Upload 3 files

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 ConvModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.cnn1 = nn.Sequential(
+            nn.Conv2d(3, 16, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.cnn2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.fc = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(32 * 56 * 56, 2)
+        )
+
+    def forward(self, x):
+        x = self.cnn1(x)
+        x = self.cnn2(x)
+        x = self.fc(x)
+        return x
+
+model = ConvModel()
+model.load_state_dict(torch.load("conv_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)

conv_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6285af0ba99bb78b83f8bdef57250483b1737d9d3abcc62e1641ef19f0c3fb7
+size 825652

requirements.txt

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+torch
+gradio
+torchvision
+pillow