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app.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import gradio as gr
+from PIL import Image
+
+class VGG19(nn.Module):
+    def __init__(self):
+        super(VGG19, self).__init__()
+
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, 64, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 128, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+            nn.Conv2d(128, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+            nn.Conv2d(256, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * 7 * 7, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.5),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.5),
+            nn.Linear(4096, 1000)
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x
+
+model = VGG19()
+model.load_state_dict(torch.load("vgg_model.pth", map_location="cpu"))
+model.eval()
+
+class_names=['NORMAL', 'PNEUMONIA']
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def predict(img):
+    img = transform(img).unsqueeze(0)
+    with torch.inference_mode():
+        pred_probs = torch.softmax(model(img), dim=1)
+
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    return pred_labels_and_probs
+
+
+title = "Zatürre Bulucu"
+description = "Gönderilen fotoğrafa göre Sağlıklı mı yoksa Zatürre mi olduğunu tahmin eder."
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes=2, label="Predictions")],
+    title=title,
+    description=description
+)
+
+demo.launch(debug=False, share=True)

requirements.txt

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+torch
+gradio
+torchvision
+pillow

vgg_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2518ae212cad272b9d167ea726a07db4300548368d2a48f0fe9c6bbb61acc03
+size 574680144