Upload 3 files

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+# In[3]:
+
+
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        # Modify fc1 to match the size in the saved checkpoint
+        self.fc1 = nn.Linear(400, 120)
+        # Modify fc2 to match the size in the saved checkpoint
+        self.fc2 = nn.Linear(120, 84)
+        # Modify fc3 to match the size in the saved checkpoint
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = x.view(x.shape[0], -1)
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+        
+# Load the trained model
+model = Net()
+model.load_state_dict(torch.load("cifar_net.pth"))
+model.eval()
+
+# Define the transformation to be applied to input images
+preprocess = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((32, 32)),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+# Define the CIFAR-10 class names
+classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+# Define a function to make predictions on input images
+def classify_image(image):
+    img_tensor = preprocess(image)
+    img_tensor = img_tensor.unsqueeze(0)
+    output = model(img_tensor)
+    _, predicted = torch.max(output, dim=1)
+    return classes[predicted[0]]  # Return as a list
+
+# Create Gradio interface
+iface = gr.Interface(fn=classify_image, inputs="image", outputs="text")
+
+# Launch the interface
+iface.launch()
+
+
+# In[ ]:
+
+
+
+

cifar_net.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a39b10e26fc76a2d0f097dfd792ff7dac5a7c79ecbb1732017a726f3c1fafdc5
+size 251167

requirements.txt

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+# Usage :pip install -r requiemnets.txt
+
+gradio == 3.36.1
+pillow == 9.5.0
+torch == 2.0.1
+torchvision == 0.15.2
+image == 1.5.33