jdwebprogrammer

import json
from bs4 import BeautifulSoup

def html_to_json(html_file, output_file):
    with open(html_file, 'r') as f:
        soup = BeautifulSoup(f, 'lxml')
    
    data = {}
    for tag in soup.find_all():
        if not tag.name in data:

jdwebprogrammer

import tempfile
import gradio as gr
from neon_tts_plugin_coqui import CoquiTTS

LANGUAGES = list(CoquiTTS.langs.keys())
coquiTTS = CoquiTTS()

def tts(text: str, language: str):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        coquiTTS.get_tts(text, fp, speaker = {"language" : language})

jdwebprogrammer

import tempfile
from neon_tts_plugin_coqui import CoquiTTS

LANGUAGES = list(CoquiTTS.langs.keys())
coquiTTS = CoquiTTS()

def tts(text: str):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        coquiTTS.get_tts(text, fp, speaker = {"language" : "en"})
        return fp.name

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# Generate synthetic data for clustering
data, _ = make_blobs(n_samples=300, centers=4, random_state=42)

# Create a K-Means clustering model
kmeans = KMeans(n_clusters=4, random_state=0)

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

class KMeansClustering:
    def __init__(self, n_clusters=3, random_state=0):
        """
        Initialize the KMeansClustering instance.

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

class DimensionalityReductionPCA:
    def __init__(self, n_components=None):
        """
        Initialize the DimensionalityReductionPCA instance.

        Parameters:

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest

class AnomalyDetectionIsolationForest:
    def __init__(self, contamination=0.05, random_state=None):
        """
        Initialize the AnomalyDetectionIsolationForest instance.

        Parameters:

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential

class SimpleGAN:
    def __init__(self, input_dim, generator_output_dim, discriminator_output_dim):
        self.input_dim = input_dim
        self.generator_output_dim = generator_output_dim

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras.losses import mse
from tensorflow.keras import backend as K

class SimpleVAE:
    def __init__(self, input_dim, latent_dim):

jdwebprogrammer

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

class LDATopicModel:
    def __init__(self, n_topics=5, max_features=1000):
        self.n_topics = n_topics
        self.max_features = max_features
        self.vectorizer = CountVectorizer(max_features=self.max_features, stop_words='english')