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import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from PIL import Image
import io
# Set page config
st.set_page_config(page_title="CIFAR-10 Classifier", layout="wide", initial_sidebar_state="expanded")
# Custom CSS for dark theme
st.markdown("""
<style>
.stApp {
background-color: #0E1117;
color: #FAFAFA;
}
.stButton>button {
background-color: #4CAF50;
color: white;
}
.stHeader {
background-color: #262730;
color: white;
padding: 1rem;
border-radius: 5px;
margin-bottom: 1rem;
}
.stImage {
background-color: #262730;
padding: 10px;
border-radius: 5px;
}
.stSuccess {
background-color: #262730;
color: #4CAF50;
padding: 10px;
border-radius: 5px;
margin-top: 1rem;
}
.upload-box {
border: 2px dashed #4CAF50;
border-radius: 5px;
padding: 20px;
text-align: center;
cursor: pointer;
}
</style>
""", unsafe_allow_html=True)
# Model definition
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 64 * 8 * 8)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# Function to train the model
@st.cache_resource
def train_model():
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(5): # Train for 5 epochs
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
return model
# Function to load or train the model
@st.cache_resource
def get_model():
try:
model = SimpleCNN()
model.load_state_dict(torch.load('cifar10_model.pth'))
model.eval()
except:
model = train_model()
torch.save(model.state_dict(), 'cifar10_model.pth')
return model
# Sidebar
st.sidebar.title("Upload Image")
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Main content
st.markdown("<h1 class='stHeader'>CIFAR-10 Image Classification</h1>", unsafe_allow_html=True)
# Drag and drop section
col1, col2, col3 = st.columns([1,2,1])
# with col2:
# st.markdown("<div class='upload-box'>Drag and drop image here</div>", unsafe_allow_html=True)
# Display uploaded image and make prediction
if uploaded_file is not None:
image = Image.open(uploaded_file)
col1, col2, col3 = st.columns([1,2,1])
with col2:
st.markdown("<div class='stImage'>", unsafe_allow_html=True)
st.image(image, caption='Uploaded Image', use_column_width=True)
st.markdown("</div>", unsafe_allow_html=True)
# Load model and make prediction
model = get_model()
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
input_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(input_tensor)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Display results in sidebar
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
_, predicted = torch.max(output, 1)
st.sidebar.markdown("<div class='stSuccess'>", unsafe_allow_html=True)
st.sidebar.write(f"Best Prediction: {classes[predicted.item()]}")
st.sidebar.markdown("</div>", unsafe_allow_html=True)
st.sidebar.write("Prediction Probabilities:")
for i, prob in enumerate(probabilities):
st.sidebar.write(f"{classes[i]}: {prob.item():.2%}")
# Footer
st.markdown("---")
st.markdown("<p style='text-align: center; color: #666;'>Created with Streamlit and PyTorch</p>", unsafe_allow_html=True) |