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A newer version of the Gradio SDK is available: 6.24.0
title: Nature Recommender
emoji: 👀
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 6.14.0
python_version: '3.13'
app_file: app.py
pinned: false
license: mit
🌿 Nature Scene Recommender
A visual similarity search app — upload a nature image and get the 3 most similar scenes from a dataset of 15,000 nature images, using CLIP embeddings and cosine similarity.
🎬 Presentation Video
📁 Part 1: Dataset
Dataset: mertcobanov/nature-dataset
Source: Hugging Face — a curated collection of high-quality nature and landscape images.
Size: 50,000 images with natural scene captions.
Features:
image— RGB photo of a natural landscape (512x512px)caption— a short textual description of the scene (e.g., "a large mountain with a very tall peak")
Use case: Given an input landscape image, the app finds the Top 3 most visually similar nature scenes using image embeddings and cosine similarity.
📊 Part 2: Exploratory Data Analysis (EDA)
The dataset contains 50,000 nature images — mountains, deserts, forests, waterfalls and beaches — each with a text caption. Every image is 512x512px.
Caption statistics:
- Total captions: 50,000
- Average caption length: ~10 words
- Min: 4 words | Max: 16 words
Sanity Checks: No missing images, no missing captions, all images are RGB and the same size. The dataset is clean and ready.
Most Common Words in Captions
The 15 most common words reveal the diversity of the dataset — mountain, desert, trees, waterfall, forest...
Sample Images from Dataset
8 sample images showing the variety of nature scenes in the dataset, each with its caption.
🧠 Part 3: Embeddings
Model: openai/clip-vit-base-patch32 — trained on millions of image-text pairs, enabling it to understand complex visual relationships.
Sample size: 15,000 images out of 50,000.
Embedding size: 768 dimensions per image — each image is converted to a numerical vector representing its visual content.
How it works: Two images of mountains will get very similar vectors. A mountain image and a beach image will get very different vectors.
3.1 PCA Visualization
PCA reduces the 768 dimensions to 2 for visualization. The first plot shows the sample index as color, the second shows the K-Means cluster assignment.
3.2 K-Means Clustering
Applied K-Means with 8 clusters. Each cluster represents a different type of nature scene.
t-SNE Visualization
t-SNE provides a clearer picture of the clusters — the 8 groups are much more distinct here. CLIP successfully grouped visually and semantically similar nature scenes together without any manual labeling.
Cluster distribution:
| Cluster | Size | Theme |
|---|---|---|
| Cluster 0 | 1,487 | 🏜️ Desert & People |
| Cluster 1 | 3,261 | 🏔️💧 Waterfalls & Mountains |
| Cluster 2 | 900 | 🌲 Dense Forests & Trees |
| Cluster 3 | 1,810 | 🪨 Rocky Landscapes |
| Cluster 4 | 1,016 | 🌊 Coastal & Open Scenes |
| Cluster 5 | 2,042 | ⬛ Mixed B&W & Rocky |
| Cluster 6 | 1,831 | 🌵 Desert Vegetation |
| Cluster 7 | 2,653 | ⛰️ Mountain Forests |
3.4 Saved Embeddings
The embeddings were saved to a nature_embeddings.parquet file containing the original image index, caption, and embedding vector — so the Space can use them directly without recomputing every time.
⚙️ Part 4: Inputs & Outputs
4.1 Prepare Embeddings
Loaded the embeddings file — extracting the embedding matrix, captions, and original image indices.
- Loaded embeddings shape: (15,000, 768)
4.2 User Input
A function that converts any uploaded image into a 768-dimensional embedding vector using CLIP — exactly like we did for the dataset images.
4.3 Similarity Scores
A function that calculates cosine similarity between the uploaded image's vector and all 15,000 vectors in the dataset — returning a score between 0 (completely different) and 1 (identical).
4.4 Top 3 Elements
The main recommendation function — converts the image to an embedding, calculates similarity against all 15,000 images, and returns the 3 most similar ones with their captions and similarity scores.
🚀 Part 5: HuggingFace Space
The app is built with Gradio and hosted on HuggingFace Spaces.
How to use:
- Upload a nature image
- Click Find Similar Scenes 🔍
- Get the Top 3 most similar scenes with scene type, caption, and similarity score
🎁 Bonus
Bonus 1: Similarity Score Visualization
Visualizing the top 10 cosine similarity scores for a query image — higher scores indicate greater visual similarity between embeddings.
Bonus 2: Auto-Classification of Nature Scenes
Each image is automatically classified into a nature scene category based on caption keywords: Mountain, Water, Forest, Desert, Coastal.
Distribution:
- 🏔️ Mountain: 6,796 images (45.3%)
- 💧 Water: 3,147 images (21.0%)
- 🌲 Forest: 1,835 images (12.2%)
- 🌿 Other Nature: 1,341 images (8.9%)
- 🏜️ Desert: 1,323 images (8.8%)
- 🌊 Coastal: 558 images (3.7%)
Bonus 3: Interactive Cluster Visualization
An interactive t-SNE plot where each point represents an image embedding, colored by cluster. Hover over any point to see the image caption.
📝 Submission
Submitted on Moodle:
https://huggingface.co/spaces/Danielhalali/nature-recommender