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app.py

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+import torch
+import torch.nn as nn
+import gradio as gr
+import numpy as np
+
+# Define the Simple1DCNN model
+class Simple1DCNN(nn.Module):
+    def __init__(self, input_channels=1, num_classes=5, sequence_length=500):
+        super(Simple1DCNN, self).__init__()
+        self.conv1 = nn.Conv1d(input_channels, 16, kernel_size=5, stride=1, padding=2)
+        self.relu = nn.ReLU()
+        self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
+        self.fc = nn.Linear(16 * (sequence_length // 2), num_classes)
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = x.view(x.size(0), -1)
+        return self.fc(x)
+
+# Load model
+model_path = "ecg.pth"  # Adjust if necessary
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+sequence_length = 500  # Adjust based on training
+model = Simple1DCNN(input_channels=1, num_classes=5, sequence_length=sequence_length).to(device)
+model.load_state_dict(torch.load(model_path, map_location=device))
+model.eval()
+
+# Preprocessing function
+def preprocess_ecg(data):
+    """Convert input ECG data to PyTorch tensor and prepare for inference."""
+    ecg = np.array(data).astype(np.float32)  # Ensure NumPy array format
+    ecg = torch.from_numpy(ecg).unsqueeze(0).unsqueeze(0).to(device)  # Shape: (1, 1, seq_length)
+    return ecg
+
+# Prediction function
+def predict(ecg_data):
+    ecg_tensor = preprocess_ecg(ecg_data)
+    with torch.no_grad():
+        output = model(ecg_tensor)
+    predicted_class = int(output.argmax(dim=1).item())
+
+    # Define class labels
+    class_labels = {0: "Normal", 1: "AFib", 2: "PVC", 3: "ST", 4: "Other"}
+    return class_labels.get(predicted_class, "Unknown")
+
+# Create Gradio interface
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter ECG values separated by commas"),
+    outputs="label",
+    title="ECG Classification",
+    description="Predicts ECG conditions based on input signal.",
+)
+
+# Launch app
+if __name__ == "__main__":
+    app.launch()

ecg.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fb18b9a827a58328596e3d9cf55bd0293afdc3193bc9a05301a6a7808447aad
+size 6138064