app.py

import gradio as gr
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import cv2
from keras.models import load_model
from keras.models import Model
from datasets import load_dataset
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

autoencoder = load_model("autoencoder_model.keras")
encoded_images = np.load("X_encoded_compressed.npy")

dataset = load_dataset("eybro/images-split")

num_clusters = 10  # Choose the number of clusters
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
kmeans.fit(encoded_images)

def create_url_from_title(title: str, timestamp: int):
    video_urls = load_dataset("eybro/video_urls")
    df = video_urls['train'].to_pandas()
    filtered = df[df['title'] == title]
    base_url = df["url"][0]
    return base_url + f"?t={timestamp}s"


def find_nearest_neighbors(encoded_images, input_image, top_n=5):
    """
    Find the closest neighbors to the input image in the encoded image space.
    Args:
    encoded_images (np.ndarray): Array of encoded images (shape: (n_samples, n_features)).
    input_image (np.ndarray): The encoded input image (shape: (1, n_features)).
    top_n (int): The number of nearest neighbors to return.
    Returns:
    List of tuples: (index, distance) of the top_n nearest neighbors.
    """
    # Compute pairwise distances
    distances = euclidean_distances(encoded_images, input_image.reshape(1, -1)).flatten()

    # Sort by distance
    nearest_neighbors = np.argsort(distances)[:top_n]
    return [(index, distances[index]) for index in nearest_neighbors]

def get_image(index):
    split = len(dataset["train"])
    if index < split:
        return dataset["train"][index]
    else:
        return dataset["test"][index-split]

def process_image(image):
    img = np.array(image)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  
    img = cv2.resize(img, (64, 64))  
    img = img.astype('float32')  
    img /= 255.0
    img = np.expand_dims(img, axis=0)

    layer_model = Model(inputs=autoencoder.input, outputs=autoencoder.layers[4].output)

    encoded_array = layer_model.predict(img) 

    pooled_array = encoded_array.max(axis=-1)
    return pooled_array  # Shape: (1, n_features)
    
def inference(image):
    """"""
    input_image = process_image(image)

    # input_image = encoded_images[2000]
    
    nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)
    
    # Print the results
    print("Nearest neighbors (index, distance):")
    for neighbor in nearest_neighbors:
        print(neighbor)
    
    top4 = [int(i[0]) for i in nearest_neighbors[:4]]
    print(f"top 4: {top4}")
    
    for i in top4:
      im = get_image(i)
      print(im["label"], im["timestamp"])

    result_image = get_image(top4[0])
    result = f"{result_image['label']} {result_image['timestamp']} \n{create_url_from_title(result_image['label'], result_image['timestamp'])}"

    n=2
    plt.figure(figsize=(8, 8))
    for i, (image1, image2) in enumerate(zip(top4[:2], top4[2:])):
        ax = plt.subplot(2, n, i + 1)
        image1 = get_image(image1)["image"]
        image2 = get_image(image2)["image"]
    
        plt.imshow(image1)
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    
        ax = plt.subplot(2, n, i + 1 + n)
        plt.imshow(image2)
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    
    return result
    


demo = gr.Interface(fn=inference, 
                    inputs=gr.Image(label='Upload image'),
                    outputs="text")
demo.launch()