import gradio as gr
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import cv2
from keras.models import load_model

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

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)
    encoded_features = autoencoder.predict(img)

    return encoded_features
    
def inference(image):

    input_image = process_image(image)
    
    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 = result_image['label'] + result_image['timestamp']
    
    return result
    


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