Add application file

app.py

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+
+import torch
+import torchvision
+import base64 
+import torch.nn.functional as F
+import gradio as gr
+from PIL import Image
+from pathlib import Path
+from torch import nn
+from torchvision import transforms
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+base_path = str(Path(__file__).parent)
+path_of_model = base_path + "/cnn_net.pt"
+default_img = base_path + "/base64_img.bin"
+
+def load_the_model():
+  class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 39)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1) # flatten all dimensions except batch
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+    
+  loaded_model = Net().to(device)
+  loaded_model.load_state_dict(torch.load(path_of_model))
+  loaded_model.eval()
+  return loaded_model
+
+def loads_data_classes() -> list:
+    """
+    Load the class labels for the prediction.
+
+    Returns:
+        list: A list of class labels.
+    """
+    class_labels = ['ا','ب','ت','ث','ج','ح','خ','د','ذ','ر','ز','س','ش','ص','ض','ط','ظ','ع','غ','ف',
+                    'ق','ك','ل','لا','م','ن','ه','و','ي','٠','١','٢','٣','٤','٥','٦','٧','٨','٩']
+    return class_labels
+
+def base64_to_image(base64_file):
+
+    # Decode the Base64 string to binary data
+    image_data = base64.b64decode(base64_file)
+    
+    image_path = 'decoded.png'
+    with open(image_path, 'wb') as output_file:
+        output_file.write(image_data)
+    
+    return image_path
+
+def read_base64_file(file_path):
+    with open(file_path, 'r') as file:
+        base64_string = file.read()
+    return base64_string
+
+def predict_on_base64(model, base64_file):
+    path = base64_to_image(base64_file)
+    img = Image.open(path)
+    model.eval()
+    with torch.inference_mode():
+        custom_image = torchvision.io.read_image(str(path)).type(torch.float32)
+        # Divide the image pixel values by 255 to get them between [0, 1]
+        custom_image = custom_image / 255. 
+        # apply the model transformations
+        transform_img = transforms.Compose([
+        transforms.Grayscale(),
+        transforms.Resize((32,32)),
+        ])
+
+        # Transform target image
+        custom_image_transformed = transform_img(custom_image)
+        # Add an extra dimension to image (Batch_size)
+        custom_image_transformed_with_batch_size = custom_image_transformed.unsqueeze(dim=0)
+        
+        # Make a prediction on image with an extra dimension
+        custom_image_pred = model(custom_image_transformed_with_batch_size.to(device))
+        # Getting the probs
+        prob = torch.softmax(custom_image_pred, dim=1)
+        # Getting the sample prob
+        sample_prob = round(prob[0][prob.argmax()].item(), 3)
+        # getting the highest logit
+        test_pred_labels = custom_image_pred.argmax(dim=1).item()
+    labels = loads_data_classes()
+    test_pred_labels = labels[test_pred_labels]
+    
+    return test_pred_labels, sample_prob, img
+
+model = load_the_model() # load the model
+
+def predict(user_base_64_file):
+  base64_string = read_base64_file(user_base_64_file.name) # convert the base64 image to string
+  prediction, probability, img = predict_on_base64(model=model, base64_file=base64_string) # use the model and getting prediction
+  return prediction, probability, img
+
+
+demo = gr.Interface(fn=predict,
+                                              inputs=gr.File(value = default_img ,label="Upload a Base64 Image File"),
+                                              outputs=[gr.Textbox(label="Predicted Label"), gr.Textbox(label="Probability"), gr.Image(label="Image")],
+                                              title="Arabic Letter Recognition", allow_flagging=False
+                                              )
+demo.launch(share=True)

base64_img.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba29ee5c5a1969449ca242baf100450addfe9f5d66b8a41f0fa37b311fd7deb
+size 496

cnn_net.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1c91753db110ed44205879307a8533ec57a64b5940406c8ff1132f3cc92bb41
+size 260024

requirements.txt

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+Flask
+torch==2.3.0+cpu
+torchvision==0.18.0+cpu
+-f https://download.pytorch.org/whl/torch_stable.html