app.py

import gradio as gr
import pandas as pd
import numpy as np
import torch
from transformers import CLIPProcessor, CLIPModel
from datasets import load_dataset
from diffusers import StableDiffusionPipeline
from sklearn.metrics.pairwise import cosine_similarity
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%; 
    background-position: 0% 0%;
    background-attachment: fixed;
    animation: panBackground 40s ease-in-out infinite alternate; 
}

@keyframes panBackground {
    0% { background-position: 0% 0%; }
    100% { background-position: 100% 100%; }
}

.gradio-container {
    background: rgba(20, 20, 20, 0.75) !important; 
    border-radius: 20px;
    box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.5);
    backdrop-filter: blur(5px); 
    border: 1px solid rgba(255, 255, 255, 0.1);
}


.visionary-title {
    font-size: 3.5rem;
    font-weight: 900;
    text-align: center;
    letter-spacing: 4px;
    margin-bottom: 5px;
    margin-top: 15px;
    background: linear-gradient(270deg, #b8860b, #ffd700, #fff8dc, #b8860b);
    background-size: 200% 200%;
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    animation: goldShine 4s ease infinite;
    transition: transform 0.3s ease, text-shadow 0.3s ease;
    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;
    border: 1px solid rgba(255, 215, 0, 0.15) !important; 
    transition: all 0.3s ease-in-out !important; 
    overflow: hidden !important;
    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 {
    background: linear-gradient(90deg, #151515, #252525) !important;
    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;
}
"""

# --- 1. Load Dataset & Embeddings ---
print("Loading Dataset and Embeddings...")
full_ds = load_dataset("tonijhanel/my_interior_design_dataset", split="train")
sample_dataset = full_ds.shuffle(seed=42).select(range(5000))

df_saved = pd.read_parquet("interior_embeddings.parquet")
dataset_matrix = np.array(df_saved['embedding'].tolist())

# --- 2. Load Deep Learning Models ---
device = "cuda" if torch.cuda.is_available() else "cpu"

print("Loading CLIP Model...")
clip_id = "openai/clip-vit-base-patch32"
processor = CLIPProcessor.from_pretrained(clip_id)
clip_model = CLIPModel.from_pretrained(clip_id).to(device)

print("Loading Stable Diffusion Model...")
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float32)
pipe = pipe.to(device)
pipe.enable_attention_slicing()

# --- 3. Core Engine Logic ---

def get_recommendations_from_vector(user_vector):
    similarities = cosine_similarity(user_vector, dataset_matrix)[0]
    top_indices = np.argsort(similarities)[-3:][::-1]
    top_scores = similarities[top_indices]
    
    recs = [sample_dataset[int(i)]['image'] for i in top_indices]
    scores = [f"Match: {score*100:.1f}%" for score in top_scores]
    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, ""
        
    inputs = processor(images=user_image, return_tensors="pt").to(device)
    with torch.no_grad():
        features = clip_model.get_image_features(**inputs)
        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, ""
        
    inputs = processor(text=[prompt], return_tensors="pt", padding=True).to(device)
    with torch.no_grad():
        features = clip_model.get_text_features(**inputs)
        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, ""
        
    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)
        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

# --- 4. Gradio User Interface (Premium UI) ---

custom_theme = gr.themes.Monochrome(
    primary_hue="neutral",
    secondary_hue="neutral",
    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:
    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():
        
        # --- 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", elem_classes="custom-card")
                            img_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            img_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                            img_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            img_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                            img_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                            
            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(
                        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", elem_classes="custom-card")
                            ft_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            ft_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                            ft_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            ft_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                            ft_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")

            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", elem_classes="custom-card")
                            txt_score1 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            txt_rec2 = gr.Image(label="2nd Match", elem_classes="custom-card")
                            txt_score2 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")
                        with gr.Column():
                            txt_rec3 = gr.Image(label="3rd Match", elem_classes="custom-card")
                            txt_score3 = gr.Textbox(label="Confidence", interactive=False, elem_classes="custom-score")

            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: 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)

demo.launch()