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# -*- coding: utf-8 -*-
"""First_Text_Classification.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1sdLss09e3OxYVoeK3oBA6qrUSj_iOxp-
<h3 align = "center">Importing Libraries</h3>
"""
import numpy as np
import pandas as pd
"""<h3 align = "center">Importing Dataset</h3>"""
data = pd.read_csv("spam.csv", encoding = "ISO-8859-1")
"""<h3 align = "center">Preliminary Data Checks</h3>"""
data.head()
data.isnull().sum()
data.shape
data['v1'].value_counts()
data.info()
"""<h3 align = "center">Putting the Length of Characters of each row in a column.</h3>"""
data["Unnamed: 2"] = data["v2"].str.len()
"""<h3 align = "center">Visualising Length of Characters for each category!</h3>"""
"""<h5>It is evident from the above plot that spam texts are usually longer in length!</h5>
<h3 align = "center">Defining Variables</h3>
"""
X = data["v2"]
y = data["v1"]
"""<h3 align = "center">Train Test Split</h3>"""
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
"""<h3 align = "center">Vecrorizing Words into Matrix</h3>"""
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X_train)
X_train_counts
X_train.shape
X_train_counts.shape
from sklearn.feature_extraction.text import TfidfTransformer
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
X_train_tfidf.shape
"""<h3 align = "center">Using TDIF Vectorizer for optimum vectorization!</h3>"""
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X_train_tfidf = vectorizer.fit_transform(X_train)
X_train_tfidf.shape
"""<h3 align = "center">Creating Model</h3>"""
from sklearn.svm import LinearSVC
clf = LinearSVC()
clf.fit(X_train_tfidf,y_train)
"""<h3 align = "center">Creating Pipeline</h3>"""
from sklearn.pipeline import Pipeline
text_clf = Pipeline([("tfidf",TfidfVectorizer()),("clf",LinearSVC())])
text_clf.fit(X_train,y_train)
predictions = text_clf.predict(X_test)
X_test
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
print(confusion_matrix(y_test,predictions))
print(classification_report(y_test,predictions))
"""<h3 align = "center">Accuracy Score</h3>"""
print(accuracy_score(y_test,predictions))
"""<h3 align = "center">Predictions </h3>"""
text_clf.predict(["Hi how are you doing today?"])
text_clf.predict(["Congratulations! You are selected for a free vouchar worth $500"])
"""<h3 align = "center">Creating User Interface!</h3>"""
import gradio as gr
def first_nlp_spam_detector(text):
list = []
list.append(text)
arr = text_clf.predict(list)
if arr[0] == 'ham':
return "Your Text is a Legitimate One!"
else:
return "Beware of such text messages, It\'s a Spam! "
interface = gr.Interface(first_nlp_spam_detector,inputs = gr.Textbox(lines=2, placeholder="Enter your Text Here.....!", show_label = False),
outputs = gr.Label(value = "Predicting the Text Classification..!"),description = "Predicting Text Legitimacy!")
first_nlp_spam_detector("Congratulations! You are selected for a free vouchar worth $500")
interface.launch() |