First commit.

PrHu_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3939b00c41628df6f9a55ba40dbab49fec289067d951a247a126d4186ce374e7
+size 2344394

app.py

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+import gradio as gr
+from torch import nn
+from torch.optim.swa_utils import AveragedModel
+import torch
+import gradio as gr
+import os
+import tempfile
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+# preprocessing
+transforms = A.Compose([
+    A.LongestMaxSize(384),
+    A.CLAHE(),
+    A.Normalize(normalization='image'),
+    A.PadIfNeeded(384, 384, border_mode=0, value=(0)),
+    ToTensorV2()
+])
+
+# model
+class PrHu_model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.configuration = transformers.ConvNextV2Config(num_channels=1, drop_path_rate=0, image_size=384, num_labels=1,
+                                                           depths=[2, 2, 6, 2], hidden_sizes=[16, 32, 64, 128])
+        self.model = transformers.ConvNextV2ForImageClassification(self.configuration)
+
+    def forward(self, x):
+        # print ('starting model F pass')
+        return self.model(x).logits
+    
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = nn.DataParallel(PrHu_model()).to(device)
+model.load_state_dict(torch.load('PrHu_model.pth', map_location=device))
+mode.eval()
+
+def inference(img_dir):
+   image = np.array(Image.open(str(img_dir)).convert('L'))
+    image = transforms(image=image)['image']
+    with torch.inference_mode():
+        out = model(image).item()
+    out = out > 0
+    return "Fracture +" if out else "Fracture -"
+
+examples = ["NF1.jpg", "NF2.jpg", "NF3.jpg", "F1.jpg", "F2.jpg", "F3.jpg"]
+
+#UI
+iface = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.File(label="Upload Input Image"),
+    ],
+    outputs=gr.Textbox(label="Classification Result"),
+    title="Proximal Humerus Fracture Detection",
+    description="Upload an image, and get the classification result.",
+    examples=examples  # Add example inputs
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+albumentations
+torch
+torchmetrics
+torchvision
+matplotlib
+numpy
+transformers
+sklearn
+pandas
+h5py
+tqdm
+pickle
+os