Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.
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TASK: using below code we make advanced and professionally visualization using plotly module , make user generalised, robustness very professionally skilled to showcase your knowledge to teammates or share

NOte: save plots html format

#Import packages
import torch
from transformers import AutoProcessor, CLIPModel, AutoImageProcessor
from datasets import load_dataset
import faiss
import numpy as np
import time

#Define device
device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

#Load CLIP model and processor
processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)

#Load cifar10 dataset
dataset = load_dataset("cifar10")

#Display an image
display(dataset['train'][0]['img'])
#Add a vector to FAISS index
def add_vector_to_index(embedding, index):
    #convert embedding to numpy
    vector = embedding.detach().cpu().numpy()
    #Convert to float32 numpy
    vector = np.float32(vector)
    #Normalize vector
    faiss.normalize_L2(vector)
    #Add to index
    index.add(vector)

#Extract features of a given image
def extract_features_clip(image):
    with torch.no_grad():
        inputs = processor(images=image, return_tensors="pt").to(device)
        image_features = model.get_image_features(**inputs)
        return image_features

#Create FAISS index
index = faiss.IndexFlatL2(512)

#Process the dataset to extract all features and store in index
for image in dataset['test']:
    clip_features = extract_features_clip(image['img'])
    add_vector_to_index(clip_features,index)
    
#Write index locally. Not needed after but can be useful for future retrieval
faiss.write_index(index,"clip.index")

#Retrieve the vectors
vectors = index.reconstruct_n(0, 10000)

#Define clusters and parameters
x= vectors
ncentroids = 10
niter = 50
verbose = True
d = x.shape[1]

t0=time.time()
#Launch clustering
kmeans = faiss.Kmeans(d, ncentroids, niter=niter, verbose=verbose)
kmeans.train(x)
print('Clustering done in ', time.time()-t0)

#cluster assignments: [array of vectors, array of indices (cluster assigned to a given vector)]
cluster_assignments = kmeans.assign(vectors)
print(cluster_assignments)
#Print indices only
clusters_ind = cluster_assignments[1]
print(clusters_ind)

distribution = np.zeros((10, 10), dtype=int)
print(distribution)

for vector_index,cluster_nb in enumerate(clusters_ind):
    label = dataset['test'][vector_index]['label']
    distribution[cluster_nb][label]+=1
 
print(distribution)

row_sums = np.sum(distribution, axis=1)
print("Number of images per cluster:")
print(row_sums)

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

disp = ConfusionMatrixDisplay(confusion_matrix=distribution)
disp.plot()
plt.ylabel('Cluster ID')
plt.xlabel('Classes')
labels = dataset["train"].features["label"].names
x = range(len(labels))
ticks = labels
plt.xticks(x, ticks)
plt.show()