vaibhavgehani

export class HomePage {
  MovieName = '';
  url = environment.modelUrl;
  public recommentList: any = [];
  constructor(private http: HttpClient) {}
  getRecommend() {
    if (this.MovieName !== '') {
      this.http.get(`${this.url}?name=${this.MovieName}`).subscribe((response) => {
        console.log(response);
        this.recommentList = response;

vaibhavgehani

<ion-header>
  <ion-toolbar>
    <ion-title>
      Recommend Movies
    </ion-title>
  </ion-toolbar>
</ion-header>

<ion-content style="background-color: gainsboro;" [fullscreen]="true">
  <ion-header collapse="condense">

vaibhavgehani

@app.route('/movie')
def main():
    name=request.args.get('name')
    print(name)
    if(name != None):
        recom_array=recommend_movie(name)
    print(recom_array)
    try:
        return recom_array.to_json(orient='records')
    except:

vaibhavgehani

def recommend_movie(movieName,cosine_sim=cosine_sim):
    try:
        indx=indices[movieName]
        score_tuple=list(enumerate(cosine_sim[indx]))
        sorted_tuple=sorted(score_tuple,key=lambda x: x[1],reverse=True)
        top_10_score=sorted_tuple[1:6]
        top_10_index=[i[0] for i in top_10_score]
        return movie_data[['title','spoken_languages','popularity','release_date','runtime','poster_path']].iloc[top_10_index]
    except(Exception):
        print('Erorr')

vaibhavgehani

movie_data = pd.read_csv('movies.csv')

#Vectorization of the Words
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(stop_words='english')
movie_data.overview=movie_data.overview.fillna('')
tfidf_matrix = tfidf.fit_transform(movie_data.overview)


#importing linear_kernel from sklearn to get the coorelation between each movie according the overview feature of dataset

vaibhavgehani

from flask import Flask,request,jsonify,Response
from flask_cors import CORS;
import pandas as pd
import json
app=Flask(__name__)
CORS(app)
#importing the dataset
movie_data = pd.read_csv('movies.csv')

#Vectorization of the Words

vaibhavgehani

goToSelectfile(event: any) {
    this.fileObj = event.target.files[0];
  }
  sendName() {
    const uploadData = new FormData();
    uploadData.append('file', this.fileObj);
    this.http.post('http://127.0.0.1:5000/getText', uploadData).subscribe((res: any) => {
      this.show = true;
      this.line = res.outputText;
      this.sentiment = res.predSentiment;

vaibhavgehani

try:
    from PIL import Image
except ImportError:
    import Image
import pytesseract

def ocr_extraction(filename):
    pytesseract.pytesseract.tesseract_cmd = r'C:/Users/intel/AppData/Local/Tesseract-OCR/tesseract.exe'
    text=pytesseract.image_to_string(Image.open(filename))
    return(text)

vaibhavgehani

positive_fileids = movie_reviews.fileids('pos')
negative_fileids = movie_reviews.fileids('neg')

vaibhavgehani

features_positive = [(extract_features(movie_reviews.words(fileids=[f])),'Positive') for f in positive_fileids]
features_negative = [(extract_features(movie_reviews.words(fileids=[f])),'Negative') for f in negative_fileids]