Add app files

app.py

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+import torch
+from torch import nn
+import gradio as gr
+from torchvision.transforms import Resize, ToTensor, Compose
+from torch.nn.functional import softmax
+
+class myCNN(nn.Module):
+    def __init__(self, input_channels, classes) -> None:
+        super().__init__()
+        self.layer1 = nn.Sequential(nn.Conv2d(in_channels=input_channels, out_channels=64, kernel_size=(3,3), padding='valid', bias=False),
+                                    nn.BatchNorm2d(num_features=64),
+                                    nn.ReLU())
+        self.layer2 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), padding='valid', bias=False),
+                                    nn.BatchNorm2d(num_features=64),
+                                    nn.ReLU())
+        self.layer3 = nn.Sequential(nn.MaxPool2d((2,2)),
+                                    nn.Dropout2d(0.4))
+
+        self.layer4 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), padding='valid', bias=False),
+                                    nn.BatchNorm2d(num_features=128),
+                                    nn.ReLU())
+        self.layer5 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3,3), padding='valid', bias=False),
+                                    nn.BatchNorm2d(num_features=128),
+                                    nn.ReLU())
+        self.layer6 = nn.Sequential(nn.MaxPool2d((2,2)),
+                                    nn.Dropout2d(0.4))
+        self.flat = nn.Flatten()
+
+        self.fc1 = nn.Sequential(nn.Linear(3200, 512),
+                                 nn.ReLU(),
+                                 nn.Dropout1d(0.5))
+
+        self.fc2 = nn.Sequential(nn.Linear(512, 256),
+                                 nn.ReLU())
+
+        self.fc3 = nn.Linear(256, classes)
+
+    def forward(self, x):
+        layer1 = self.layer1(x)
+        layer2 = self.layer2(layer1)
+        layer3 = self.layer3(layer2)
+        layer4 = self.layer4(layer3)
+        layer5 = self.layer5(layer4)
+        layer6 = self.layer6(layer5)
+        flat = self.flat(layer6)
+        fc1 = self.fc1(flat)
+        fc2 = self.fc2(fc1)
+        fc3 = self.fc3(fc2)
+        return fc3
+    
+
+device = 'gpu' if torch.cuda.is_available() else 'cpu'
+
+model_state = torch.load("myCNN_states.pt", map_location=device, weights_only=False)
+input_shape = model_state['input_shape']
+cls_to_idx = model_state['labels_encoder']
+idx_to_cls = {value:key for key,value in cls_to_idx.items()}
+
+pre_processor = Compose([Resize(input_shape[1:]),
+                         ToTensor()])
+
+model = torch.load("myCNN.bin",
+                    map_location=device,
+                    weights_only=False)
+
+def post_processor(raw_output):
+    softmax_output = softmax(raw_output, -1)
+    values, indices = torch.max(softmax_output, -1)
+    return idx_to_cls[indices.item()].capitalize(), round(values.item(), 2)
+
+
+@torch.no_grad
+def lunch(raw_input):
+    input = pre_processor(raw_input)
+    output = model(input.unsqueeze(0))
+    return post_processor(output)
+
+custom_css ='.gr-button {background-color: #bf4b04; color: white;}'
+
+with gr.Blocks(css=custom_css) as demo:
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label='Input Image')
+            gr.Text("Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck", label="Supported Classes:")
+        with gr.Column():
+            class_name = gr.Textbox(label="This is (a\\an)")
+            confidence = gr.Textbox(label='Confidence')
+            start_btn = gr.Button(value='Submit', elem_classes=["gr-button"])
+    start_btn.click(fn=lunch, inputs=input_image, outputs=[class_name, confidence])
+
+demo.launch()

myCNN.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2510e5ef54bc911f9daf0d8efabe79b2ba1aca62a8e988ba5aca292826e59b01
+size 8152632

myCNN_states.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1c54e6595788d3bb34e76db6c064e200a28e7fe1e7d0594ab724363944adfd7e
+size 24429714

requirements.txt

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+torch
+torchvision
+gradio