Add application file

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import torch
+from model import model, classes
+from torchvision import transforms
+
+checkpoint = torch.load('mnist_model.pth', map_location=torch.device('cpu'))
+
+# Load the state dictionary into model
+model.load_state_dict(checkpoint['model_state_dict'])
+
+# Set your model to evaluation mode
+model.eval()
+
+
+def preprocess_image(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+    resized = cv2.resize(threshold, (28, 28), interpolation=cv2.INTER_AREA)
+    tensor = transforms.ToTensor()(resized).unsqueeze(0)
+    tensor = transforms.Normalize((0.5,), (0.5,))(tensor)
+    return tensor
+
+
+def classify(image):
+    tensor = preprocess_image(image)
+    with torch.no_grad():
+        output = model(tensor)
+        prediction = output.argmax(dim=1, keepdim=True).item()
+    return str(prediction)  # Convert prediction to string
+
+
+iface = gr.Interface(
+    fn=classify,
+    inputs="sketchpad",
+    outputs='label',
+    theme="huggingface",
+    title="Digit Recognition",
+    description="Draw a Digit 0-9 and the algorithm will detect it in real time!",
+    article="<p style='text-align: center'>Digit Recognition | Demo Model by Jugal</p>",
+    live=True)
+iface.launch(debug=True)