Update app.py

app.py

@@ -3,32 +3,34 @@ import os
 
 import cv2
 import torch
-import numpy as np  # Import numpy explicitly
-from PIL import Image  # Use PIL for image processing
+import numpy as np
+from PIL import Image
 from insightface.app import FaceAnalysis
 import face_align
 
 faceAnalysis = FaceAnalysis(name='buffalo_l')
-faceAnalysis.prepare(ctx_id=-1, det_size=(512, 512)) #ctx_id=-1 for CPU
+faceAnalysis.prepare(ctx_id=-1, det_size=(512, 512))
 
 from StyleTransferModel_128 import StyleTransferModel
 import gradio as gr
 
 def parse_arguments():
     parser = argparse.ArgumentParser(description='Process command line arguments')
-    
-    parser.add_argument('--modelPath', required=True, help='Model path')
-    parser.add_argument('--resolution', type=int, default=128, help='Resolution')
+    parser.add_argument('--resolution', type=int, default=128, help='Resolution') #Removed model path
 
     return parser.parse_args()
 
 def get_device():
-    return torch.device('cpu')  # Force CPU
+    return torch.device('cpu')
 
 def load_model(model_path):
     device = get_device()
     model = StyleTransferModel().to(device)
-    model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
+    try:
+        model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
+    except FileNotFoundError:
+        print(f"Error: Model file not found at {model_path}")
+        return None
     model.eval()
     return model
 
@@ -41,20 +43,20 @@ def swap_face(model, target_face, source_face_latent):
     with torch.no_grad():
         swapped_tensor = model(target_tensor, source_tensor)
     
-    swapped_face = postprocess_face(swapped_tensor) # Use PIL-based postprocess
+    swapped_face = postprocess_face(swapped_tensor)
 
     return swapped_face, swapped_tensor
 
 def create_target(target_image, resolution):
-    target_face = faceAnalysis.get(np.array(target_image))[0] # Convert PIL to numpy
+    target_face = faceAnalysis.get(np.array(target_image))[0]
 
-    aligned_target_face, M = face_align.norm_crop2(np.array(target_image), target_face.kps, resolution) # Convert PIL to numpy
+    aligned_target_face, M = face_align.norm_crop2(np.array(target_image), target_face.kps, resolution)
     target_face_blob = getBlob(aligned_target_face, (resolution, resolution))
 
     return target_face_blob, M
 
 def create_source(source_image):
-    source_face = faceAnalysis.get(np.array(source_image))[0] # Convert PIL to numpy
+    source_face = faceAnalysis.get(np.array(source_image))[0]
     source_latent = getLatent(source_face)
 
     return source_latent
@@ -64,7 +66,7 @@ def postprocess_face(swapped_tensor):
     swapped_tensor = swapped_tensor.cpu().numpy()
     swapped_tensor = np.transpose(swapped_tensor, (0, 2, 3, 1))
     swapped_tensor = (swapped_tensor * 255).astype(np.uint8)
-    swapped_face = Image.fromarray(swapped_tensor[0]) # Convert to PIL Image
+    swapped_face = Image.fromarray(swapped_tensor[0])
     return swapped_face
 
 def getBlob(aligned_face, size):
@@ -80,39 +82,41 @@ def getLatent(source_face):
 
 
 def blend_swapped_image(swapped_face, target_img, M):
-    swapped_face = np.array(swapped_face) # PIL to numpy
+    swapped_face = np.array(swapped_face)
     swapped_face = cv2.warpAffine(swapped_face, M, (target_img.shape[1], target_img.shape[0]))
     mask = np.ones_like(swapped_face) * 255
     mask = cv2.warpAffine(mask, M, (target_img.shape[1], target_img.shape[0]))
 
-    target_img = np.array(target_img) # PIL to numpy
+    target_img = np.array(target_img)
     swapped_face = Image.blend(Image.fromarray(target_img), Image.fromarray(swapped_face), Image.fromarray(mask).convert("L"))
 
-    return np.array(swapped_face) # numpy to PIL
+    return np.array(swapped_face)
 
 
-def process_images(target_image, source_image, model_path):
+def process_images(target_image, source_image):
     args = parse_arguments()
-    args.modelPath = model_path
-    args.no_paste_back = False # or True, as you prefer
     args.resolution = 128
-    model = load_model(args.modelPath)
+
+    model_path = "reswapper-429500.pth" # Hardcoded model path
+
+    model = load_model(model_path)
+    if model is None:
+        return "Error: Could not load the model. Check the path."
 
     target_face_blob, M = create_target(target_image, args.resolution)
     source_latent = create_source(source_image)
     swapped_face, _ = swap_face(model, target_face_blob, source_latent)
 
-    swapped_face = blend_swapped_image(swapped_face, target_image, M) # PIL images
+    swapped_face = blend_swapped_image(swapped_face, target_image, M)
 
-    return Image.fromarray(swapped_face) # Return PIL image
+    return Image.fromarray(swapped_face)
 
 
 with gr.Blocks() as demo:
-    target_image = gr.Image(label="Target Image", type="pil") # Use PIL type
-    source_image = gr.Image(label="Source Image", type="pil") # Use PIL type
-    model_path = gr.Textbox(label="Model Path", value="path/to/your/model.pth") # Add model path input
-    output_image = gr.Image(label="Output Image", type="pil") # Use PIL type
+    target_image = gr.Image(label="Target Image", type="pil")
+    source_image = gr.Image(label="Source Image", type="pil")
+    output_image = gr.Image(label="Output Image", type="pil")
     btn = gr.Button("Swap Face")
-    btn.click(fn=process_images, inputs=[target_image, source_image, model_path], outputs=output_image)
+    btn.click(fn=process_images, inputs=[target_image, source_image], outputs=output_image)
 
-demo.launch() #no share = true for local running
+demo.launch()