AmolMavuduru

def train_model(X_train, y_train, X_test, y_test):

    model = Sequential()
    model.add(Conv2D(kernel_size=(3, 3), filters=32, 
                     input_shape=input_shape))
    model.add(Activation('relu'))

    model.add(Conv2D(kernel_size=(3, 3), filters=32))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(2, 2))

AmolMavuduru

import joblib
import re
from sklearn.neural_network import MLPClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from fastapi import FastAPI

app = FastAPI()

model = joblib.load('spam_classifier.joblib')

AmolMavuduru

import numpy as np
import pandas as pd

# Read the Data

data = pd.read_csv('./data/spam_data.csv')

# Text Preprocessing

import re # regex library

AmolMavuduru

import joblib
import re
from sklearn.neural_network import MLPClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
import streamlit as st
from lime.lime_text import LimeTextExplainer
import streamlit.components.v1 as components

st.write("# Spam Detection Engine")

AmolMavuduru

import numpy as np
import pandas as pd

# Read the Data

data = pd.read_csv('./data/spam_data.csv')

# Text Preprocessing

import re # regex library

AmolMavuduru

from collections import defaultdict
from scipy.spatial.distance import cdist
import difflib

number_cols = ['valence', 'year', 'acousticness', 'danceability', 'duration_ms', 'energy', 'explicit',
 'instrumentalness', 'key', 'liveness', 'loudness', 'mode', 'popularity', 'speechiness', 'tempo']

def get_song_data(song, spotify_data):
    
    """

AmolMavuduru

from spotipy.oauth2 import SpotifyClientCredentials
from collections import defaultdict

sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(client_id=os.environ["SPOTIFY_CLIENT_ID"],
                                                           client_secret=os.environ["SPOTIFY_CLIENT_SECRET"]))


def find_song(name, year):
  
    """

AmolMavuduru

"""
Utility functions for generating movie recommendations using sequence models.
"""
import difflib

def get_movie_id(movie_title, metadata):
    """
    Gets the movie id for a movie title
    """
    

AmolMavuduru

"""
Utility functions for generating movie recommendations using matrix factorization models
"""

def get_metadata(movie_id, metadata):
    """
    Retrieves the metadata for a movie given the movie ID
    """
    movie_data = metadata[metadata['movieId'] == movie_id]
    return movie_data[['original_title', 'release_date', 'genres']].to_dict(orient='records')

AmolMavuduru

import difflib
import random

def get_book_id(book_title, metadata):
    
    """
    Gets the book ID for a book title based on the closest match in the metadata dataframe.
    """
    
    existing_titles = list(metadata['title'].values)