app.py

import streamlit as st
import torch
from torch import nn
import torchvision.transforms as transforms
from PIL import Image
import numpy as np

# Define the model architecture (same as before)
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

# Load the trained model
@st.cache_resource
def load_model():
    model = SimpleCNN()
    model.load_state_dict(torch.load('cifar10_model.pth', map_location=torch.device('cpu')))
    model.eval()
    return model

# Define class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

# Define image transformation
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Streamlit app
st.title('CIFAR-10 Image Classification')

uploaded_file = st.file_uploader("Choose an image...", type="jpg")

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    
    # Preprocess the image
    input_tensor = transform(image).unsqueeze(0)
    
    # Load model and make prediction
    model = load_model()
    with torch.no_grad():
        output = model(input_tensor)
    
    # Get the predicted class
    _, predicted_idx = torch.max(output, 1)
    predicted_class = class_names[predicted_idx.item()]
    
    # Display the result
    st.write(f"Prediction: {predicted_class}")
    
    # Display probabilities
    probabilities = torch.nn.functional.softmax(output[0], dim=0)
    st.write("Class Probabilities:")
    for i, prob in enumerate(probabilities):
        st.write(f"{class_names[i]}: {prob.item():.2%}")