initial commit

.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

app.py

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+import torch
+import torchvision.transforms as transforms
+import gradio as gr
+from model import load_model
+
+CLASS_NAMES = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia']
+MODEL_PATH = "chexnet_epoch_17_auc_0.8457.pth"
+
+# Load model
+model = load_model(MODEL_PATH)
+
+# Define the image transformation pipeline
+def transform_image(image):
+    transformation_pipeline = transforms.Compose([
+        transforms.Resize(256),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ])
+    return transformation_pipeline(image).unsqueeze(0)
+
+# Define the prediction function
+def predict(image):
+    pred = []
+    img_tensor = transform_image(image)
+    with torch.no_grad():
+        output = model(img_tensor)
+        values = output.squeeze().tolist()
+        prediction = torch.nn.functional.sigmoid(output).squeeze().tolist()
+    for i in range(len(CLASS_NAMES)):
+        pred.append({"disease": CLASS_NAMES[i], "model_value": values[i], "sigmoid_value": prediction[i]})
+    return pred
+
+# Create Gradio interface
+demo = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.JSON())
+demo.launch(share=True)

chexnet_epoch_17_auc_0.8457.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea29519adc4db0edffa8291165d1bb190462162a4e5b972aac140602f3673b4d
+size 28513799

model.py

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+import torch
+import torch.nn as nn
+from torchvision import models as models
+
+class DenseNet121(nn.Module):
+    """Model modified.
+
+    The architecture of our model is the same as standard DenseNet121
+    except the classifier layer which has an additional sigmoid function.
+
+    """
+    def __init__(self, out_size):
+        super(DenseNet121, self).__init__()
+        self.densenet121 = models.densenet121(weights=models.DenseNet121_Weights.DEFAULT)
+        num_ftrs = self.densenet121.classifier.in_features
+        self.densenet121.classifier = nn.Sequential(
+            nn.Linear(num_ftrs, out_size),
+            # nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = self.densenet121(x)
+        return x
+
+def load_model(ckpt_path, n_classes=14):
+    model = DenseNet121(n_classes).cpu()
+    print("=> loading checkpoint")
+    checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'), weights_only=True)
+    new_state_dict = {}
+    for key, value in checkpoint.items():
+        new_key = key.replace("module.", "")  # Remove 'module.' from keys
+        new_state_dict[new_key] = value
+
+    model.load_state_dict(new_state_dict)
+    print("=> loaded checkpoint")
+    model.eval()
+    return model
+

requirements.txt

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+torch
+torchvision
+gradio
+Pillow