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README (1).md

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+---
+title: WikiArt Painting Classifier
+emoji: 🎨
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# WikiArt Painting Classifier - Interactive Demo
+
+An interactive Gradio application for classifying paintings by artist, genre, and style.
+
+## Features
+
+- **Multi-Task Classification**: Predicts artist, genre, and style simultaneously
+- **129 Artists**: Including Monet, Van Gogh, Picasso, Da Vinci, Rembrandt, and more
+- **11 Genres**: Portrait, landscape, abstract painting, still life, cityscape, etc.
+- **27 Art Styles**: Impressionism, Cubism, Renaissance, Baroque, Expressionism, and more
+- **Real-Time Predictions**: Instant results with confidence scores
+
+## How to Use
+
+1. Upload a painting image using the interface
+2. Click "Classify Painting" or wait for automatic classification
+3. View predictions for:
+   - Top 10 likely artists with confidence scores
+   - Genre classification with probabilities
+   - Top 10 likely art styles with confidence scores
+
+## Model Details
+
+This app uses the model hosted at: [meetran/painting-classifier-keras-v1](https://huggingface.co/meetran/painting-classifier-keras-v1)
+
+### Architecture
+- **Base Model**: MobileNetV2 (pre-trained on ImageNet)
+- **Approach**: Multi-head classification with shared feature extraction
+- **Framework**: TensorFlow/Keras
+- **Input Size**: 224x224 RGB images
+
+### Training Details
+- **Dataset**: WikiArt dataset with 84,440 paintings
+- **Split**: 75% training, 25% validation
+- **Augmentation**: Random horizontal flip, rotation, zoom
+- **Training Stages**: Frozen backbone (2 epochs) + full fine-tuning (10 epochs)
+
+## Supported Classes
+
+### Artists (129 classes)
+Claude Monet, Vincent van Gogh, Pablo Picasso, Leonardo da Vinci, Rembrandt, Salvador Dali, Edgar Degas, Pierre-Auguste Renoir, Paul Cezanne, Henri Matisse, Michelangelo, Raphael, Edvard Munch, Gustav Klimt, Wassily Kandinsky, and 114 more.
+
+### Genres (11 classes)
+Abstract painting, Cityscape, Genre painting, Illustration, Landscape, Nude painting, Portrait, Religious painting, Sketch and study, Still life, Unknown Genre
+
+### Art Styles (27 classes)
+Impressionism, Post-Impressionism, Cubism, Abstract Expressionism, Early Renaissance, High Renaissance, Northern Renaissance, Mannerism Late Renaissance, Baroque, Rococo, Romanticism, Realism, Expressionism, Symbolism, Art Nouveau, Fauvism, Pointillism, Naive Art Primitivism, Synthetic Cubism, Analytical Cubism, Action painting, Color Field Painting, Contemporary Realism, New Realism, Minimalism, Pop Art, Ukiyo-e
+
+## Limitations
+
+- Best performance on Western classical and modern art from the training distribution
+- May have reduced accuracy on contemporary art, digital art, or non-Western styles
+- "Unknown Artist" and "Unknown Genre" classes may have lower precision
+- Requires clear, well-lit images for optimal results
+
+## License
+
+MIT License
+
+## Links
+
+- **Model Repository**: https://huggingface.co/meetran/painting-classifier-keras-v1
+- **Training Dataset**: [WikiArt on Hugging Face](https://huggingface.co/datasets/huggan/wikiart)

app (1).py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mb_preprocess
+import numpy as np
+import json
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+# Download model and labels from Model Hub repository
+print("Downloading model from Hugging Face Model Hub...")
+model_path = hf_hub_download(
+    repo_id="meetran/painting-classifier-keras-v1",
+    filename="wikiart_mobilenetv2_multihead.keras"
+)
+labels_path = hf_hub_download(
+    repo_id="meetran/painting-classifier-keras-v1",
+    filename="class_labels.json"
+)
+
+print("Model and labels downloaded successfully")
+
+# Load class labels
+with open(labels_path, "r", encoding="utf-8") as f:
+    class_labels = json.load(f)
+
+artist_names = class_labels["artist_names"]
+genre_names = class_labels["genre_names"]
+style_names = class_labels["style_names"]
+
+# Load the trained model
+print("Loading model...")
+model = tf.keras.models.load_model(model_path)
+print("Model loaded successfully")
+
+IMG_SIZE = (224, 224)
+
+def preprocess_image(image):
+    """Preprocess input image for model inference"""
+    img = np.array(image)
+    img = tf.image.resize(img, IMG_SIZE)
+    img = mb_preprocess(img)
+    img = tf.expand_dims(img, axis=0)
+    return img
+
+def classify_painting(image):
+    """Classify painting by artist, genre, and style"""
+    if image is None:
+        return None, None, None
+    
+    try:
+        # Preprocess image
+        processed_img = preprocess_image(image)
+        
+        # Get predictions
+        predictions = model.predict(processed_img, verbose=0)
+        
+        # Process artist predictions
+        artist_probs = tf.nn.softmax(predictions['artist'][0]).numpy()
+        artist_dict = {artist_names[i]: float(artist_probs[i]) 
+                       for i in range(len(artist_names))}
+        
+        # Process genre predictions
+        genre_probs = tf.nn.softmax(predictions['genre'][0]).numpy()
+        genre_dict = {genre_names[i]: float(genre_probs[i]) 
+                      for i in range(len(genre_names))}
+        
+        # Process style predictions
+        style_probs = tf.nn.softmax(predictions['style'][0]).numpy()
+        style_dict = {style_names[i]: float(style_probs[i]) 
+                      for i in range(len(style_names))}
+        
+        return artist_dict, genre_dict, style_dict
+    
+    except Exception as e:
+        print(f"Error during classification: {e}")
+        return None, None, None
+
+# Create Gradio interface
+with gr.Blocks(title="WikiArt Painting Classifier", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# WikiArt Painting Classifier")
+    gr.Markdown(
+        "Upload a painting image to classify its Artist (129 classes), "
+        "Genre (11 classes), and Style (27 classes) using a MobileNetV2-based multi-task model."
+    )
+    gr.Markdown(
+        "**Model Repository**: [meetran/painting-classifier-keras-v1]"
+        "(https://huggingface.co/meetran/painting-classifier-keras-v1)"
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Painting Image")
+            classify_btn = gr.Button("Classify Painting", variant="primary", size="lg")
+            
+            gr.Markdown("### Tips for Best Results")
+            gr.Markdown(
+                "- Upload clear, high-quality images of paintings\n"
+                "- Works best with Western classical and modern art\n"
+                "- Supports paintings from 129 famous artists\n"
+                "- Can identify 27 different art styles"
+            )
+            
+        with gr.Column():
+            artist_output = gr.Label(label="Artist Prediction (Top 10)", num_top_classes=10)
+            genre_output = gr.Label(label="Genre Prediction", num_top_classes=5)
+            style_output = gr.Label(label="Art Style Prediction (Top 10)", num_top_classes=10)
+    
+    gr.Markdown("---")
+    gr.Markdown("### Model Information")
+    gr.Markdown(
+        "- **Architecture**: MobileNetV2 (ImageNet pre-trained) with multi-head classification\n"
+        "- **Dataset**: WikiArt dataset containing 84,440 paintings\n"
+        "- **Training**: Two-stage training (frozen backbone + fine-tuning)\n"
+        "- **Input Size**: 224x224 RGB images\n"
+        "- **Framework**: TensorFlow/Keras\n\n"
+        "**Notable Artists**: Claude Monet, Vincent van Gogh, Pablo Picasso, Leonardo da Vinci, "
+        "Rembrandt, Salvador Dali, Michelangelo, Edgar Degas, Paul Cezanne, Henri Matisse, and 119 more.\n\n"
+        "**Art Styles**: Impressionism, Cubism, Renaissance, Baroque, Expressionism, "
+        "Abstract Expressionism, Realism, Pop Art, Romanticism, Symbolism, and 17 more."
+    )
+    
+    # Connect button to function
+    classify_btn.click(
+        fn=classify_painting,
+        inputs=image_input,
+        outputs=[artist_output, genre_output, style_output]
+    )
+    
+    # Auto-classify on image upload
+    image_input.change(
+        fn=classify_painting,
+        inputs=image_input,
+        outputs=[artist_output, genre_output, style_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

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requirements (1).txt

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+gradio>=5.49.1
+tensorflow>=2.15.0
+pillow>=10.0.0
+numpy>=1.24.0
+huggingface-hub>=0.20.0