Add application file

app.py

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+# Importing essential libraries
+import gradio as gr
+import torch
+from torch import nn
+import numpy as np
+from PIL import Image
+
+# Creating Neural Model class used for training and validation
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, 10),
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+# Intializing and loading the saved model
+model = NeuralNetwork()
+state_dict = torch.load('model.pt',    map_location='cpu')
+model.load_state_dict(state_dict, strict=False)
+model.eval()
+
+# Preprocessing input value for giving to function
+def preprocess_input(input):
+    input = input['composite']
+
+    # Convert the image data to a PIL Image
+    input = Image.fromarray(input)
+
+    # Resize the image to match the input size expected by your model
+    input = input.resize((28, 28))
+
+    # Convert the image to grayscale
+    input = input.convert('L')
+
+    # Flatten the pixel values
+    input = np.array(input)
+
+    return input
+
+# Define a predict function
+def predict(img):
+    x = torch.tensor(preprocess_input(img), dtype=torch.float32)
+    with torch.no_grad():
+        return model(x.unsqueeze(0)).argmax().item()
+    
+# Design UI
+gr.Interface(fn=predict,
+             inputs="sketchpad",
+             outputs="label",
+             live=True).launch(share=True)

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2747d4ed523df909a0f83c65be46375580bb86176f5921c53c3141f03c22a8a7
+size 2681332