app.py

# pylint: disable=import-error
import gradio as gr
import numpy as np
import torch
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
from transformers import ViTForImageClassification, ViTImageProcessor

# Load pre-trained Vision Transformer model
model_name = "google/vit-base-patch16-224"
model = ViTForImageClassification.from_pretrained(model_name)
processor = ViTImageProcessor.from_pretrained(model_name)

# Function to predict image class
def classify_image(image):
    if image is None:
        return None, None
    
    # Process image
    inputs = processor(images=image, return_tensors="pt")
    
    # Make prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
    
    # Get predicted class and probabilities
    predicted_class_idx = logits.argmax(-1).item()
    predicted_class = model.config.id2label[predicted_class_idx]
    
    # Get top 5 predictions
    probs = torch.nn.functional.softmax(logits, dim=-1)[0]
    top5_prob, top5_indices = torch.topk(probs, 5)
    
    # Create plot for visualization
    fig, ax = plt.subplots(figsize=(10, 5))
    
    # Get class names and probabilities
    classes = [model.config.id2label[idx.item()] for idx in top5_indices]
    probabilities = [prob.item() * 100 for prob in top5_prob]
    
    # Create horizontal bar chart
    bars = ax.barh(classes, probabilities, color='#4C72B0')
    ax.set_xlabel('Probability (%)')
    ax.set_title('Top 5 Predictions')
    
    # Add percentage labels
    for i, bar in enumerate(bars):
        width = bar.get_width()
        ax.text(width + 1, bar.get_y() + bar.get_height()/2, 
                f'{probabilities[i]:.1f}%', 
                va='center', fontsize=10)
    
    # Improve layout
    plt.tight_layout()
    
    return predicted_class, fig

# Create Gradio interface
with gr.Blocks(title="Image Classifier", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🖼️ Image Classification Tool
        
        This application uses a Vision Transformer (ViT) model to classify images into 1,000 different categories.
        
        Upload an image or take a photo to see what the AI recognizes in it!
        """
    )
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(
                label="Upload or capture an image",
                type="pil",
                height=400
            )
            classify_btn = gr.Button("Classify Image", variant="primary")
        
        with gr.Column():
            prediction = gr.Textbox(label="Prediction")
            confidence_plot = gr.Plot(label="Confidence Levels")
    
    # Add examples
    example_images = [
        "examples/dog.jpg",
        "examples/cat.jpg",
        "examples/coffee.jpg",
        "examples/laptop.jpg",
        "examples/beach.jpg"
    ]
    
    gr.Examples(
        examples=example_images,
        inputs=image_input,
        outputs=[prediction, confidence_plot],
        fn=classify_image,
        cache_examples=True
    )
    
    # Set up the click event
    classify_btn.click(
        fn=classify_image,
        inputs=image_input,
        outputs=[prediction, confidence_plot]
    )
    
    # Set up the input change event
    image_input.change(
        fn=classify_image,
        inputs=image_input,
        outputs=[prediction, confidence_plot]
    )
    
    gr.Markdown("""
    ### How it works
    
    This tool uses a Vision Transformer (ViT) model pre-trained on ImageNet, enabling it to recognize 1,000 
    different object categories ranging from animals and plants to vehicles, household items, and more.
    
    ### Applications
    
    - **Content Categorization**: Automatically organize image libraries
    - **Accessibility**: Help describe images for visually impaired users
    - **Education**: Learn about objects in the world around you
    - **Data Analysis**: Process and categorize large image datasets
    
    Created by [Vinicius Guerra e Ribas](https://viniciusgribas.netlify.app/)
    """)

# Launch the app
if __name__ == "__main__":
    demo.launch()