Update app.py

app.py

@@ -63,6 +63,126 @@
 
 # iface.launch()
 
+
+
+
+
+
+
+# import gradio as gr
+# import torch
+# import numpy as np
+# from PIL import Image
+# import os
+# import legacy
+# import torch_utils
+# import requests
+# import io
+# import warnings
+
+# warnings.filterwarnings("ignore")
+# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# model_path = 'dress_model.pkl'
+# 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):
+#     image1_name = os.path.splitext(os.path.basename(image1_path))[0]
+#     image2_name = os.path.splitext(os.path.basename(image2_path))[0]
+
+#     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']
+
+#     latent_1_tensor = torch.from_numpy(latent_vector_1).to(device)
+#     latent_2_tensor = torch.from_numpy(latent_vector_2).to(device)
+
+#     mixed_latent = latent_1_tensor.clone()
+#     mixed_latent[:, styles_to_mix] = latent_2_tensor[:, styles_to_mix]
+
+#     with torch.no_grad():
+#         image = G.synthesis(mixed_latent, noise_mode='const')
+
+#     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, None
+#     selected_layers = list(range(mix_value + 1))
+#     mixed_img = mix_styles(image1, image2, selected_layers)
+
+#     buffer = io.BytesIO()
+#     mixed_img.save(buffer, format="PNG")
+#     buffer.seek(0)
+#     return mixed_img, buffer
+
+# def send_to_backend(image_buffer, user_id):
+#     if not user_id:
+#         return "❌ user_id not found."
+
+#     try:
+#         files = {'file': ('generated_image.png', image_buffer, 'image/png')}
+#         url = f"https://5a4d-103-40-74-78.ngrok-free.app/customisation/upload/{user_id}"
+
+#         response = requests.post(url, files=files)
+
+#         if response.status_code == 201:
+#             return "✅ Image uploaded and saved to database!"
+#         else:
+#             return f"❌ Upload failed: {response.status_code} - {response.text}"
+
+#     except Exception as e:
+#         return f"⚠️ Error: {str(e)}"
+
+# # --- Gradio UI ---
+# with gr.Blocks(title="Style Mixing for Clothing Design") as iface:
+
+#     user_id_state = gr.State()
+
+#     @iface.load(inputs=None, outputs=[user_id_state])
+#     def on_load(request: gr.Request):
+#         user_id = request.query_params.get('user_id', '')
+#         return user_id
+
+#     gr.Markdown("## Style Mixing for Clothing Design\nUpload two projected clothing images and mix their styles.")
+
+#     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)
+
+#     with gr.Row():
+#         output_image = gr.Image(label="Mixed Clothing Design")
+#         save_button = gr.Button("Download & Save to Database")
+
+#     image_buffer = gr.State()
+#     save_status = gr.Textbox(label="Save Status", interactive=False)
+
+#     def mix_and_store(image1, image2, mix_value):
+#         result_image, buffer = style_mixing_interface(image1, image2, mix_value)
+#         return result_image, buffer
+
+#     mix_slider.change(mix_and_store, inputs=[image1_input, image2_input, mix_slider], outputs=[output_image, image_buffer])
+#     save_button.click(send_to_backend, inputs=[image_buffer, user_id_state], outputs=[save_status])
+
+# iface.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 import gradio as gr
 import torch
 import numpy as np
@@ -77,10 +197,12 @@ import warnings
 warnings.filterwarnings("ignore")
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
+# Load pretrained StyleGAN model
 model_path = 'dress_model.pkl'
 with open(model_path, 'rb') as f:
     G = legacy.load_network_pkl(f)['G_ema'].to(device)
 
+# Style mixing between two projected images
 def mix_styles(image1_path, image2_path, styles_to_mix):
     image1_name = os.path.splitext(os.path.basename(image1_path))[0]
     image2_name = os.path.splitext(os.path.basename(image2_path))[0]
@@ -101,6 +223,7 @@ def mix_styles(image1_path, image2_path, styles_to_mix):
     mixed_image = Image.fromarray(image[0], 'RGB')
     return mixed_image
 
+# Handles style mixing + output buffer
 def style_mixing_interface(image1, image2, mix_value):
     if image1 is None or image2 is None:
         return None, None
@@ -112,12 +235,16 @@ def style_mixing_interface(image1, image2, mix_value):
     buffer.seek(0)
     return mixed_img, buffer
 
+# Upload to NestJS backend
 def send_to_backend(image_buffer, user_id):
     if not user_id:
         return "❌ user_id not found."
 
     try:
+        # ✅ key 'file' must match the NestJS interceptor
         files = {'file': ('generated_image.png', image_buffer, 'image/png')}
+
+        # ✅ Update URL accordingly
         url = f"https://5a4d-103-40-74-78.ngrok-free.app/customisation/upload/{user_id}"
 
         response = requests.post(url, files=files)
@@ -130,9 +257,8 @@ def send_to_backend(image_buffer, user_id):
     except Exception as e:
         return f"⚠️ Error: {str(e)}"
 
-# --- Gradio UI ---
+# Gradio interface
 with gr.Blocks(title="Style Mixing for Clothing Design") as iface:
-
     user_id_state = gr.State()
 
     @iface.load(inputs=None, outputs=[user_id_state])