Update app.py

app.py

@@ -4,13 +4,13 @@ import numpy as np
 import torch
 from transformers import CLIPProcessor, CLIPModel
 from datasets import load_dataset
-from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline
+from diffusers import StableDiffusionPipeline
 from sklearn.metrics.pairwise import cosine_similarity
-from PIL import Image
 import warnings
 warnings.filterwarnings('ignore')
 
 custom_css = """
+
 body {
     background-image: url('https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/resolve/main/viss.jpeg');
     background-size: 130% 130%; 
@@ -32,6 +32,7 @@ body {
     border: 1px solid rgba(255, 255, 255, 0.1);
 }
 
+
 .visionary-title {
     font-size: 3.5rem;
     font-weight: 900;
@@ -48,6 +49,25 @@ body {
     cursor: pointer;
 }
 
+.visionary-title:hover {
+    transform: scale(1.03);
+    text-shadow: 0px 0px 20px rgba(255, 215, 0, 0.4);
+}
+
+@keyframes goldShine {
+    0% { background-position: 0% 50%; }
+    50% { background-position: 100% 50%; }
+    100% { background-position: 0% 50%; }
+}
+
+.visionary-subtitle {
+    text-align: center;
+    color: rgba(255, 255, 255, 0.7);
+    font-size: 1.2rem;
+    letter-spacing: 2px;
+    margin-bottom: 30px;
+}
+
 .custom-card {
     background: rgba(15, 15, 15, 0.6) !important; 
     border-radius: 20px !important;
@@ -57,10 +77,32 @@ body {
     box-shadow: 0 4px 15px rgba(0,0,0,0.4) !important;
 }
 
+.custom-card:hover {
+    transform: translateY(-5px) scale(1.01) !important; 
+    border: 1px solid rgba(255, 215, 0, 0.6) !important; 
+    box-shadow: 0 10px 30px rgba(255, 215, 0, 0.15) !important; 
+}
+
+.custom-card img {
+    transition: transform 0.4s ease !important; 
+}
+.custom-card:hover img {
+    transform: scale(1.05) !important; 
+}
+
 .custom-score {
     background: rgba(10, 10, 10, 0.8) !important;
     border-radius: 12px !important;
     border: 1px solid rgba(255, 215, 0, 0.1) !important;
+    transition: all 0.3s ease !important;
+}
+
+.custom-score:hover {
+    border: 1px solid rgba(255, 215, 0, 0.4) !important;
+    box-shadow: 0 0 15px rgba(255, 215, 0, 0.1) !important;
+}
+.custom-score:hover textarea {
+    color: #ffd700 !important; 
 }
 
 button.primary {
@@ -68,26 +110,17 @@ button.primary {
     border: 1px solid rgba(255, 215, 0, 0.4) !important;
     color: #ffd700 !important;
     text-transform: uppercase;
+    letter-spacing: 1px;
     font-weight: bold !important;
+    transition: all 0.3s ease !important;
 }
 
 button.primary:hover {
     background: linear-gradient(90deg, #252525, #353535) !important;
     border: 1px solid rgba(255, 215, 0, 0.8) !important;
+    box-shadow: 0 0 15px rgba(255, 215, 0, 0.2) !important;
     transform: translateY(-2px) !important;
 }
-
-.edit-btn {
-    background: rgba(20, 20, 20, 0.8) !important;
-    border: 1px solid rgba(255, 215, 0, 0.3) !important;
-    color: #ffd700 !important;
-    margin-top: 5px !important;
-}
-
-.edit-btn:hover {
-    background: rgba(255, 215, 0, 0.15) !important;
-    border: 1px solid rgba(255, 215, 0, 0.8) !important;
-}
 """
 
 # --- 1. Load Dataset & Embeddings ---
@@ -111,11 +144,6 @@ pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",
 pipe = pipe.to(device)
 pipe.enable_attention_slicing()
 
-print("Loading Stable Diffusion Inpainting Model...")
-inpaint_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float32)
-inpaint_pipe = inpaint_pipe.to(device)
-inpaint_pipe.enable_attention_slicing()
-
 # --- 3. Core Engine Logic ---
 
 def get_recommendations_from_vector(user_vector):
@@ -128,66 +156,48 @@ def get_recommendations_from_vector(user_vector):
     return recs[0], scores[0], recs[1], scores[1], recs[2], scores[2]
 
 def search_by_image(user_image):
-    if user_image is None: return None, "", None, "", None, ""
+    if user_image is None:
+        return None, "", None, "", None, ""
+        
     inputs = processor(images=user_image, return_tensors="pt").to(device)
     with torch.no_grad():
         features = clip_model.get_image_features(**inputs)
-        features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+        if not isinstance(features, torch.Tensor):
+            features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+            
     user_vector = features.cpu().numpy().flatten().reshape(1, -1)
     return get_recommendations_from_vector(user_vector)
 
 def search_by_text_only(prompt):
-    if not prompt: return None, "", None, "", None, ""
+    if not prompt:
+        return None, "", None, "", None, ""
+        
     inputs = processor(text=[prompt], return_tensors="pt", padding=True).to(device)
     with torch.no_grad():
         features = clip_model.get_text_features(**inputs)
-        features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+        if not isinstance(features, torch.Tensor):
+            features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+            
     user_vector = features.cpu().numpy().flatten().reshape(1, -1)
     return get_recommendations_from_vector(user_vector)
 
 def generate_and_recommend(prompt):
-    if not prompt: return None, None, "", None, "", None, ""
+    if not prompt:
+        return None, None, "", None, "", None, ""
+        
     generated_image = pipe(prompt, num_inference_steps=15).images[0]
+    
     inputs = processor(images=generated_image, return_tensors="pt").to(device)
     with torch.no_grad():
         features = clip_model.get_image_features(**inputs)
-        features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+        if not isinstance(features, torch.Tensor):
+            features = features.pooler_output if hasattr(features, 'pooler_output') else features[0]
+            
     user_vector = features.cpu().numpy().flatten().reshape(1, -1)
     rec1, score1, rec2, score2, rec3, score3 = get_recommendations_from_vector(user_vector)
+    
     return generated_image, rec1, score1, rec2, score2, rec3, score3
 
-# לוגיקת המעקף (Bypass) החדשה
-def send_to_holding_state(img):
-    """ שומר את התמונה בזיכרון השרת ומעביר לטאב המטרה """
-    return img, gr.update(selected="tab_magic")
-
-def load_into_editor(img_from_state):
-    """ טוען את התמונה לתוך העורך בצורה בטוחה כשהמסך פתוח """
-    if img_from_state is None:
-        return None
-    return {"background": img_from_state, "layers": [], "composite": None}
-
-def process_magic_edit(edit_dict, prompt):
-    if not edit_dict or not prompt:
-        return None
-    try:
-        bg = edit_dict.get("background")
-        if not bg: return None
-        bg = bg.convert("RGB").resize((512, 512))
-        
-        layers = edit_dict.get("layers", [])
-        if layers and len(layers) > 0:
-            mask = layers[0].convert("RGBA").split()[-1].resize((512, 512))
-        else:
-            mask = Image.new("L", (512, 512), 0)
-            
-        edited_image = inpaint_pipe(prompt=prompt, image=bg, mask_image=mask, num_inference_steps=20).images[0]
-        return edited_image
-    except Exception as e:
-        print(f"Error during Magic Edit process: {e}")
-        return None
-
-
 # --- 4. Gradio User Interface (Premium UI) ---
 
 custom_theme = gr.themes.Monochrome(
@@ -196,121 +206,108 @@ custom_theme = gr.themes.Monochrome(
     font=[gr.themes.GoogleFont("Montserrat"), "ui-sans-serif", "system-ui", "sans-serif"]
 )
 
-with gr.Blocks(title="Visionary | AI Interior Design", css=custom_css, theme=custom_theme) as demo:
-    
-    # הזיכרון החבוי שלנו
-    shared_edit_image = gr.State()
 
+with gr.Blocks(title="Visionary | AI Interior Design", css=custom_css, theme=custom_theme) as demo:
     gr.HTML("""
     <div style="display: flex; flex-direction: column; align-items: center; justify-content: center; margin-top: 20px;">
         <img src="https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/resolve/main/viss1.jpeg" 
              style="height: 110px; border-radius: 15px; box-shadow: 0 4px 15px rgba(0,0,0,0.8); margin-bottom: 10px;">
+        
         <h1 class="visionary-title">VISIONARY</h1>
         <div class="visionary-subtitle">AI-Powered Interior Design Engine</div>
     </div>
     """)
     
-    with gr.Tabs() as main_tabs:
+    with gr.Tabs():
         
-        # --- TAB 1 ---
-        with gr.TabItem("🖼️ Search by Image", id="tab1"):
+        # --- TAB 1: Classic Search by Image ---
+        with gr.TabItem("🖼️ Search by Image"):
+            gr.Markdown("Upload an inspiration photo to instantly discover visually and stylistically similar rooms from our curated catalog.")
             with gr.Row():
                 with gr.Column(scale=1):
                     image_input = gr.Image(label="Upload Inspiration", type="pil", elem_classes="custom-card")
                     img_submit_btn = gr.Button("Find Matches (Instant)", variant="primary")
+                    
                 with gr.Column(scale=2):
                     with gr.Row():
                         with gr.Column():
-                            img_rec1 = gr.Image(label="Top Match", type="pil", elem_classes="custom-card")
+                            img_rec1 = gr.Image(label="Top Match", elem_classes="custom-card")
                             img_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            img_edit_btn1 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            img_rec2 = gr.Image(label="2nd Match", type="pil", elem_classes="custom-card")
+                            img_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                             img_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            img_edit_btn2 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            img_rec3 = gr.Image(label="3rd Match", type="pil", elem_classes="custom-card")
+                            img_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                             img_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            img_edit_btn3 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
-            img_submit_btn.click(fn=search_by_image, inputs=[image_input], outputs=[img_rec1, img_score1, img_rec2, img_score2, img_rec3, img_score3])
-
-        # --- TAB 2 ---
-        with gr.TabItem("🔍 Fast Text Search", id="tab2"):
+                            
+            img_submit_btn.click(
+                fn=search_by_image, inputs=[image_input],
+                outputs=[img_rec1, img_score1, img_rec2, img_score2, img_rec3, img_score3]
+            )
+
+        # --- TAB 2: Fast Text Search (CLIP Multi-modal) ---
+        with gr.TabItem("🔍 Fast Text Search"):
+            gr.Markdown("Describe a room in text. Our multi-modal vision engine will instantly search the catalog for matching designs.")
             with gr.Row():
                 with gr.Column(scale=1):
                     fast_text_input = gr.Textbox(label="Search Query", placeholder="e.g., A minimalist industrial bedroom...", lines=3)
                     fast_txt_submit_btn = gr.Button("Search Catalog (Instant)", variant="primary")
-                    gr.Examples(["A minimalist industrial bedroom with concrete walls", "Luxury modern bathroom with marble and warm lights"], inputs=fast_text_input)
+                    
+                    gr.Examples(
+                        examples=[
+                            "A minimalist industrial bedroom with concrete walls",
+                            "Luxury modern bathroom with marble and warm lights",
+                            "Bohemian living room with lots of plants and wood"
+                        ],
+                        inputs=fast_text_input
+                    )
+                    
                 with gr.Column(scale=2):
                     with gr.Row():
                         with gr.Column():
-                            ft_rec1 = gr.Image(label="Top Match", type="pil", elem_classes="custom-card")
+                            ft_rec1 = gr.Image(label="Top Match", elem_classes="custom-card")
                             ft_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            ft_edit_btn1 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            ft_rec2 = gr.Image(label="2nd Match", type="pil", elem_classes="custom-card")
+                            ft_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                             ft_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            ft_edit_btn2 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            ft_rec3 = gr.Image(label="3rd Match", type="pil", elem_classes="custom-card")
+                            ft_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                             ft_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            ft_edit_btn3 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
-            fast_txt_submit_btn.click(fn=search_by_text_only, inputs=[fast_text_input], outputs=[ft_rec1, ft_score1, ft_rec2, ft_score2, ft_rec3, ft_score3])
 
-        # --- TAB 3 ---
-        with gr.TabItem("✨ AI Design Studio", id="tab3"):
+            fast_txt_submit_btn.click(
+                fn=search_by_text_only, inputs=[fast_text_input],
+                outputs=[ft_rec1, ft_score1, ft_rec2, ft_score2, ft_rec3, ft_score3]
+            )
+
+        # --- TAB 3: Advanced GenAI Search ---
+        with gr.TabItem("✨ AI Design Studio"):
+            gr.Markdown("Describe your ideal space. Our Generative AI will draft a concept from scratch, and then find the closest real-world equivalents.")
             with gr.Row():
                 with gr.Column(scale=1):
                     text_input = gr.Textbox(label="Concept Description", placeholder="e.g., A cozy modern living room...", lines=3)
                     txt_submit_btn = gr.Button("Generate Concept & Match", variant="primary")
                     gen_output = gr.Image(label="AI Drafted Concept", type="pil", elem_classes="custom-card")
+                    
                 with gr.Column(scale=2):
                     with gr.Row():
                         with gr.Column():
-                            txt_rec1 = gr.Image(label="Top Match", type="pil", elem_classes="custom-card")
+                            txt_rec1 = gr.Image(label="Top Match", elem_classes="custom-card")
                             txt_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            txt_edit_btn1 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            txt_rec2 = gr.Image(label="2nd Match", type="pil", elem_classes="custom-card")
+                            txt_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                             txt_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            txt_edit_btn2 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
                         with gr.Column():
-                            txt_rec3 = gr.Image(label="3rd Match", type="pil", elem_classes="custom-card")
+                            txt_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                             txt_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
-                            txt_edit_btn3 = gr.Button("🪄 Edit Match", elem_classes="edit-btn")
-            txt_submit_btn.click(fn=generate_and_recommend, inputs=[text_input], outputs=[gen_output, txt_rec1, txt_score1, txt_rec2, txt_score2, txt_rec3, txt_score3])
-            
-        # --- TAB 4: Magic Edit (המעקף) ---
-        with gr.TabItem("🎨 Magic Edit", id="tab_magic"):
-            gr.Markdown("Your selected match has been sent here. **Please follow the steps below to edit.**")
-            with gr.Row():
-                with gr.Column(scale=1):
-                    # כפתור הטעינה החדש שפותר את הבאג
-                    load_magic_btn = gr.Button("1️⃣ CLICK HERE FIRST: Load Image to Editor", variant="primary")
-                    magic_editor = gr.ImageEditor(label="2️⃣ Paint over object", type="pil", elem_classes="custom-card", brush=gr.Brush(colors=["#FFFFFF"]))
-                    magic_prompt = gr.Textbox(label="3️⃣ What do you want instead?", placeholder="e.g., A modern yellow leather sofa", lines=2)
-                    magic_submit_btn = gr.Button("4️⃣ Apply Magic Edit", variant="primary")
-                    
-                with gr.Column(scale=1):
-                    magic_output = gr.Image(label="Magic Result", type="pil", elem_classes="custom-card")
 
-            # פעולת הטעינה הישירה
-            load_magic_btn.click(fn=load_into_editor, inputs=[shared_edit_image], outputs=[magic_editor])
-            
-            # פעולת העריכה
-            magic_submit_btn.click(fn=process_magic_edit, inputs=[magic_editor, magic_prompt], outputs=[magic_output])
+            txt_submit_btn.click(
+                fn=generate_and_recommend, inputs=[text_input],
+                outputs=[gen_output, txt_rec1, txt_score1, txt_rec2, txt_score2, txt_rec3, txt_score3]
+            )
 
-        # --- TAB 5 ---
-        with gr.TabItem("🎥 Presentation Video", id="tab_video"):
+        # --- TAB 4: Presentation Video ---
+        with gr.TabItem("🎥 Presentation Video"):
+            gr.Markdown("### 🎓 Project Presentation & Walkthrough \nWatch the video below to see a full walkthrough of the dataset, EDA, model pipeline, and the live application.")
             gr.Video(value="https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/resolve/main/A3.presentation.mp4", interactive=False)
 
-    # חיווט הכפתורים אל הזיכרון החבוי והחלפת הטאב
-    edit_buttons_and_sources = [
-        (img_edit_btn1, img_rec1), (img_edit_btn2, img_rec2), (img_edit_btn3, img_rec3),
-        (ft_edit_btn1, ft_rec1), (ft_edit_btn2, ft_rec2), (ft_edit_btn3, ft_rec3),
-        (txt_edit_btn1, txt_rec1), (txt_edit_btn2, txt_rec2), (txt_edit_btn3, txt_rec3)
-    ]
-    for btn, source_img in edit_buttons_and_sources:
-        btn.click(fn=send_to_holding_state, inputs=[source_img], outputs=[shared_edit_image, main_tabs])
-
 demo.launch()