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starterdex.py
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starterdex.py
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import streamlit as st
from PIL import Image
from model import predict
import torch.nn as nn
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
class ImageClassificationBase(nn.Module):
def training_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate predictions
loss = F.cross_entropy(out, labels) # Calculate loss
acc = accuracy(out, labels) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}".format(
epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_acc']))
def conv_block(in_channels, out_channels, pool=False):
layers = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)]
if pool: layers.append(nn.MaxPool2d(2))
return nn.Sequential(*layers)
class ResNet9(ImageClassificationBase):
def __init__(self, in_channels, num_classes):
super().__init__()
self.conv1 = conv_block(in_channels, 64)
self.conv2 = conv_block(64, 128, pool=True)
self.res1 = nn.Sequential(conv_block(128, 128), conv_block(128, 128))
self.conv3 = conv_block(128, 256, pool=True)
self.conv4 = conv_block(256, 512, pool=True)
self.res2 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))
self.classifier = nn.Sequential(nn.MaxPool2d(4),
nn.Flatten(),
nn.Linear(512, num_classes))
def forward(self, xb):
out = self.conv1(xb)
out = self.conv2(out)
out = self.res1(out) + out
out = self.conv3(out)
out = self.conv4(out)
out = self.res2(out) + out
out = self.classifier(out)
return out
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title("Starterdex")
st.write("""Find out whatever you want to about your starter Pokémon""")
st.write("includes pikachu ( ͡~ ͜ʖ ͡°)")
st.write("")
#newimg = Image.open("starters")
#st.image(newimage, caption='...starters...', use_column_width=True)
st.markdown("![starters](https://bestreamer.com/wp-content/uploads/2019/12/xall-pokemon-starters-by-generation-featured.jpg.pagespeed.ic.f53gW9iLxu.jpg)")
st.write("")
file_up = st.file_uploader("Upload a jpg picture of your starter", type=["png","jpg","jpeg"])
if file_up is not None:
image = Image.open(file_up)
st.image(image, caption='loading predictions ...', use_column_width=True)
st.write("")
st.write("The Pokémon is: ")
labels = predict(file_up)
# print out the top 3 prediction labels with scores
for i in labels:
st.write("", i[0], ", chances:", i[1])