Update app.py

app.py

@@ -1,26 +1,35 @@
+# ===========================================
+# IP-Composer 🌅✚🖌️ – FULL IMPROVED UI SCRIPT
+# (기능 동일, UI·테마·갤러리 강화 + FileNotFoundError 수정)
+# ===========================================
+
 import os, json, random, gc
 import numpy as np
 import torch
 from PIL import Image
 import gradio as gr
-from gradio.themes import Soft                                  
+from gradio.themes import Soft
 from diffusers import StableDiffusionXLPipeline
 import open_clip
 from huggingface_hub import hf_hub_download
 from IP_Composer.IP_Adapter.ip_adapter import IPAdapterXL
-from IP_Composer.perform_swap import (compute_dataset_embeds_svd,
-                                      get_modified_images_embeds_composition)
-from IP_Composer.generate_text_embeddings import (load_descriptions,
-                                                  generate_embeddings)
+from IP_Composer.perform_swap import (
+    compute_dataset_embeds_svd,
+    get_modified_images_embeds_composition,
+)
+from IP_Composer.generate_text_embeddings import (
+    load_descriptions,
+    generate_embeddings,
+)
 import spaces
 
 # ─────────────────────────────
-# 1 · Device
+# 1 · Device
 # ─────────────────────────────
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # ─────────────────────────────
-# 2 · Stable-Diffusion XL
+# 2 · Stable-Diffusion XL
 # ─────────────────────────────
 base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
 pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -30,32 +39,30 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 )
 
 # ─────────────────────────────
-# 3 · IP-Adapter
+# 3 · IP-Adapter
 # ─────────────────────────────
-image_encoder_repo      = 'h94/IP-Adapter'
-image_encoder_subfolder = 'models/image_encoder'
+image_encoder_repo = "h94/IP-Adapter"
+image_encoder_subfolder = "models/image_encoder"
 ip_ckpt = hf_hub_download(
-    'h94/IP-Adapter',
-    subfolder="sdxl_models",
-    filename='ip-adapter_sdxl_vit-h.bin'
+    "h94/IP-Adapter", subfolder="sdxl_models", filename="ip-adapter_sdxl_vit-h.bin"
+)
+ip_model = IPAdapterXL(
+    pipe, image_encoder_repo, image_encoder_subfolder, ip_ckpt, device
 )
-ip_model = IPAdapterXL(pipe, image_encoder_repo,
-                       image_encoder_subfolder,
-                       ip_ckpt, device)
 
 # ─────────────────────────────
-# 4 · CLIP
+# 4 · CLIP
 # ─────────────────────────────
 clip_model, _, preprocess = open_clip.create_model_and_transforms(
-    'hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K'
+    "hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
 )
 clip_model.to(device)
 tokenizer = open_clip.get_tokenizer(
-    'hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K'
+    "hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
 )
 
 # ─────────────────────────────
-# 5 · Concept maps
+# 5 · Concept maps
 # ─────────────────────────────
 CONCEPTS_MAP = {
     "age": "age_descriptions.npy",
@@ -72,87 +79,158 @@ CONCEPTS_MAP = {
     "daytime": "times_of_day_descriptions.npy",
     "pose": "person_poses_descriptions.npy",
     "season": "season_descriptions.npy",
-    "material": "material_descriptions_with_gems.npy"
+    "material": "material_descriptions_with_gems.npy",
 }
 RANKS_MAP = {
-    "age": 30, "animal fur": 80, "dogs": 30, "emotions": 30,
-    "flowers": 30, "fruit/vegtable": 30, "outfit type": 30,
-    "outfit pattern (including color)": 80, "patterns": 80,
-    "patterns (including color)": 80, "vehicle": 30,
-    "daytime": 30, "pose": 30, "season": 30, "material": 80
+    "age": 30,
+    "animal fur": 80,
+    "dogs": 30,
+    "emotions": 30,
+    "flowers": 30,
+    "fruit/vegtable": 30,
+    "outfit type": 30,
+    "outfit pattern (including color)": 80,
+    "patterns": 80,
+    "patterns (including color)": 80,
+    "vehicle": 30,
+    "daytime": 30,
+    "pose": 30,
+    "season": 30,
+    "material": 80,
 }
 concept_options = list(CONCEPTS_MAP.keys())
 
 # ─────────────────────────────
-# 6 · Example tuples (base_img, c1_img, …)
+# 6 · Example tuples (base_img, c1_img, …)
 # ─────────────────────────────
 examples = [
-    ['./IP_Composer/assets/patterns/base.jpg',
-     './IP_Composer/assets/patterns/pattern.png',
-     'patterns (including color)', None, None, None, None,
-     80, 30, 30, None, 1.0, 0, 30],
-    ['./IP_Composer/assets/flowers/base.png',
-     './IP_Composer/assets/flowers/concept.png',
-     'flowers', None, None, None, None,
-     30, 30, 30, None, 1.0, 0, 30],
-    ['./IP_Composer/assets/materials/base.png',
-     './IP_Composer/assets/materials/concept.jpg',
-     'material', None, None, None, None,
-     80, 30, 30, None, 1.0, 0, 30],
-    # … (생략 없이 추가 가능)
+    [
+        "./IP_Composer/assets/patterns/base.jpg",
+        "./IP_Composer/assets/patterns/pattern.png",
+        "patterns (including color)",
+        None,
+        None,
+        None,
+        None,
+        80,
+        30,
+        30,
+        None,
+        1.0,
+        0,
+        30,
+    ],
+    [
+        "./IP_Composer/assets/flowers/base.png",
+        "./IP_Composer/assets/flowers/concept.png",
+        "flowers",
+        None,
+        None,
+        None,
+        None,
+        30,
+        30,
+        30,
+        None,
+        1.0,
+        0,
+        30,
+    ],
+    [
+        "./IP_Composer/assets/materials/base.png",
+        "./IP_Composer/assets/materials/concept.jpg",
+        "material",
+        None,
+        None,
+        None,
+        None,
+        80,
+        30,
+        30,
+        None,
+        1.0,
+        0,
+        30,
+    ],
 ]
 
 # ----------------------------------------------------------
-# 7 · Utility functions (unchanged except docstring tweaks)
+# 7 · Utility functions
 # ----------------------------------------------------------
-def generate_examples(base_image, 
-                      concept_image1, concept_name1,
-                      concept_image2, concept_name2,
-                      concept_image3, concept_name3,
-                      rank1, rank2, rank3, 
-                      prompt, scale, seed, num_inference_steps):
-    return process_and_display(base_image,
-                               concept_image1, concept_name1,
-                               concept_image2, concept_name2,
-                               concept_image3, concept_name3,
-                               rank1, rank2, rank3,
-                               prompt, scale, seed, num_inference_steps)
+def generate_examples(
+    base_image,
+    concept_image1,
+    concept_name1,
+    concept_image2,
+    concept_name2,
+    concept_image3,
+    concept_name3,
+    rank1,
+    rank2,
+    rank3,
+    prompt,
+    scale,
+    seed,
+    num_inference_steps,
+):
+    return process_and_display(
+        base_image,
+        concept_image1,
+        concept_name1,
+        concept_image2,
+        concept_name2,
+        concept_image3,
+        concept_name3,
+        rank1,
+        rank2,
+        rank3,
+        prompt,
+        scale,
+        seed,
+        num_inference_steps,
+    )
+
 
 MAX_SEED = np.iinfo(np.int32).max
+
+
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     return random.randint(0, MAX_SEED) if randomize_seed else seed
 
-def change_rank_default(concept_name):             # rank 자동 조정
+
+def change_rank_default(concept_name):
     return RANKS_MAP.get(concept_name, 30)
 
+
 @spaces.GPU
 def match_image_to_concept(image):
     if image is None:
         return None
-    img_pil   = Image.fromarray(image).convert("RGB")
+    img_pil = Image.fromarray(image).convert("RGB")
     img_embed = get_image_embeds(img_pil, clip_model, preprocess, device)
-
-    similarities = {}
-    for concept_name, concept_file in CONCEPTS_MAP.items():
+    sims = {}
+    for cname, cfile in CONCEPTS_MAP.items():
         try:
-            embeds_path = f"./IP_Composer/text_embeddings/{concept_file}"
-            with open(embeds_path, "rb") as f:
-                concept_embeds = np.load(f)
-            sim_scores = []
-            for embed in concept_embeds:
-                sim = np.dot(img_embed.flatten()/np.linalg.norm(img_embed),
-                             embed.flatten()/np.linalg.norm(embed))
-                sim_scores.append(sim)
-            sim_scores.sort(reverse=True)
-            similarities[concept_name] = np.mean(sim_scores[:5])
+            with open(f"./IP_Composer/text_embeddings/{cfile}", "rb") as f:
+                embeds = np.load(f)
+            scores = []
+            for e in embeds:
+                s = np.dot(
+                    img_embed.flatten() / np.linalg.norm(img_embed),
+                    e.flatten() / np.linalg.norm(e),
+                )
+                scores.append(s)
+            scores.sort(reverse=True)
+            sims[cname] = np.mean(scores[:5])
         except Exception as e:
-            print(f"Concept {concept_name} error: {e}")
-    if similarities:
-        detected = max(similarities, key=similarities.get)
-        gr.Info(f"Image automatically matched to concept: {detected}")
-        return detected
+            print(cname, "error:", e)
+    if sims:
+        best = max(sims, key=sims.get)
+        gr.Info(f"Image automatically matched to concept: {best}")
+        return best
     return None
 
+
 @spaces.GPU
 def get_image_embeds(pil_image, model=clip_model, preproc=preprocess, dev=device):
     image = preproc(pil_image)[np.newaxis, :, :, :]
@@ -160,47 +238,63 @@ def get_image_embeds(pil_image, model=clip_model, preproc=preprocess, dev=device
         embeds = model.encode_image(image.to(dev))
     return embeds.cpu().detach().numpy()
 
+
 @spaces.GPU
 def process_images(
-    base_image, 
-    concept_image1, concept_name1,
-    concept_image2=None, concept_name2=None,
-    concept_image3=None, concept_name3=None,
-    rank1=10, rank2=10, rank3=10,
-    prompt=None, scale=1.0, seed=420, num_inference_steps=50,
-    concpet_from_file_1=None, concpet_from_file_2=None, concpet_from_file_3=None,
-    use_concpet_from_file_1=False, use_concpet_from_file_2=False, use_concpet_from_file_3=False
+    base_image,
+    concept_image1,
+    concept_name1,
+    concept_image2=None,
+    concept_name2=None,
+    concept_image3=None,
+    concept_name3=None,
+    rank1=10,
+    rank2=10,
+    rank3=10,
+    prompt=None,
+    scale=1.0,
+    seed=420,
+    num_inference_steps=50,
+    concpet_from_file_1=None,
+    concpet_from_file_2=None,
+    concpet_from_file_3=None,
+    use_concpet_from_file_1=False,
+    use_concpet_from_file_2=False,
+    use_concpet_from_file_3=False,
 ):
-    base_pil  = Image.fromarray(base_image).convert("RGB")
+    base_pil = Image.fromarray(base_image).convert("RGB")
     base_embed = get_image_embeds(base_pil, clip_model, preprocess, device)
 
     concept_images, concept_descs, ranks = [], [], []
     skip = [False, False, False]
 
-    # ─── concept 1
+    # concept 1
     if concept_image1 is None:
         return None, "Please upload at least one concept image"
     concept_images.append(concept_image1)
     if use_concpet_from_file_1 and concpet_from_file_1 is not None:
-        concept_descs.append(concpet_from_file_1); skip[0] = True
+        concept_descs.append(concpet_from_file_1)
+        skip[0] = True
     else:
         concept_descs.append(CONCEPTS_MAP[concept_name1])
     ranks.append(rank1)
 
-    # ─── concept 2
+    # concept 2
     if concept_image2 is not None:
         concept_images.append(concept_image2)
         if use_concpet_from_file_2 and concpet_from_file_2 is not None:
-            concept_descs.append(concpet_from_file_2); skip[1] = True
+            concept_descs.append(concpet_from_file_2)
+            skip[1] = True
         else:
             concept_descs.append(CONCEPTS_MAP[concept_name2])
         ranks.append(rank2)
 
-    # ─── concept 3
+    # concept 3
     if concept_image3 is not None:
         concept_images.append(concept_image3)
         if use_concpet_from_file_3 and concpet_from_file_3 is not None:
-            concept_descs.append(concpet_from_file_3); skip[2] = True
+            concept_descs.append(concpet_from_file_3)
+            skip[2] = True
         else:
             concept_descs.append(CONCEPTS_MAP[concept_name3])
         ranks.append(rank3)
@@ -220,29 +314,47 @@ def process_images(
         {"embed": e, "projection_matrix": p}
         for e, p in zip(concept_embeds, proj_mats)
     ]
-    modified_images = get_modified_images_embeds_composition(
-        base_embed, projections_data, ip_model,
-        prompt=prompt, scale=scale,
-        num_samples=1, seed=seed, num_inference_steps=num_inference_steps
+    modified = get_modified_images_embeds_composition(
+        base_embed,
+        projections_data,
+        ip_model,
+        prompt=prompt,
+        scale=scale,
+        num_samples=1,
+        seed=seed,
+        num_inference_steps=num_inference_steps,
     )
-    return modified_images[0]
+    return modified[0]
+
 
 @spaces.GPU
 def get_text_embeddings(concept_file):
-    descriptions = load_descriptions(concept_file) 
-    embeddings   = generate_embeddings(descriptions, clip_model,
-                                       tokenizer, device, batch_size=100)
-    return embeddings, True
+    descs = load_descriptions(concept_file)
+    embeds = generate_embeddings(descs, clip_model, tokenizer, device, batch_size=100)
+    return embeds, True
+
 
 def process_and_display(
-    base_image, 
-    concept_image1, concept_name1="age",
-    concept_image2=None, concept_name2=None,
-    concept_image3=None, concept_name3=None,
-    rank1=30, rank2=30, rank3=30,
-    prompt=None, scale=1.0, seed=0, num_inference_steps=50,
-    concpet_from_file_1=None, concpet_from_file_2=None, concpet_from_file_3=None,
-    use_concpet_from_file_1=False, use_concpet_from_file_2=False, use_concpet_from_file_3=False
+    base_image,
+    concept_image1,
+    concept_name1="age",
+    concept_image2=None,
+    concept_name2=None,
+    concept_image3=None,
+    concept_name3=None,
+    rank1=30,
+    rank2=30,
+    rank3=30,
+    prompt=None,
+    scale=1.0,
+    seed=0,
+    num_inference_steps=50,
+    concpet_from_file_1=None,
+    concpet_from_file_2=None,
+    concpet_from_file_3=None,
+    use_concpet_from_file_1=False,
+    use_concpet_from_file_2=False,
+    use_concpet_from_file_3=False,
 ):
     if base_image is None:
         raise gr.Error("Please upload a base image")
@@ -250,32 +362,45 @@ def process_and_display(
         raise gr.Error("Choose at least one concept image")
 
     return process_images(
-        base_image, concept_image1, concept_name1,
-        concept_image2, concept_name2,
-        concept_image3, concept_name3,
-        rank1, rank2, rank3,
-        prompt, scale, seed, num_inference_steps,
-        concpet_from_file_1, concpet_from_file_2, concpet_from_file_3,
-        use_concpet_from_file_1, use_concpet_from_file_2, use_concpet_from_file_3
+        base_image,
+        concept_image1,
+        concept_name1,
+        concept_image2,
+        concept_name2,
+        concept_image3,
+        concept_name3,
+        rank1,
+        rank2,
+        rank3,
+        prompt,
+        scale,
+        seed,
+        num_inference_steps,
+        concpet_from_file_1,
+        concpet_from_file_2,
+        concpet_from_file_3,
+        use_concpet_from_file_1,
+        use_concpet_from_file_2,
+        use_concpet_from_file_3,
     )
 
+
 # ----------------------------------------------------------
-# 8 · 💄 THEME & CSS UPGRADE
+# 8 · THEME & CSS
 # ----------------------------------------------------------
-demo_theme = Soft(                                           # ★ NEW
-    primary_hue="purple",
-    font=[gr.themes.GoogleFont("Inter")]
-)
+demo_theme = Soft(primary_hue="purple", font=[gr.themes.GoogleFont("Inter")])
 css = """
 body{
    background:#0f0c29;
    background:linear-gradient(135deg,#0f0c29,#302b63,#24243e);
 }
-#header{ text-align:center;
-         padding:24px 0 8px;
-         font-weight:700;
-         font-size:2.1rem;
-         color:#ffffff;}
+#header{
+   text-align:center;
+   padding:24px 0 8px;
+   font-weight:700;
+   font-size:2.1rem;
+   color:#ffffff;
+}
 .gradio-container{max-width:1024px !important;margin:0 auto}
 .card{
    border-radius:18px;
@@ -288,137 +413,186 @@ body{
 """
 
 # ----------------------------------------------------------
-# 9 · 🖼️ Demo UI
+# 9 · UI
 # ----------------------------------------------------------
 example_gallery = [
-    ['./IP_Composer/assets/patterns/base.jpg', "Patterns demo"],
-    ['./IP_Composer/assets/flowers/base.png',  "Flowers demo"],
-    ['./IP_Composer/assets/materials/base.png',"Material demo"],
+    ["./IP_Composer/assets/patterns/base.jpg", "Patterns demo"],
+    ["./IP_Composer/assets/flowers/base.png", "Flowers demo"],
+    ["./IP_Composer/assets/materials/base.png", "Material demo"],
 ]
 
 with gr.Blocks(css=css, theme=demo_theme) as demo:
-    # ─── Header
-    gr.Markdown("<div id='header'>🌅 IP-Composer&nbsp;"
-                "<sup style='font-size:14px'>SDXL</sup></div>")
-
-    # ─── States for custom concepts
-    concpet_from_file_1 = gr.State()
-    concpet_from_file_2 = gr.State()
-    concpet_from_file_3 = gr.State()
-    use_concpet_from_file_1 = gr.State()
-    use_concpet_from_file_2 = gr.State()
-    use_concpet_from_file_3 = gr.State()
-
-    # ─── Main layout
+    gr.Markdown(
+        "<div id='header'>🌅 IP-Composer&nbsp;"
+        "<sup style='font-size:14px'>SDXL</sup></div>"
+    )
+
+    concpet_from_file_1, concpet_from_file_2, concpet_from_file_3 = (
+        gr.State(),
+        gr.State(),
+        gr.State(),
+    )
+    use_concpet_from_file_1, use_concpet_from_file_2, use_concpet_from_file_3 = (
+        gr.State(),
+        gr.State(),
+        gr.State(),
+    )
+
     with gr.Row(equal_height=True):
-        # Base image card
         with gr.Column(elem_classes="card"):
-            base_image = gr.Image(label="Base Image (Required)",
-                                  type="numpy", height=400, width=400)
+            base_image = gr.Image(
+                label="Base Image (Required)", type="numpy", height=400, width=400
+            )
 
-        # Concept cards (1 · 2 · 3)
         for idx in (1, 2, 3):
             with gr.Column(elem_classes="card"):
                 locals()[f"concept_image{idx}"] = gr.Image(
-                    label=f"Concept Image {idx}" if idx == 1 else f"Concept {idx} (Optional)",
-                    type="numpy", height=400, width=400
+                    label=f"Concept Image {idx}"
+                    if idx == 1
+                    else f"Concept {idx} (Optional)",
+                    type="numpy",
+                    height=400,
+                    width=400,
                 )
                 locals()[f"concept_name{idx}"] = gr.Dropdown(
-                    concept_options, label=f"Concept {idx}",
+                    concept_options,
+                    label=f"Concept {idx}",
                     value=None if idx != 1 else "age",
-                    info="Pick concept type"
+                    info="Pick concept type",
                 )
                 with gr.Accordion("💡 Or use a new concept 👇", open=False):
-                    gr.Markdown("1. Upload a file with **>100** text variations<br>"
-                                "2. Tip: Ask an LLM to list variations.")
+                    gr.Markdown(
+                        "1. Upload a file with **>100** text variations<br>"
+                        "2. Tip: Ask an LLM to list variations."
+                    )
                     if idx == 1:
-                        concept_file_1 = gr.File("Concept variations",
-                                                 file_types=["text"])
+                        concept_file_1 = gr.File(
+                            label="Concept variations", file_types=["text"]
+                        )
                     elif idx == 2:
-                        concept_file_2 = gr.File("Concept variations",
-                                                 file_types=["text"])
+                        concept_file_2 = gr.File(
+                            label="Concept variations", file_types=["text"]
+                        )
                     else:
-                        concept_file_3 = gr.File("Concept variations",
-                                                 file_types=["text"])
+                        concept_file_3 = gr.File(
+                            label="Concept variations", file_types=["text"]
+                        )
 
-    # ─── Advanced options card (full width)
     with gr.Column(elem_classes="card"):
         with gr.Accordion("⚙️ Advanced options", open=False):
-            prompt = gr.Textbox(label="Guidance Prompt (Optional)",
-                                placeholder="Optional text prompt to guide generation")
-            num_inference_steps = gr.Slider(1, 50, value=30, step=1,
-                                            label="Num steps")
+            prompt = gr.Textbox(
+                label="Guidance Prompt (Optional)",
+                placeholder="Optional text prompt to guide generation",
+            )
+            num_inference_steps = gr.Slider(1, 50, 30, step=1, label="Num steps")
             with gr.Row():
-                scale = gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="Scale")
+                scale = gr.Slider(0.1, 2.0, 1.0, step=0.1, label="Scale")
                 randomize_seed = gr.Checkbox(True, label="Randomize seed")
                 seed = gr.Number(value=0, label="Seed", precision=0)
-            gr.Markdown("If a concept is not showing enough, **increase rank** ⬇️")
+            gr.Markdown(
+                "If a concept is not showing enough, **increase rank** ⬇️"
+            )
             with gr.Row():
-                rank1 = gr.Slider(1, 150, value=30, step=1, label="Rank concept 1")
-                rank2 = gr.Slider(1, 150, value=30, step=1, label="Rank concept 2")
-                rank3 = gr.Slider(1, 150, value=30, step=1, label="Rank concept 3")
+                rank1 = gr.Slider(1, 150, 30, step=1, label="Rank concept 1")
+                rank2 = gr.Slider(1, 150, 30, step=1, label="Rank concept 2")
+                rank3 = gr.Slider(1, 150, 30, step=1, label="Rank concept 3")
 
-    # ─── Output & Generate button
     with gr.Column(elem_classes="card"):
         output_image = gr.Image(show_label=False, height=480)
         submit_btn = gr.Button("🔮 Generate", variant="primary", size="lg")
 
-    # ─── Ready-made Gallery
     gr.Markdown("### 🔥 Ready-made examples")
-    gr.Gallery(example_gallery, label="Preview",
-               columns=[3], height="auto")
+    gr.Gallery(example_gallery, label="클릭해서 미리보기", columns=[3], height="auto")
 
-    # ─── Example usage (kept for quick test)
     gr.Examples(
         examples,
-        inputs=[base_image, concept_image1, concept_name1,
-                concept_image2, concept_name2,
-                concept_image3, concept_name3,
-                rank1, rank2, rank3,
-                prompt, scale, seed, num_inference_steps],
+        inputs=[
+            base_image,
+            concept_image1,
+            concept_name1,
+            concept_image2,
+            concept_name2,
+            concept_image3,
+            concept_name3,
+            rank1,
+            rank2,
+            rank3,
+            prompt,
+            scale,
+            seed,
+            num_inference_steps,
+        ],
         outputs=[output_image],
         fn=generate_examples,
-        cache_examples=False
+        cache_examples=False,
     )
 
-    # ─── File upload triggers
-    concept_file_1.upload(get_text_embeddings, [concept_file_1],
-                          [concpet_from_file_1, use_concpet_from_file_1])
-    concept_file_2.upload(get_text_embeddings, [concept_file_2],
-                          [concpet_from_file_2, use_concpet_from_file_2])
-    concept_file_3.upload(get_text_embeddings, [concept_file_3],
-                          [concpet_from_file_3, use_concpet_from_file_3])
-    concept_file_1.delete(lambda x: False, [concept_file_1],
-                          [use_concpet_from_file_1])
-    concept_file_2.delete(lambda x: False, [concept_file_2],
-                          [use_concpet_from_file_2])
-    concept_file_3.delete(lambda x: False, [concept_file_3],
-                          [use_concpet_from_file_3])
-
-    # ─── Dropdown auto-rank
+    # Upload hooks
+    concept_file_1.upload(
+        get_text_embeddings,
+        [concept_file_1],
+        [concpet_from_file_1, use_concpet_from_file_1],
+    )
+    concept_file_2.upload(
+        get_text_embeddings,
+        [concept_file_2],
+        [concpet_from_file_2, use_concpet_from_file_2],
+    )
+    concept_file_3.upload(
+        get_text_embeddings,
+        [concept_file_3],
+        [concpet_from_file_3, use_concpet_from_file_3],
+    )
+    concept_file_1.delete(
+        lambda _: False, [concept_file_1], [use_concpet_from_file_1]
+    )
+    concept_file_2.delete(
+        lambda _: False, [concept_file_2], [use_concpet_from_file_2]
+    )
+    concept_file_3.delete(
+        lambda _: False, [concept_file_3], [use_concpet_from_file_3]
+    )
+
+    # Dropdown auto-rank
     concept_name1.select(change_rank_default, [concept_name1], [rank1])
     concept_name2.select(change_rank_default, [concept_name2], [rank2])
     concept_name3.select(change_rank_default, [concept_name3], [rank3])
 
-    # ─── Auto-match concept type on image upload
+    # Auto-match on upload
     concept_image1.upload(match_image_to_concept, [concept_image1], [concept_name1])
     concept_image2.upload(match_image_to_concept, [concept_image2], [concept_name2])
     concept_image3.upload(match_image_to_concept, [concept_image3], [concept_name3])
 
-    # ─── Generate click chain
-    submit_btn.click(randomize_seed_fn, [seed, randomize_seed], seed) \
-              .then(process_and_display,
-                    [base_image, concept_image1, concept_name1,
-                     concept_image2, concept_name2,
-                     concept_image3, concept_name3,
-                     rank1, rank2, rank3,
-                     prompt, scale, seed, num_inference_steps,
-                     concpet_from_file_1, concpet_from_file_2, concpet_from_file_3,
-                     use_concpet_from_file_1, use_concpet_from_file_2, use_concpet_from_file_3],
-                    [output_image])
+    # Generate chain
+    submit_btn.click(randomize_seed_fn, [seed, randomize_seed], seed).then(
+        process_and_display,
+        [
+            base_image,
+            concept_image1,
+            concept_name1,
+            concept_image2,
+            concept_name2,
+            concept_image3,
+            concept_name3,
+            rank1,
+            rank2,
+            rank3,
+            prompt,
+            scale,
+            seed,
+            num_inference_steps,
+            concpet_from_file_1,
+            concpet_from_file_2,
+            concpet_from_file_3,
+            use_concpet_from_file_1,
+            use_concpet_from_file_2,
+            use_concpet_from_file_3,
+        ],
+        [output_image],
+    )
 
 # ─────────────────────────────
-# 10 · Launch
+# 10 · Launch
 # ─────────────────────────────
 if __name__ == "__main__":
     demo.launch()