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flaskblog.py
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from flask import Flask, render_template, request, url_for,flash, redirect
from forms import RegistrationForm, LoginForm
from flask_sqlalchemy import SQLAlchemy
from flask_bcrypt import Bcrypt
from datetime import datetime
from flask_login import LoginManager, UserMixin, login_user
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.options.display.max_columns = None
from scipy import stats
from ast import literal_eval
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import linear_kernel, cosine_similarity
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet
import urllib.request
import json
app = Flask(__name__)
app.config['SECRET_KEY']='a8ca03f6bb27fb5d2e9543b0a5c0ded3'
app.config['SQLALCHEMY_DATABASE_URI']='sqlite:///site.db'
db=SQLAlchemy(app)
bcrypt=Bcrypt(app)
login_manager=LoginManager(app)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class User(db.Model, UserMixin):
id=db.Column(db.Integer,primary_key=True)
username=db.Column(db.String(20),unique=True,nullable=False)
email=db.Column(db.String(120),unique=True,nullable=False)
password=db.Column(db.String(60),nullable=False)
posts=db.relationship('Post',backref='author',lazy=True)
def __repr__(self):
return f"User('{self.username}','{self.email}')"
class Post(db.Model):
id=db.Column(db.Integer,primary_key=True)
title=db.Column(db.String(100),nullable=False)
date_posted=db.Column(db.DateTime,nullable=False,default=datetime.utcnow)
content=db.Column(db.Text,nullable=False)
user_id=db.Column(db.Integer,db.ForeignKey('user.id'),nullable=False)
def __repr__(self):
return f"Post('{self.title}','{self.date_posted}')"
@app.route("/login",methods=['GET','POST'])
def login():
form=LoginForm()
if form.validate_on_submit():
user=User.query.filter_by(email=form.email.data).first()
if user and bcrypt.check_password_hash(user.password,form.password.data):
login_user(user,remember=form.remember.data)
return render_template('home.html')
else:
flash('Login Unsucessful')
return render_template('login.html',title='Login',form=form)
@app.route("/register",methods=['GET','POST'])
def register():
form=RegistrationForm()
if form.validate_on_submit():
hashed_password=bcrypt.generate_password_hash(form.password.data).decode('utf-8')
user=User(username=form.username.data, email=form.email.data, password=hashed_password)
db.session.add(user)
db.session.commit()
flash('Account has been created for {form.username.data}!','success')
return redirect(url_for('login'))
return render_template('register.html',title='Register',form=form)
@app.route("/home",methods=['GET','POST'])
def home():
if request.method=='POST':
mk=request.form['mk']
url='http://www.omdbapi.com/?apikey=34035a0a&type=movie&s='
url=url+str(mk)
json_obj=urllib.request.urlopen(url)
data=json.load(json_obj)
data=data['Search']
return render_template('about.html',data=data)
return render_template('home.html')
@app.route("/about",methods=['GET','POST'])
def about():
if request.method=='POST':
imdb_id=request.form['imdbid']
url='http://www.omdbapi.com/?apikey=34035a0a&i='+str(imdb_id)
json_obj=urllib.request.urlopen(url)
data=json.load(json_obj)
return render_template('info.html',data=data)
@app.route("/review",methods=['GET','POST'])
def review():
class recommend_movies(object):
def __init__(self):
self.file = pd. read_csv('data/movies_metadata.csv')
self.m = 0
self.C = 0
def preprocessing(self):
file = self.file.copy()
file['genres'] = self.file['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x]\
if isinstance(x, list) else [])
vote_counts = file[file['vote_count'].notnull()]['vote_count'].astype('int')
vote_averages = file[file['vote_average'].notnull()]['vote_average'].astype('int')
self.C = vote_averages.mean()
self.m = vote_counts.quantile(0.95)
file['year'] = pd.to_datetime(file['release_date'], errors='coerce').apply(lambda x: str(x).split('-')[0] \
if x != np.nan else np.nan)
return file
def weighted_rating(self,x):
v = x['vote_count']
R = x['vote_average']
return (v/(v+self.m) * R) + (self.m/(self.m+v) * self.C)
def get_top_movies(self):
df = self.preprocessing()
qualified = df[(df['vote_count'] >= self.m) & (df['vote_count'].notnull()) & (df['vote_average'].notnull())]\
[['title', 'year', 'vote_count', 'vote_average', 'popularity', 'genres']]
qualified['vote_count'] = qualified['vote_count'].astype('int')
qualified['vote_average'] = qualified['vote_average'].astype('int')
qualified['wr'] = qualified.apply(self.weighted_rating, axis=1)
qualified = qualified.sort_values('wr', ascending=False).head(250)
qualified = qualified.sort_values('wr', ascending=False).head(250)
return qualified
def build_chart(self,genre, percentile=0.85):
df = self.preprocessing()
s = df.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)
s.name = 'genre'
gen_md = df.drop('genres', axis=1).join(s)
df1 = gen_md[gen_md['genre'] == genre]
vote_counts = df[df['vote_count'].notnull()]['vote_count'].astype('int')
vote_averages = df[df['vote_average'].notnull()]['vote_average'].astype('int')
C = vote_averages.mean()
m = vote_counts.quantile(percentile)
qualified = df1[(df1['vote_count'] >= m) & (df1['vote_count'].notnull()) & (df1['vote_average'].notnull())]\
[['title', 'year', 'vote_count', 'vote_average', 'popularity']]
qualified['vote_count'] = qualified['vote_count'].astype('int')
qualified['vote_average'] = qualified['vote_average'].astype('int')
qualified['wr'] = qualified.apply(lambda x: (x['vote_count']/(x['vote_count']+self.m) * x['vote_average'])\
+ (self.m/(self.m+x['vote_count']) * self.C), axis=1)
return qualified
def get_reommended_movies(self,title):
df = self.preprocessing()
links_small = pd.read_csv('data/links_small.csv')
links_small = links_small[links_small['tmdbId'].notnull()]['tmdbId'].astype('int')
df = df.drop([19730, 29503, 35587])
df['id'] = df['id'].astype('int')
smd = df.loc[df['id'].isin(links_small)]
smd['tagline'] = smd['tagline'].fillna('')
smd['description'] = smd['overview'] + smd['tagline']
smd['description'] = smd['description'].fillna('')
tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')
tfidf_matrix = tf.fit_transform(smd['description'])
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
smd = smd.reset_index()
titles = smd['title'].str.lower()
indices = pd.Series(smd.index, index=smd['title'].str.lower())
print(indices.index)
idx = indices[title]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:31]
movie_indices = [i[0] for i in sim_scores]
return titles.iloc[movie_indices]
cls = recommend_movies()
prediction = list(cls.get_reommended_movies('rustom').head(10))
details=[]
url='http://www.omdbapi.com/?apikey=34035a0a&type=movie&t='
for i in prediction:
i=i.replace(' ','%20')
json_obj=urllib.request.urlopen(url+i)
data=json.load(json_obj)
details.append(data)
return render_template('review.html',details=details)
@app.route("/hello",methods=['GET','POST'])
def hello():
if request.method=='GET':
render_template('hello.html')
if __name__=='__main__':
app.run(debug=True)