Upload 6 files

Model.py

@@ -0,0 +1,27 @@
+import torch
+from torch import nn
+
+# Neural Network
+class LeNet(nn.Module):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        
+        self.convs = nn.Sequential(
+            nn.Conv2d(in_channels=1, out_channels=4, kernel_size=(5, 5)),   
+            nn.Tanh(),                                                      
+            nn.AvgPool2d(2, 2),                                            
+
+            nn.Conv2d(in_channels=4, out_channels=12, kernel_size=(5, 5)), 
+            nn.Tanh(),
+            nn.AvgPool2d(2, 2) 
+        )
+
+        self.linear = nn.Sequential(
+            nn.Linear(4*4*12,10)
+        )
+    
+    def forward(self, x):
+        x = self.convs(x)
+        x = torch.flatten(x, 1)
+
+        return self.linear(x)

README.md

@@ -1,13 +1,12 @@
 ---
-title: Mnist Classifier
-emoji: 🐠
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.26.0
+title: MNIST Classifier
+emoji: 📚
+colorFrom: purple
+colorTo: gray
+sdk: gradio
+sdk_version: 3.24.1
 app_file: app.py
 pinned: false
-license: openrail
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+import torch
+from Model import LeNet
+
+labels = ['Zero','One','Two','Three','Four','Five','Six','Seven','Eight', 'Nine']
+
+# Locate device
+if torch.cuda.is_available():
+    device = torch.device("cuda:0")
+    print("GPU")
+else:
+    device = torch.device("cpu")
+    print("CPU")
+
+# Loading model
+model = LeNet().to(device)
+model.load_state_dict(torch.load("model_mnist.pth", map_location=torch.device('cpu')))
+
+
+def predict(input):
+  input = torch.from_numpy(input.reshape(1, 1, 28, 28)).to(dtype=torch.float32, device=device)
+
+  with torch.no_grad():
+    outputs = model(input)  
+    prediction = torch.nn.functional.softmax(outputs[0], dim=0)
+    confidences = {labels[i]: float(prediction[i]) for i in range(10)}    
+  return confidences
+
+gr.Interface(title='Digit classifier', fn=predict, 
+             inputs="sketchpad",
+             outputs=gr.Label(num_top_classes=3)).launch(share=False, debug=True)

gitattributes.txt

@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

model_mnist.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af89d1e1fde10c06a7566411894aa65b033a43d3427f69b9ff5789fc37307f6e
+size 15287

requirements.txt

@@ -0,0 +1,2 @@
+torch
+numpy