image data purified

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import torch
+import numpy as np
 from torchvision import transforms
 from PIL import Image
 
@@ -31,9 +32,14 @@ model.load_state_dict(torch.hub.load_state_dict_from_url(model_path))
 model.eval()
 
 # Gradio preprocessing and prediction pipeline
-def predict_digit(image):
-    # Preprocess the image: resize to 28x28, convert to grayscale, and normalize
-    image = Image.fromarray(image).convert('L')  # Convert to grayscale
+def predict_digit(image_dict):
+    # Extract the image array from the dict
+    image = image_dict["image"]  # Extract the image data from the dictionary
+
+    # Convert the image to a numpy array, then to a PIL image, and preprocess
+    image = Image.fromarray(np.array(image)).convert('L')  # Convert to grayscale
+
+    # Preprocess: resize to 28x28 and normalize
     transform = transforms.Compose([
         transforms.Resize((28, 28)),
         transforms.ToTensor(),
@@ -60,4 +66,4 @@ interface = gr.Interface(
 
 # Launch the app
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch(share=True)