diff --git a/api.py b/api.py
new file mode 100644
index 0000000..8948f7e
--- /dev/null
+++ b/api.py
@@ -0,0 +1,65 @@
+from torch import optim
+
+from helper import *
+from model.generator import SkipEncoderDecoder, input_noise
+
+def remove_watermark(image_path, mask_path, max_dim, reg_noise, input_depth, lr, show_step, training_steps):
+    DTYPE = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
+    if not torch.cuda.is_available():
+        print('\nSetting device to "cpu", since torch is not built with "cuda" support...')
+        print('It is recommended to use GPU if possible...')
+
+    image_np, mask_np = preprocess_images(image_path, mask_path, max_dim)
+
+    print('Building the model...')
+    generator = SkipEncoderDecoder(
+        input_depth,
+        num_channels_down = [128] * 5,
+        num_channels_up = [128] * 5,
+        num_channels_skip = [128] * 5
+    ).type(DTYPE)
+
+    objective = torch.nn.MSELoss().type(DTYPE)
+    optimizer = optim.Adam(generator.parameters(), lr)
+
+    image_var = np_to_torch_array(image_np).type(DTYPE)
+    mask_var = np_to_torch_array(mask_np).type(DTYPE)
+
+    generator_input = input_noise(input_depth, image_np.shape[1:]).type(DTYPE)
+
+    generator_input_saved = generator_input.detach().clone()
+    noise = generator_input.detach().clone()
+
+    print('Starting training...')
+
+    progress_bar = tqdm(range(training_steps), desc = 'Completed', ncols = 100)
+
+    for step in progress_bar:
+        optimizer.zero_grad()
+        generator_input = generator_input_saved
+
+        if reg_noise > 0:
+            generator_input = generator_input_saved + (noise.normal_() * reg_noise)
+            
+        output = generator(generator_input)
+    
+        loss = objective(output * mask_var, image_var * mask_var)
+        loss.backward()
+
+        if step % show_step == 0:
+            output_image = torch_to_np_array(output)
+            visualize_sample(image_np, output_image, nrow = 2, size_factor = 10)
+        
+        progress_bar.set_postfix(Loss = loss.item())
+            
+        optimizer.step()
+    
+    output_image = torch_to_np_array(output)
+    visualize_sample(output_image, nrow = 1, size_factor = 10)
+
+    pil_image = Image.fromarray((output_image.transpose(1, 2, 0) * 255.0).astype('uint8'))
+
+    output_path = image_path.split('.')[-2] + '-without-watermark.jpg'
+    print(f'\nSaving final output image to: "{output_path}"')
+
+    pil_image.save(output_path)
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