app.py

import os
import numpy as np
import pandas as pd
import gradio as gr
import tensorflow as tf
from annoy import AnnoyIndex
from tensorflow import keras


def load_image(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)
    image = tf.image.resize(image, (224, 224))
    image = tf.image.convert_image_dtype(image, tf.float32)
    image = image/255.
    return image.numpy()


# Specify Database Path
database_path = './AnimalSubset'

# Create Example Images
class_names = []
with open('./Animal-ClassNames.txt', mode='r') as names:
    class_names = names.read().split(',')[:-1]

example_image_paths = [
    './AnimalSubset/Beetle/Beetle-Train (101).jpeg',
    './AnimalSubset/Butterfly/Butterfly-train (1042).jpeg',
    './AnimalSubset/Cat/Cat-Train (1004).jpeg',
    './AnimalSubset/Cow/Cow-Train (1022).jpeg',
    './AnimalSubset/Dog/Dog-Train (1144).jpeg',
    './AnimalSubset/Elephant/Elephant-Train (1043).jpeg',
    './AnimalSubset/Gorilla/Gorilla (1045).jpeg',
    './AnimalSubset/Hippo/Hippo - Train (1133).jpeg',
    './AnimalSubset/Lizard/Lizard-Train (161).jpeg',
    './AnimalSubset/Monkey/M (224).jpeg',
    './AnimalSubset/Mouse/Mouse-Train (1225).jpeg',
    './AnimalSubset/Panda/Panda (1992).jpeg',
    './AnimalSubset/Spider/Spider-Train (1191).jpeg',
    './AnimalSubset/Tiger/Tiger (1020).jpeg',
    './AnimalSubset/Zebra/Zebra (975).jpeg'
]
example_images = [load_image(path) for path in example_image_paths]

# Load Feature Extractor
feature_extractor_path = './Animal-FeatureExtractor.keras'
feature_extractor = keras.models.load_model(
    feature_extractor_path, compile=False)

# Load Annoy index
index_path = './AnimalSubset.ann'
annoy_index = AnnoyIndex(256, 'angular')
annoy_index.load(index_path)


def similarity_search(
    query_image, num_images=5, *_,
    feature_extractor=feature_extractor,
    annoy_index=annoy_index,
    database_path=database_path,
    metadata_path='./Animals.csv'
):

    if np.max(query_image) == 255:
        query_image = query_image/255.

    query_vector = feature_extractor.predict(
        query_image[np.newaxis, ...], verbose=0)[0]

    # Compute nearest neighbors
    nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_images)

    # Load metadata
    metadata = pd.read_csv(metadata_path, index_col=0)
    metadata = metadata.iloc[nearest_neighbors]
    closest_class = metadata.class_name.values[0]

    # Similar Images
    similar_images = [
        load_image(os.path.join(database_path, class_name, file_name))
        for class_name, file_name in zip(metadata.class_name.values, metadata.file_name.values)
    ]
    
    # return closest_class, similar_images

    image_gallery = gr.Gallery(
        value=similar_images,
        label='Similar Images',
        object_fit='fill',
        preview=True,
        visible=True,
    )
    return closest_class, image_gallery


# Gradio Application
with gr.Blocks(theme='soft') as app:

    gr.Markdown("# Animal - Content Based Image Retrieval (CBIR)")
    gr.Markdown(f"Model only supports: {', '.join(class_names[:-1])} and {class_names[-1]}")
    gr.Markdown("Disclaimer:- Model might suggest incorrect images, try using a different image.")

    with gr.Row(equal_height=True):
        # Image Input
        query_image = gr.Image(
            label='Query Image',
            sources=['upload', 'clipboard'],
            height='50vh'
        )

        # Output Gallery Display
        output_gallery = gr.Gallery(visible=False)

    with gr.Row(equal_height=True):

        # Predicted Class
        pred_class = gr.Textbox(
            label='Predicted Class', placeholder='Let the model think!!...')

        # Number of images to search
        n_images = gr.Slider(
            value=10,
            label='Number of images to search',
            minimum=1,
            maximum=99,
            step=1
        )

        # Search Button
        search_btn = gr.Button('Search')

    # Example Images
    examples = gr.Examples(
        examples=example_images,
        inputs=query_image,
        label='Something similar to me??',
    )

    # Search - On Click
    search_btn.click(
        fn=similarity_search,
        inputs=[query_image, n_images],
        outputs=[pred_class, output_gallery]
    )

if __name__ == '__main__':
    app.launch()
    # pred_class, sim_images = similarity_search(example_images[class_names.index('Spider')])
    # print(pred_class)