Stackoverflow tag predictor

Author: Pushpendra9350

.DS_Store

@@ -0,0 +1,111 @@
+import pandas as pd
+import numpy as np
+import re
+from tqdm import tqdm
+import warnings
+import nltk
+nltk.download('punkt')
+warnings.filterwarnings("ignore")
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem.snowball import SnowballStemmer
+from sklearn.model_selection import train_test_split
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.linear_model import SGDClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn import metrics
+import joblib
+from joblib import load
+from flask_ngrok import run_with_ngrok
+from flask import Flask, request, render_template
+app = Flask(__name__)
+
+# Stopwords list 
+stop_words = {'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"}
+
+# This will work as a text spliter 
+def text_splitter(text):
+    return text.split()
+
+# To load model, Vectorizer and tag list 
+model = joblib.load("stackoverflow_model_100k.pkl")
+vectorizer = joblib.load("stackoverflow_tfidf_vectorizer_100k.pkl")
+tags = pd.read_csv("tag_list.csv")
+tag_list = tags["Tags"].to_list()
+
+# Data Preprocessing for new question
+def text_preprocessing(title,question):
+    # Stemmer is converting like: eating --> eat or running --> run
+    stemmer = SnowballStemmer("english")
+
+    # To replace <code>...</code> with space " "
+    question=re.sub('<code>(.*?)</code>', '', question, flags=re.MULTILINE|re.DOTALL)
+
+    # To remove all HTML tags from questions
+    html_cleaner = re.compile('<.*?>')
+    question = re.sub(html_cleaner, ' ', str(question.encode("utf-8")))
+    
+    title=title.encode('utf-8')
+        
+    # Concatinate title and question
+    question=str(title)+" "+str(question)   
+
+    # To remove all characters instead of alphabets
+    question=re.sub(r'[^A-Za-z]+',' ',question)
+
+    # First, we convert all text to lowercase and Word tokenize will convert 
+    # "This is data preprocessing" --> ["This","is","data","preprocessing"]
+    words=word_tokenize(str(question.lower()))
+
+    # Removing all single letter and stopwords from question except letter 'c' because c is a prog. language
+    # After removing all single words join then with space
+    question= ' '.join(str(stemmer.stem(j)) for j in words if j not in stop_words and (len(j)!=1 or j=='c'))
+    return question
+
+# To predict results 
+def predict1(title,question):
+
+    # Step 1: Text preprocessing
+    fine_text = text_preprocessing(title,question)
+
+    # Step 2: Make transform of your text to features 
+    vector = vectorizer.transform([fine_text])
+
+    # Step 3: Predict outputs 
+    prediction = model.predict(vector)
+
+    # Get the array of the prediction
+    prediction_results = prediction.toarray()[0]
+    final_tags = []
+
+    # Find relevent tags according to the results 
+    for i in range(5500):
+        if prediction_results[i]==1:
+            final_tags.append(tag_list[i])
+    return final_tags
+
+# Home page 
+@app.route('/')
+def homepage():
+    return render_template('index.html')
+
+# To get input from frontend and send the results to frontend 
+@app.route('/', methods= ["POST"])
+def background_process():
+    if request.method == 'POST':
+        title = request.form.get('title')
+        question = request.form.get('question')
+        result = predict1(title,question)
+        if len(result) == 0:
+            result = ["Not Find Any Results"]
+        if title:
+            result1 = tuple(result)
+            return render_template('index.html',result1 = result)
+        else:
+            return render_template('index.html',error='Please provide atleast title to get results')
+
+# To start the application
+if __name__ == "__main__":
+    app.run(debug=True,host="0.0.0.0",port="5000")