Create app.py

app.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()