Update app.py

app.py

@@ -64,7 +64,6 @@
 # iface.launch()
 
 
-
 import gradio as gr
 import torch
 import numpy as np
@@ -76,13 +75,10 @@ import requests
 import io
 import warnings
 
-# Suppress deprecated torch warnings
 warnings.filterwarnings("ignore")
-
-# --- Load the pre-trained StyleGAN model ---
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model_path = 'dress_model.pkl'
 
+model_path = 'dress_model.pkl'
 with open(model_path, 'rb') as f:
     G = legacy.load_network_pkl(f)['G_ema'].to(device)
 
@@ -117,7 +113,100 @@ def style_mixing_interface(image1, image2, mix_value):
     buffer.seek(0)
     return mixed_img, buffer
 
-def send_to_backend(image_buffer, "74"):
+def send_to_backend(image_buffer, user_id):
+    if not user_id:
+        return "❌ user_id not found in URL."
+
+    try:
+        files = {'file': ('generated_image.png', image_buffer, 'image/png')}
+        url = f"https://361d-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)}"
+
+with gr.Blocks(title="Style Mixing for Clothing Design") as iface:
+    user_id_state = gr.State()
+
+    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]
+    )
+
+    # Initialization function that extracts user_id
+    def init_fn(request: gr.Request):
+        user_id = request.query_params.get("user_id", "")
+        return {user_id_state: user_id}
+
+    iface.load(fn=None, inputs=None, outputs=None, preprocess=False, queue=False, show_progress=False)
+    iface.launch(initialize=init_fn)
+(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."