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app.py

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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()

requirements.txt

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+scikit-learn==1.0.2