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app.py

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+import torch
+import torch.nn as nn
+import torchvision
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+
+weights = torchvision.models.DenseNet169_Weights.DEFAULT
+dense_tranform = weights.transforms()
+
+transfermodel = torchvision.models.densenet169(weights = weights)
+transfermodel.classifier = nn.Sequential(nn.Linear(1664, 800), nn.ReLU(),
+                                          nn.Linear(800, 400), nn.ReLU(),
+                                 nn.Linear(400, 2))
+transfermodel.load_state_dict(torch.load('transfermodel.pth'))
+
+class_names=['NORMAL', 'PNEUMONIA']
+
+def predict(img):
+    img = dense_tranform(img).unsqueeze(0)
+
+    transfermodel.eval()
+    transfermodel.to("cpu")
+    with torch.inference_mode():
+        pred_probs = torch.softmax(transfermodel(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

transfermodel.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:955ec03a73eee282488340f1c9c46bbc45721caf93f558babe378f0031fe064c
+size 57555145