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Project2-ParagraphSimilaritcheck.py
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Project2-ParagraphSimilaritcheck.py
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from sklearn.feature_extraction.text import CountVectorizer
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer('distilbert-base-nli-mean-tokens')
stop_words = "english"
n_gram_range = (1, 1)
top_n = 5
def func(d1,d2):
count1 = CountVectorizer(ngram_range=n_gram_range, stop_words=stop_words).fit([d1])
count2 = CountVectorizer(ngram_range=n_gram_range, stop_words=stop_words).fit([d2])
candidates1 = count1.get_feature_names()
candidates2 = count2.get_feature_names()
doc_embedding1 = model.encode([d1])
doc_embedding2 = model.encode([d2])
candidate_embeddings1 = model.encode(candidates1)
candidate_embeddings2 = model.encode(candidates2)
distances1 = cosine_similarity(doc_embedding1, candidate_embeddings1)
distances2 = cosine_similarity(doc_embedding2, candidate_embeddings2)
keywords1 = [candidates1[index] for index in distances1.argsort()[0][-top_n:]]
keywords2 = [candidates2[index] for index in distances2.argsort()[0][-top_n:]]
sc=0
for i in keywords1:
for j in keywords2:
if i==j:
sc+=1
return(sc)
from flask import *
app = Flask(__name__)
@app.route('/',methods=['POST', 'GET'])
def index():
if(request.method=='GET'):
return render_template('upload.html')
elif(request.method=='POST'):
d1 = request.form['file1']
d2 = request.form['file2']
score=str(func(d1,d2))
return render_template("upload.html", name = score)
if __name__ == '__main__':
app.run(debug=True)