87_modelweights.pth'), use_auth_token=HF_Token) | |
net.load_state_dict(torch.load(model, map_location=device)) | |
mean = np.array([0.5, 0.5, 0.5]) | |
std = np.array([0.25, 0.25, 0.25]) | |
data_transforms = transforms.Compose([ | |
transforms.Resize((150, 150)), | |
transforms.ToTensor(), | |
transforms.Normalize(mean, std)]) | |
labels = ['Buildings', 'Forest', 'Glacier', 'Mountain', 'Sea', 'Street'] | |
title = "Scenery Classifier" | |
def examples(): | |
number = 8 | |
for i in range(number): | |
imgs = os.listdir('examples') | |
egs = [os.path.join('examples/', eg) for eg in imgs] | |
return egs | |
def predict(img): | |
try: | |
img = data_transforms(img) | |
img = img.to(device) | |
with torch.no_grad(): | |
output = net(img) | |
pred = [output[0][i].item() for i in range(len(labels))] | |
except: | |
pred = [0 for i in range(len(labels))] | |
weightage = {labels[i]: pred[i] for i in range(len(labels))} | |
return weightage | |
gr.Interface(fn=predict, inputs=gr.Image(shape=(150, 150), type='pil'), | |
outputs='label', title=title, examples=examples()).launch() | |