Answer to Question #293767 in C++ for dreigns

Question

how to make plagiarism detection software in c++.(i want code)

Answer 1

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#include<iostream>
#include <fstream>
using namespace std;
 
int main() {
   string str_inp1("Python");
   string str_inp2("Python");
  /* this commented code can be used when the data is read from a file
  *******************************************************************************
  
   fstream my_file;
	my_file.open("my_file", ios::out);
	if (!my_file) {
		cout << "File not created!";
	}
	else {
		cout << "File created successfully!";
		my_file.close(); 
	}
	return 0;
}
 */   
cout<<"String 1:"<<str_inp1<<endl;
cout<<"String 2:"<<str_inp2<<endl;
int res = str_inp1.compare(str_inp2);
 
    if (res == 0)
        cout << "\nBoth the input strings are equal." << endl;
    else if(res < 0)
        cout << "String 1 is smaller as compared to String 2\n.";
        else
        cout<<"String 1 is greater as compared to String 2\n.";
/*****************************************************************************************************
Developing a Plagiarism detector software would need intergration of more than one language
There are some Libraries which are supposed to be utilized which are not present in C++
I'd reccomend you study the software below don in python, css, JS assets and a HTML file front end
******************************************************************************************************
        
import load_dotenv
from flask import Flask
from flask import render_template
from flask import request
from flask import url_for
import json
import os
import pandas as pd
import pinecone
import re
import requests
from sentence_transformers import SentenceTransformer
from statistics import mean
import swifter


app = Flask(__name__)


PINECONE_INDEX_NAME = "plagiarism-checker"
DATA_FILE = "articles.csv"
NROWS = 20000


def initialize_pinecone():
    load_dotenv()
    PINECONE_API_KEY = os.environ["PINECONE_API_KEY"]
    pinecone.init(api_key=PINECONE_API_KEY)


def delete_existing_pinecone_index():
    if PINECONE_INDEX_NAME in pinecone.list_indexes():
        pinecone.delete_index(PINECONE_INDEX_NAME)


def create_pinecone_index():
    pinecone.create_index(name=PINECONE_INDEX_NAME, metric="cosine", shards=1)
    pinecone_index = pinecone.Index(name=PINECONE_INDEX_NAME)


    return pinecone_index


def create_model():
    model = SentenceTransformer('average_word_embeddings_komninos')


    return model


def prepare_data(data):
    # rename id column and remove unnecessary columns
    data.rename(columns={"Unnamed: 0": "article_id"}, inplace = True)
    data.drop(columns=['date'], inplace = True)


    # combine the article title and content into a single field
    data['content'] = data['content'].fillna('')
    data['content'] = data.content.swifter.apply(lambda x: ' '.join(re.split(r'(?<=[.:;])\s', x)))
    data['title_and_content'] = data['title'] + ' ' + data['content']


    # create a vector embedding based on title and article content
    encoded_articles = model.encode(data['title_and_content'], show_progress_bar=True)
    data['article_vector'] = pd.Series(encoded_articles.tolist())


    return data


def upload_items(data):
    items_to_upload = [(row.id, row.article_vector) for i, row in data.iterrows()]
    pinecone_index.upsert(items=items_to_upload)


def process_file(filename):
    data = pd.read_csv(filename, nrows=NROWS)
    data = prepare_data(data)
    upload_items(data)
    pinecone_index.info()


    return data


def map_titles(data):
    return dict(zip(uploaded_data.id, uploaded_data.title))


def map_publications(data):
    return dict(zip(uploaded_data.id, uploaded_data.publication))


def query_pinecone(originalContent):
    query_content = str(originalContent)
    query_vectors = [model.encode(query_content)]


    query_results = pinecone_index.query(queries=query_vectors, top_k=10)
    res = query_results[0]


    results_list = []


    for idx, _id in enumerate(res.ids):
        results_list.append({
            "id": _id,
            "title": titles_mapped[int(_id)],
            "publication": publications_mapped[int(_id)],
            "score": res.scores[idx],
        })


    return json.dumps(results_list)


initialize_pinecone()
delete_existing_pinecone_index()
pinecone_index = create_pinecone_index()
model = create_model()-
uploaded_data = process_file(filename=DATA_FILE)
titles_mapped = map_titles(uploaded_data)
publications_mapped = map_publications(uploaded_data)


@app.route("/")
def index():
    return render_template("index.html")


@app.route("/api/search", methods=["POST", "GET"])
def search():
    if request.method == "POST":
        return query_pinecone(request.form.get("originalContent", ""))
    if request.method == "GET":
        return query_pinecone(request.args.get("originalContent", ""))
    return "Only GET and POST methods are allowed for this endpoint"
    return 0;
}