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README (1).md

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+---
+title: Wonder Finder
+emoji: 🌍
+colorFrom: yellow
+colorTo: yellow
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🌍 Wonder Finder
+
+Visual recommender for the 12 Wonders of the World, powered by CLIP embeddings.
+
+## What it does
+- **Image search:** upload a travel photo → get the 3 most visually similar wonders
+- **Text search:** describe a place in natural language → get the 3 closest matching wonders
+
+## How it works
+1. The catalog (11,544 images across 12 wonder classes) is pre-embedded using CLIP ViT-B/32.
+2. User input (image or text) is embedded into the same 512-D space.
+3. Cosine similarity ranks the catalog; the top 3 results are returned with a diversity filter to avoid duplicates.
+
+## Dataset
+[chavajaz/wonders_dataset](https://huggingface.co/datasets/chavajaz/wonders_dataset) — CC0-1.0 licensed, ~960 images per class on average.
+
+## Model
+[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) — chosen for its joint image-text embedding space, which enables both image and text input through a single model.
+
+## Cluster analysis
+K-Means at k=12 on the embeddings achieved **ARI = 0.890** and **NMI = 0.927** against ground-truth wonder labels, indicating CLIP's pretrained space already separates the 12 wonders almost perfectly without supervision.
+
+## Files
+- `app.py` — the Gradio application
+- `requirements.txt` — pinned dependencies
+- `wonders_embeddings.parquet` — precomputed CLIP embeddings (one row per catalog image, column `embedding`, aligned 1:1 with the dataset in `train → validation → test` order)

app (1).py

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+import gradio as gr
+import torch
+import numpy as np
+import pandas as pd
+from transformers import CLIPProcessor, CLIPModel
+from datasets import load_dataset, concatenate_datasets
+
+# ============================================================
+# LOAD EVERYTHING ON STARTUP (runs once when the Space boots)
+# ============================================================
+
+print("Loading CLIP model...")
+MODEL_NAME = "openai/clip-vit-base-patch32"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+clip_model = CLIPModel.from_pretrained(MODEL_NAME).to(device)
+clip_model.eval()
+processor = CLIPProcessor.from_pretrained(MODEL_NAME)
+
+print("Loading dataset...")
+ds = load_dataset("chavajaz/wonders_dataset")
+# Concatenate whatever splits exist, in a stable order, instead of assuming
+# train/validation/test are all present.
+split_order = [s for s in ["train", "validation", "test"] if s in ds]
+split_order += [s for s in ds.keys() if s not in split_order]
+splits = [ds[s] for s in split_order]
+full_ds = concatenate_datasets(splits) if len(splits) > 1 else splits[0]
+class_names = full_ds.features["label"].names
+
+print("Loading precomputed embeddings...")
+embeddings_df = pd.read_parquet("wonders_embeddings.parquet")
+image_embeddings = np.array(embeddings_df["embedding"].tolist(), dtype=np.float32)
+EMBEDDINGS_TENSOR = torch.tensor(image_embeddings, device=device, dtype=torch.float32)
+# Defensively L2-normalize so cosine similarity and the diversity threshold
+# are correct even if the stored vectors weren't normalized.
+EMBEDDINGS_TENSOR = EMBEDDINGS_TENSOR / EMBEDDINGS_TENSOR.norm(dim=-1, keepdim=True).clamp_min(1e-12)
+
+if len(full_ds) != EMBEDDINGS_TENSOR.shape[0]:
+    print(
+        f"WARNING: dataset has {len(full_ds)} images but the embeddings file has "
+        f"{EMBEDDINGS_TENSOR.shape[0]} rows. They must line up 1:1 and be in the "
+        f"same order for results to be correct."
+    )
+
+print(f"Ready. {len(full_ds)} images, embeddings {image_embeddings.shape}, on {device}")
+
+# ============================================================
+# CORE FUNCTIONS
+# ============================================================
+
+@torch.no_grad()
+def embed_image(pil_image):
+    img = pil_image.convert("RGB")
+    inputs = processor(images=img, return_tensors="pt").to(device)
+    feats = clip_model.get_image_features(**inputs)
+    if not isinstance(feats, torch.Tensor):
+        if hasattr(feats, "image_embeds") and feats.image_embeds is not None:
+            feats = feats.image_embeds
+        elif hasattr(feats, "pooler_output") and feats.pooler_output is not None:
+            feats = feats.pooler_output
+        else:
+            feats = feats[0]
+    feats = feats / feats.norm(dim=-1, keepdim=True)
+    return feats
+
+@torch.no_grad()
+def embed_text(text):
+    inputs = processor(text=[text], return_tensors="pt", padding=True, truncation=True).to(device)
+    feats = clip_model.get_text_features(**inputs)
+    if not isinstance(feats, torch.Tensor):
+        if hasattr(feats, "text_embeds") and feats.text_embeds is not None:
+            feats = feats.text_embeds
+        elif hasattr(feats, "pooler_output") and feats.pooler_output is not None:
+            feats = feats.pooler_output
+        else:
+            feats = feats[0]
+    feats = feats / feats.norm(dim=-1, keepdim=True)
+    return feats
+
+def recommend(query_embedding, top_k=3, diversity_threshold=0.98):
+    sims = (query_embedding @ EMBEDDINGS_TENSOR.T).squeeze(0)
+    top_scores, top_indices = sims.topk(min(top_k * 20, len(sims)))
+    top_scores = top_scores.cpu().tolist()
+    top_indices = top_indices.cpu().tolist()
+
+    results = []
+    chosen_embeddings = []
+    for score, idx in zip(top_scores, top_indices):
+        candidate_emb = EMBEDDINGS_TENSOR[idx]
+        too_similar = any(
+            (candidate_emb @ prev_emb).item() > diversity_threshold
+            for prev_emb in chosen_embeddings
+        )
+        if too_similar:
+            continue
+        item = full_ds[idx]
+        results.append({
+            "index": idx,
+            "score": score,
+            "image": item["image"],
+            "label_name": class_names[item["label"]],
+        })
+        chosen_embeddings.append(candidate_emb)
+        if len(results) >= top_k:
+            break
+    return results
+
+def recommend_from_image(input_image):
+    if input_image is None:
+        return [], "✋ Please upload an image to find matching wonders."
+    query_emb = embed_image(input_image)
+    results = recommend(query_emb, top_k=3)
+    gallery_items = [
+        (r["image"], f"{r['label_name'].replace('_', ' ').title()}  •  match {r['score']*100:.1f}%")
+        for r in results
+    ]
+    medals = ["🥇", "🥈", "🥉"]
+    summary = "Your top 3 wonder matches:\n\n" + "\n".join(
+        f"{medals[i]}  {r['label_name'].replace('_', ' ').title():<22}  similarity {r['score']:.3f}"
+        for i, r in enumerate(results)
+    )
+    return gallery_items, summary
+
+def recommend_from_text(text_query):
+    if not text_query or not text_query.strip():
+        return [], "✋ Please describe what you're looking for."
+    query_emb = embed_text(text_query)
+    results = recommend(query_emb, top_k=3)
+    gallery_items = [
+        (r["image"], f"{r['label_name'].replace('_', ' ').title()}  •  match {r['score']*100:.1f}%")
+        for r in results
+    ]
+    medals = ["🥇", "🥈", "🥉"]
+    summary = f'Best matches for "{text_query}":\n\n' + "\n".join(
+        f"{medals[i]}  {r['label_name'].replace('_', ' ').title():<22}  similarity {r['score']:.3f}"
+        for i, r in enumerate(results)
+    )
+    return gallery_items, summary
+
+# ============================================================
+# UI
+# ============================================================
+
+CUSTOM_CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600;700&family=Nunito:wght@400;600;700;800&display=swap');
+
+.gradio-container {
+    background: linear-gradient(135deg, #F5EBDD 0%, #EDE0CC 100%) !important;
+    font-family: 'Nunito', 'Quicksand', -apple-system, sans-serif !important;
+}
+
+/* Headings get the rounder, friendlier Quicksand */
+h1, h2, h3, h4 {
+    font-family: 'Quicksand', sans-serif !important;
+    letter-spacing: 0.3px !important;
+}
+
+/* ---------- HEADER ---------- */
+#header-block {
+    background: linear-gradient(135deg, #8B4513 0%, #A0522D 50%, #CD853F 100%);
+    padding: 36px 28px;
+    border-radius: 20px;
+    margin-bottom: 28px;
+    box-shadow: 0 8px 24px rgba(139, 69, 19, 0.25);
+    text-align: center;
+}
+#header-block h1 {
+    color: #FFF8E7 !important;
+    font-size: 2.8em !important;
+    font-weight: 700 !important;
+    margin: 0 !important;
+    text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
+}
+#header-block h3 {
+    color: #FFE4B5 !important;
+    font-weight: 500 !important;
+    margin: 10px 0 0 0 !important;
+}
+#header-block p {
+    color: #FFF8E7 !important;
+    margin-top: 14px !important;
+    font-size: 1.05em !important;
+    opacity: 0.95;
+}
+
+/* ---------- TABS (the big upgrade) ---------- */
+.tab-nav {
+    background: transparent !important;
+    border-bottom: none !important;
+    gap: 12px !important;
+    padding: 0 4px !important;
+    margin-bottom: 8px !important;
+}
+
+.tab-nav button {
+    background: #FFF8E7 !important;
+    border: 2px solid #D2B48C !important;
+    color: #8B4513 !important;
+    font-family: 'Nunito', sans-serif !important;
+    font-size: 1.15em !important;
+    font-weight: 700 !important;
+    padding: 14px 32px !important;
+    border-radius: 14px !important;
+    margin: 0 !important;
+    box-shadow: 0 2px 6px rgba(139, 69, 19, 0.12) !important;
+    transition: all 0.25s ease !important;
+    cursor: pointer !important;
+}
+
+.tab-nav button:hover {
+    background: #FFE8C8 !important;
+    border-color: #A0522D !important;
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(139, 69, 19, 0.25) !important;
+}
+
+.tab-nav button.selected {
+    background: linear-gradient(135deg, #8B4513 0%, #A0522D 100%) !important;
+    border-color: #8B4513 !important;
+    color: #FFF8E7 !important;
+    box-shadow: 0 6px 16px rgba(139, 69, 19, 0.4) !important;
+    transform: translateY(-2px);
+}
+
+/* ---------- BUTTONS ---------- */
+button.primary, .gr-button-primary {
+    background: linear-gradient(135deg, #8B4513 0%, #A0522D 100%) !important;
+    border: none !important;
+    color: #FFF8E7 !important;
+    font-family: 'Nunito', sans-serif !important;
+    font-weight: 700 !important;
+    font-size: 1.08em !important;
+    padding: 14px 30px !important;
+    border-radius: 12px !important;
+    box-shadow: 0 4px 12px rgba(139, 69, 19, 0.3) !important;
+    transition: all 0.2s ease !important;
+}
+button.primary:hover, .gr-button-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 6px 16px rgba(139, 69, 19, 0.45) !important;
+}
+
+/* ---------- INPUTS / PANELS ---------- */
+.gr-box, .gr-form, .gr-panel {
+    background: #FFF8E7 !important;
+    border: 2px solid #D2B48C !important;
+    border-radius: 14px !important;
+}
+
+label, .gr-input-label {
+    color: #5C4033 !important;
+    font-family: 'Nunito', sans-serif !important;
+    font-weight: 700 !important;
+    font-size: 1em !important;
+}
+
+textarea, input[type="text"] {
+    background: #FFFAF0 !important;
+    border: 2px solid #D2B48C !important;
+    color: #3E2723 !important;
+    font-family: 'Nunito', sans-serif !important;
+    font-size: 1.02em !important;
+    border-radius: 10px !important;
+    padding: 12px !important;
+}
+
+textarea:focus, input[type="text"]:focus {
+    border-color: #8B4513 !important;
+    outline: none !important;
+    box-shadow: 0 0 0 3px rgba(139, 69, 19, 0.15) !important;
+}
+
+.gr-gallery {
+    background: #FFF8E7 !important;
+    border: 2px solid #D2B48C !important;
+    border-radius: 14px !important;
+    padding: 10px !important;
+}
+
+/* ---------- FOOTER ---------- */
+#footer-block {
+    margin-top: 28px;
+    padding: 22px 24px;
+    background: rgba(139, 69, 19, 0.08);
+    border-radius: 14px;
+    border-left: 5px solid #8B4513;
+    color: #5C4033 !important;
+    font-family: 'Nunito', sans-serif !important;
+    line-height: 1.7;
+}
+#footer-block a {
+    color: #8B4513 !important;
+    font-weight: 700;
+    text-decoration: none;
+    border-bottom: 1px dashed #8B4513;
+}
+#footer-block a:hover {
+    color: #A0522D !important;
+}
+"""
+
+# Build sample image indices defensively so a smaller dataset can't crash startup.
+SAMPLE_IDX = [i for i in [50, 2000, 5000, 7500, 10000] if i < len(full_ds)]
+
+with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft(
+    primary_hue="orange", secondary_hue="amber", neutral_hue="stone",
+), title="Wonder Finder") as demo:
+
+    gr.HTML("""
+    <div id="header-block">
+        <h1>🌍 Wonder Finder</h1>
+        <h3>Discover the World's 12 Wonders Through AI Vision</h3>
+        <p>Upload a travel photo or describe a place — get the closest matches from 11,544 images.<br>
+        Powered by CLIP's joint image–text embedding space.</p>
+    </div>
+    """)
+
+    with gr.Tabs():
+        with gr.Tab("📷  Search by Image"):
+            gr.Markdown("### Upload your travel photo, and we'll find the wonders that look most like it.")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    img_input = gr.Image(type="pil", label="Drop your photo here", height=320)
+                    img_btn = gr.Button("✨  Find Similar Wonders", variant="primary", size="lg")
+                with gr.Column(scale=2):
+                    img_gallery = gr.Gallery(label="Top 3 Matches", columns=3, rows=1, height=320, object_fit="cover")
+                    img_summary = gr.Textbox(label="📊  Match Details", lines=6, show_copy_button=True)
+            if SAMPLE_IDX:
+                gr.Examples(
+                    examples=[[full_ds[i]["image"]] for i in SAMPLE_IDX],
+                    inputs=img_input,
+                    label="✨  Or try these sample images:",
+                )
+            img_btn.click(recommend_from_image, inputs=img_input, outputs=[img_gallery, img_summary])
+
+        with gr.Tab("💬  Search by Description"):
+            gr.Markdown("### Describe a place in your own words — CLIP translates language into visual matches.")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    text_input = gr.Textbox(
+                        label="Describe a wonder",
+                        placeholder='e.g. "an ancient stone temple in the jungle" or "a tall tower at sunset"',
+                        lines=3,
+                    )
+                    text_btn = gr.Button("✨  Find Matching Wonders", variant="primary", size="lg")
+                with gr.Column(scale=2):
+                    text_gallery = gr.Gallery(label="Top 3 Matches", columns=3, rows=1, height=320, object_fit="cover")
+                    text_summary = gr.Textbox(label="📊  Match Details", lines=6, show_copy_button=True)
+            gr.Examples(
+                examples=[
+                    ["ancient stone pyramid in the desert"],
+                    ["tall modern skyscraper at night"],
+                    ["waterfall in the tropical jungle"],
+                    ["ancient Roman amphitheater"],
+                    ["statue of a religious figure with outstretched arms"],
+                    ["a misty stone monument at sunrise"],
+                    ["white marble palace with a dome"],
+                ],
+                inputs=text_input,
+                label="✨  Or try these example queries:",
+            )
+            text_btn.click(recommend_from_text, inputs=text_input, outputs=[text_gallery, text_summary])
+
+    gr.HTML("""
+    <div id="footer-block">
+        <strong>About this app</strong><br>
+        <strong>Dataset:</strong> <a href="https://huggingface.co/datasets/chavajaz/wonders_dataset">chavajaz/wonders_dataset</a> — 11,544 images across 12 wonder classes (CC0).<br>
+        <strong>Model:</strong> <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP ViT-B/32</a> — embeds images and text into the same 512-D space for cross-modal retrieval.<br>
+        <strong>Method:</strong> L2-normalized cosine similarity over precomputed embeddings, with a diversity filter (threshold 0.98) to suppress near-duplicate results.
+    </div>
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements (1).txt

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+gradio==4.44.0
+transformers==4.45.2
+torch==2.4.1
+datasets==3.0.0
+pillow==10.4.0
+numpy==1.26.4
+pandas==2.2.2
+pyarrow==17.0.0
+huggingface-hub==0.25.0

wonders_embeddings.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec4b31afc69b0b81a15b06640df8fa427f0a1eba2f2fa9984a36444b456fe8f5
+size 36795559