Upload 2 files

app.py

@@ -0,0 +1,72 @@
+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%}")

cifar10_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1323f6ba07e72febc1e1736184c24752394c58f6611c95f1d21f70e733c52f76
+size 8491976