add app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms
+
+import torchxrayvision as xrv
+
+
+def classify_image(img, model_name):
+
+    model = xrv.models.get_model(model_name, from_hf_hub=True)
+
+    img = xrv.datasets.normalize(img, 255)
+
+    # Check that images are 2D arrays
+    if len(img.shape) > 2:
+        img = img[:, :, 0]
+    if len(img.shape) < 2:
+        print("error, dimension lower than 2 for image")
+
+    # Add color channel
+    img = img[None, :, :]
+
+    transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])
+
+    img = transform(img)
+
+    with torch.no_grad():
+        img = torch.from_numpy(img).unsqueeze(0)
+        preds = model(img).cpu()
+        output = {
+            k: float(v)
+            for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
+        }
+        return output
+
+
+gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(shape=(224, 224), image_mode="L"),
+        gr.Dropdown(
+            [
+                "densenet121-res224-all",
+                "densenet121-res224-nih",
+                "densenet121-res224-pc",
+                "densenet121-res224-chex",
+                "densenet121-res224-rsna",
+                "densenet121-res224-mimic_nb",
+                "densenet121-res224-mimic_ch",
+                "resnet50-res512-all",
+            ],
+            value="densenet121-res224-all",
+            type="value",
+            label="Pre-trained model",
+        ),
+    ],
+    outputs=gr.outputs.Label(),
+    title="Classify chest x-ray image",
+    examples=[
+        ["16747_3_1.jpg", "densenet121-res224-all"],
+        ["00000001_000.png", "resnet50-res512-all"],
+    ],
+).launch()