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.gitattributes

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README.md

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----
-title: Kidneystone Detection
-emoji: 🌍
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 4.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Kidneystone Detection
+emoji: 🌍
+colorFrom: green
+colorTo: purple
+sdk: gradio
+sdk_version: 4.29.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+import torch.nn as nn
+from efficientnet_pytorch import EfficientNet
+import gradio as gr
+
+
+
+# Define the custom model architecture
+class CustomModel(nn.Module):
+    def __init__(self):
+        super(CustomModel, self).__init__()
+        self.fc = nn.Linear(6, 50176)
+        self.fc_bn = nn.BatchNorm1d(50176)
+        self.pretrained_model = EfficientNet.from_pretrained('efficientnet-b0', num_classes=32)
+        self.classification_head = nn.Sequential(
+            nn.Linear(32, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        x = self.fc(x)
+        x = self.fc_bn(x)
+        x = x.view(-1, 224, 224)
+        x = torch.stack([x] * 3, dim=1)
+        x = self.pretrained_model(x)
+        x = self.classification_head(x)
+        return x
+
+# Load the trained model
+model = CustomModel()
+model.load_state_dict(torch.load('best_model_efficientnet_b0.pth'))
+model.eval()
+
+# Load the validation dataset
+#val_dataset = CustomDataset('outside.csv')
+#val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+
+# Function to make prediction
+def predict(feature1, feature2, feature3, feature4, feature5, feature6):
+    features = torch.tensor([[feature1, feature2, feature3, feature4, feature5, feature6]], dtype=torch.float32)
+    output = model(features)
+    prediction = output.round().item()
+    return "Kidney Stone Detected" if prediction == 1 else "No Stone Detected"
+light_blue = "#ADD8E6"
+# Create a Gradio interface
+inputs = [
+    gr.Slider(minimum=0.8, maximum=1.5, label="gravity: Specific Gravity"),  # Using gr.Slider for each feature
+    gr.Slider(minimum=3, maximum=8, label="ph: pH (Potential of Hydrogen)"),
+    gr.Slider(minimum=200, maximum=1200, label="osmo: Osmolality"),
+    gr.Slider(minimum=5, maximum=30, label="cond: Conductivity"),
+    gr.Slider(minimum=50, maximum=700, label="urea: Urea"),
+    gr.Slider(minimum=0, maximum=20, label="calc: Calcium")
+]
+
+output = gr.Label()  # Output label for the prediction
+
+interface = gr.Interface(predict, inputs, output, title="Kidney Stone Detection NOTE- FOR RESEARCH PURPOSE ONLY-",
+                         description="Enter the values for each feature",
+                         css=f".gradio-container {{ background-color: {light_blue} }}"  # Inline CSS injection
+                         )  # Customize interface details
+
+interface.launch()  # Launch the Gradio interface

best_model_efficientnet_b0.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a8d9e0eeee8151f9a7da88b9662f5c0bb41a1595a682ec3ac777c68de74a0f46
+size 18709823

requirements.txt

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+torch
+torchvision
+efficientnet_pytorch
+pandas
+scikit-learn
+gradio