#%%

#Importing All necessary libraries used in this project

import pandas as pd

import numpy as np

import re

#visualization

import seaborn as sns

import matplotlib.pyplot as plt

#NLP

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize

from wordcloud import STOPWORDS , WordCloud

#model

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

#Tensorflow

import tensorflow as tf

from tensorflow.keras.preprocessing.text import Tokenizer

from tensorflow.keras.preprocessing.sequence import pad_sequences

from tensorflow.keras.preprocessing.text import one_hot

from tensorflow.keras.layers import Embedding

from tensorflow.keras.models import Sequential

# %%

df = pd.read_csv("spam.csv")

# %%

df.head(5)

# %%

df=df.drop(["Unnamed: 2", "Unnamed: 3", "Unnamed: 4"], axis=1)

# %%

df.head()

# %%

df=df.rename(columns={"v1":"label" , "v2":"message"})

# %%

df.head()

# %%

df.describe()

# %%

df.groupby('label').describe().T

# %%

# Get all the ham and spam emails

ham_msg = df[df.label =='ham']

spam_msg = df[df.label=='spam']

# %%

ham_msg_text = " ".join(ham_msg.message.to_numpy().tolist())

spam_msg_text = " ".join(spam_msg.message.to_numpy().tolist())

# %%

#WordCloud For the Ham Messages

ham_msg_cloud = WordCloud(width =520, height =260, stopwords=STOPWORDS,max_font_size=50, background_color ="black", colormap='Blues').generate(ham_msg_text)

plt.figure(figsize=(16,10))

plt.imshow(ham_msg_cloud)

plt.show()

# %%

#Worcloud For The Spam Messages

spam_msg_cloud = WordCloud(width =520, height =260, stopwords=STOPWORDS,max_font_size=50, background_color ="black", colormap='Blues').generate(spam_msg_text)

plt.figure(figsize=(16,10))

plt.imshow(spam_msg_cloud)

plt.show()

# %%

plt.figure(figsize=(8,6))

sns.countplot(df.label)

# %%

#percentage of the spam messages

(len(spam_msg)/len(ham_msg))*100

#%%

stopwords = stopwords.words('english')

#%%

df['message']=df['message'].apply(lambda x:re.sub('[.,@#$%*?_]','',x))

#%%

df['message']=df['message'].apply(lambda x:word_tokenize(x))

#%%

df['message']=df['message'].apply(lambda x:[word for word in x if word not in stopwords])

df.head()

# %%

X = df['message']

y = df['label']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

X_train

# %%

X_train = X_train.apply(lambda x :' '.join(x))

# %%

X_train

# %%

t = Tokenizer()

t.fit_on_texts(X_train)

# %%

encoded_train = t.texts_to_sequences(X_train)

encoded_test = t.texts_to_sequences(X_test)

# %%

padded_train = pad_sequences(encoded_train, maxlen=8, padding='post')

padded_test = pad_sequences(encoded_test, maxlen=8, padding='post')

print(padded_test)

#%%

#RandomForest model fitting

model=RandomForestClassifier(n_estimators=100)

model.fit(padded_train,y_train)

#%%

y_pred=model.predict(padded_test)

acc=accuracy_score(y_pred,y_test)

print(acc*100,"%")

# %%

#%%

vocab_size = 50_000

one_hots = [one_hot(word,vocab_size)for word in X_train]

print(one_hots)

# %%

padded = pad_sequences(one_hots, padding ='post' , maxlen=5)

print(padded)

# %%

model = Sequential()

model.add(Embedding(vocab_size,50))

model.compile("adam" , "mse")

# %%

predict = model.predict(padded)

# %%

predict.shape

# %%

# %%