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app.py

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+
+import os
+import numpy as np
+import pandas as pd
+import torch
+import gradio as gr
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+from datasets import load_dataset
+import random
+
+# =============================================================================
+# SETUP
+# =============================================================================
+
+print("Loading model and data...")
+
+# Device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load CLIP model
+MODEL_NAME = "openai/clip-vit-base-patch32"
+model = CLIPModel.from_pretrained(MODEL_NAME).to(device)
+processor = CLIPProcessor.from_pretrained(MODEL_NAME)
+model.eval()
+print("✓ CLIP model loaded")
+
+# Load embeddings and metadata
+embeddings = np.load("artwork_embeddings.npy")
+df = pd.read_csv("artwork_metadata.csv")
+EMBEDDINGS_TENSOR = torch.tensor(embeddings).to(device)
+print(f"✓ Loaded {len(embeddings)} embeddings")
+
+# Load dataset for images
+print("Loading WikiArt dataset (this may take a moment)...")
+full_dataset = load_dataset("huggan/wikiart", split="train")
+sample_indices = np.load("sample_indices.npy")
+dataset = full_dataset.select(sample_indices.tolist())
+print(f"✓ Dataset loaded: {len(dataset)} artworks")
+
+# =============================================================================
+# CORE FUNCTIONS
+# =============================================================================
+
+def get_image_embedding(image):
+    """Convert PIL image to CLIP embedding."""
+    image = image.convert("RGB")
+    inputs = processor(images=image, return_tensors="pt", padding=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    with torch.no_grad():
+        features = model.get_image_features(**inputs)
+        features = features / features.norm(dim=-1, keepdim=True)
+    return features
+
+def get_text_embedding(text):
+    """Convert text to CLIP embedding."""
+    inputs = processor(text=text, return_tensors="pt", padding=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    with torch.no_grad():
+        features = model.get_text_features(**inputs)
+        features = features / features.norm(dim=-1, keepdim=True)
+    return features
+
+def get_recommendations(query_embedding, top_k=5):
+    """Get top-k similar artworks."""
+    query_embedding = query_embedding.to(device)
+    similarities = torch.mm(query_embedding, EMBEDDINGS_TENSOR.T)[0]
+    top_scores, top_indices = torch.topk(similarities, top_k)
+    
+    results = []
+    for score, idx in zip(top_scores.cpu().numpy(), top_indices.cpu().numpy()):
+        artwork_info = df.iloc[idx]
+        results.append({
+            "index": int(idx),
+            "similarity": float(score),
+            "artist": artwork_info["artist"],
+            "genre": artwork_info["genre"],
+            "style": artwork_info["style"],
+            "image": dataset[int(idx)]["image"]
+        })
+    return results
+
+# =============================================================================
+# GRADIO FUNCTIONS
+# =============================================================================
+
+def recommend_from_text(text_query, num_results=5):
+    if not text_query.strip():
+        return [], "Please enter a description"
+    
+    query_emb = get_text_embedding(text_query)
+    recommendations = get_recommendations(query_emb, top_k=int(num_results))
+    
+    gallery_images = []
+    info_text = f"Results for: \"{text_query}\"\n\n"
+    
+    for i, rec in enumerate(recommendations):
+        gallery_images.append((rec["image"], f"{rec['style']} | {rec['artist'][:20]}"))
+        info_text += f"{i+1}. {rec['style']} by {rec['artist']} (Score: {rec['similarity']:.3f})\n"
+    
+    return gallery_images, info_text
+
+def recommend_from_image(image, num_results=5):
+    if image is None:
+        return [], "Please upload an image"
+    
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    
+    query_emb = get_image_embedding(image)
+    recommendations = get_recommendations(query_emb, top_k=int(num_results))
+    
+    gallery_images = []
+    info_text = "Similar artworks found:\n\n"
+    
+    for i, rec in enumerate(recommendations):
+        gallery_images.append((rec["image"], f"{rec['style']} | {rec['artist'][:20]}"))
+        info_text += f"{i+1}. {rec['style']} by {rec['artist']} (Score: {rec['similarity']:.3f})\n"
+    
+    return gallery_images, info_text
+
+# =============================================================================
+# GRADIO INTERFACE
+# =============================================================================
+
+with gr.Blocks(title="WikiArt Recommendation System", theme=gr.themes.Soft()) as demo:
+    
+    gr.Markdown("""
+    # 🎨 WikiArt Artwork Recommendation System
+    
+    Find similar artworks using AI! You can either:
+    - **Describe** what you're looking for in text
+    - **Upload** an image to find similar artworks
+    
+    *Powered by CLIP embeddings on 15,000 artworks from WikiArt*
+    """)
+    
+    with gr.Tabs():
+        with gr.TabItem("🔤 Search by Description"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    text_input = gr.Textbox(
+                        label="Describe the artwork you're looking for",
+                        placeholder="e.g., 'impressionist painting of a garden with flowers'",
+                        lines=3
+                    )
+                    text_num_results = gr.Slider(
+                        minimum=1, maximum=10, value=5, step=1,
+                        label="Number of results"
+                    )
+                    text_btn = gr.Button("🔍 Find Artworks", variant="primary")
+                
+                with gr.Column(scale=2):
+                    text_gallery = gr.Gallery(
+                        label="Recommended Artworks",
+                        columns=5,
+                        height=400,
+                        object_fit="contain"
+                    )
+            
+            text_info = gr.Textbox(label="Details", lines=6)
+            
+            text_btn.click(
+                fn=recommend_from_text,
+                inputs=[text_input, text_num_results],
+                outputs=[text_gallery, text_info]
+            )
+            
+            gr.Examples(
+                examples=[
+                    ["impressionist landscape with water and trees"],
+                    ["dark moody portrait with dramatic lighting"],
+                    ["abstract colorful geometric shapes"],
+                    ["religious painting with angels"],
+                    ["Japanese style artwork with nature"],
+                ],
+                inputs=text_input
+            )
+        
+        with gr.TabItem("🖼️ Search by Image"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    image_input = gr.Image(
+                        label="Upload an artwork image",
+                        type="pil"
+                    )
+                    image_num_results = gr.Slider(
+                        minimum=1, maximum=10, value=5, step=1,
+                        label="Number of results"
+                    )
+                    image_btn = gr.Button("🔍 Find Similar", variant="primary")
+                
+                with gr.Column(scale=2):
+                    image_gallery = gr.Gallery(
+                        label="Similar Artworks",
+                        columns=5,
+                        height=400,
+                        object_fit="contain"
+                    )
+            
+            image_info = gr.Textbox(label="Details", lines=6)
+            
+            image_btn.click(
+                fn=recommend_from_image,
+                inputs=[image_input, image_num_results],
+                outputs=[image_gallery, image_info]
+            )
+    
+    gr.Markdown("""
+    ---
+    **Dataset:** WikiArt (15,000 artworks) | **Model:** CLIP ViT-B/32 | **Assignment 3 - ML Course**
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

artwork_embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:599d408174194866e68dda6775c012c7360e4fd39f35a79d52a45869f94d0c72
+size 30720128

requirements.txt

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+gradio>=4.0.0
+torch
+transformers
+datasets
+numpy
+pandas
+Pillow

sample_indices.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:927c21f714b4d8807380dcf7b9ca1b1d919859d15b5ed1274607a337a64f9153
+size 120128