Create v1.app.py

v1.app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+from PIL import Image
+import onnx
+from io import BytesIO
+
+class SimpleNN(nn.Module):
+    def __init__(self):
+        super(SimpleNN, self).__init__()
+        self.fc = nn.Linear(28 * 28, 10)  # Assuming 28x28 input and 10 classes
+
+    def forward(self, x):
+        x = x.view(-1, 28 * 28)
+        x = self.fc(x)
+        return x
+
+st.title("PyTorch Neural Network Interface")
+
+uploaded_file = st.file_uploader("Choose an ONNX model file", type="onnx")
+
+if uploaded_file:
+    byte_stream = BytesIO(uploaded_file.getvalue())
+    model = onnx.load(byte_stream)
+    st.write("Model uploaded successfully!")
+
+if st.button('Download Model as ONNX'):
+    buffer = BytesIO()
+    torch.onnx.export(SimpleNN(), torch.randn(1, 28, 28), buffer)
+    st.download_button(
+        label="Download ONNX model",
+        data=buffer,
+        file_name="model.onnx",
+        mime="application/octet-stream"
+    )