Update app.py

app.py

@@ -1,3 +1,71 @@
+# import gradio as gr
+# import torch
+# import numpy as np
+# from PIL import Image
+# import os
+# import legacy
+# import torch_utils
+
+# # Load the pre-trained StyleGAN model
+# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+# model_path = 'dress_model.pkl'  # Place your .pkl in the same directory or update path
+
+# # Load StyleGAN Generator
+# with open(model_path, 'rb') as f:
+#     G = legacy.load_network_pkl(f)['G_ema'].to(device)
+
+# def mix_styles(image1_path, image2_path, styles_to_mix):
+#     # Extract image names (without extensions)
+#     image1_name = os.path.splitext(os.path.basename(image1_path))[0]
+#     image2_name = os.path.splitext(os.path.basename(image2_path))[0]
+
+#     # Load latent vectors from .npz
+#     latent_vector_1 = np.load(os.path.join("projection_results", image1_name, "projected_w.npz"))['w']
+#     latent_vector_2 = np.load(os.path.join("projection_results", image2_name, "projected_w.npz"))['w']
+
+#     # Convert to torch tensors
+#     latent_1_tensor = torch.from_numpy(latent_vector_1).to(device)
+#     latent_2_tensor = torch.from_numpy(latent_vector_2).to(device)
+
+#     # Mix layers
+#     mixed_latent = latent_1_tensor.clone()
+#     mixed_latent[:, styles_to_mix] = latent_2_tensor[:, styles_to_mix]
+
+#     # Generate image
+#     with torch.no_grad():
+#         image = G.synthesis(mixed_latent, noise_mode='const')
+
+#     # Convert to image
+#     image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy()
+#     mixed_image = Image.fromarray(image[0], 'RGB')
+#     return mixed_image
+
+# def style_mixing_interface(image1, image2, mix_value):
+#     if image1 is None or image2 is None:
+#         return None
+#     selected_layers = list(range(mix_value + 1))
+#     return mix_styles(image1, image2, selected_layers)
+
+# # Gradio UI
+# iface = gr.Interface(
+#     fn=style_mixing_interface,
+#     inputs=[
+#         gr.Image(label="First Clothing Image", type="filepath"),
+#         gr.Image(label="Second Clothing Image", type="filepath"),
+#         gr.Slider(label="Style Mixing Strength (Layers 0 to N)", minimum=0, maximum=9, step=1, value=5)
+#     ],
+#     outputs=gr.Image(label="Mixed Clothing Design"),
+#     live=True,
+#     title="Style Mixing for Clothing Design",
+#     description="Upload two projected images and choose how many early layers to mix."
+# )
+
+
+# iface.launch()
+
+
+
+
 import gradio as gr
 import torch
 import numpy as np
@@ -5,6 +73,9 @@ from PIL import Image
 import os
 import legacy
 import torch_utils
+import requests
+import io
+import base64
 
 # Load the pre-trained StyleGAN model
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -42,23 +113,54 @@ def mix_styles(image1_path, image2_path, styles_to_mix):
 
 def style_mixing_interface(image1, image2, mix_value):
     if image1 is None or image2 is None:
-        return None
+        return None, None
+
     selected_layers = list(range(mix_value + 1))
-    return mix_styles(image1, image2, selected_layers)
+    mixed_img = mix_styles(image1, image2, selected_layers)
+
+    # Convert to base64
+    buffer = io.BytesIO()
+    mixed_img.save(buffer, format="PNG")
+    img_bytes = buffer.getvalue()
+    img_base64 = base64.b64encode(img_bytes).decode("utf-8")
+
+    return mixed_img, img_base64
+
+def send_to_backend(base64_img):
+    try:
+        response = requests.post(
+            "http://localhost:3000/customisation/save",  # Change if using different port/route
+            json={"image": base64_img},
+            timeout=10
+        )
+        if response.status_code == 200:
+            return "✅ Saved to database!"
+        else:
+            return f"❌ Failed to save: {response.status_code} - {response.text}"
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
 
 # Gradio UI
-iface = gr.Interface(
-    fn=style_mixing_interface,
-    inputs=[
-        gr.Image(label="First Clothing Image", type="filepath"),
-        gr.Image(label="Second Clothing Image", type="filepath"),
-        gr.Slider(label="Style Mixing Strength (Layers 0 to N)", minimum=0, maximum=9, step=1, value=5)
-    ],
-    outputs=gr.Image(label="Mixed Clothing Design"),
-    live=True,
-    title="Style Mixing for Clothing Design",
-    description="Upload two projected images and choose how many early layers to mix."
-)
+with gr.Blocks(title="Style Mixing for Clothing Design") as iface:
+    gr.Markdown("## Style Mixing for Clothing Design\nUpload two projected clothing images and select how many early layers to mix.")
+    
+    with gr.Row():
+        image1_input = gr.Image(label="First Clothing Image", type="filepath")
+        image2_input = gr.Image(label="Second Clothing Image", type="filepath")
+    
+    mix_slider = gr.Slider(label="Style Mixing Strength (Layers 0 to N)", minimum=0, maximum=9, step=1, value=5)
+    
+    output_image = gr.Image(label="Mixed Clothing Design")
+    base64_output = gr.Textbox(visible=False)
+    
+    download_button = gr.Button("Download & Save to Database")
+    save_status = gr.Textbox(label="Save Status", interactive=False)
+
+    def mix_and_return(image1, image2, mix_value):
+        return style_mixing_interface(image1, image2, mix_value)
+
+    mix_slider.change(mix_and_return, inputs=[image1_input, image2_input, mix_slider], outputs=[output_image, base64_output])
 
+    download_button.click(fn=send_to_backend, inputs=[base64_output], outputs=[save_status])
 
 iface.launch()