def getMovieDetailsFromOMDB(self, movieName):
## API key
API_key = "bf1418d4"
movie = GetMovie(title= movieName, api_key=API_key)
return movie.get_data('Title','Poster', 'Year', 'Ratings', 'Director', 'Genre')
def get_recommendations(title):
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[1:11]
movie_indices = [i[0] for i in sim_scores]
title = df2['title'].iloc[movie_indices]
date = df2['release_date'].iloc[movie_indices]
return_df = pd.DataFrame(
columns=['Title', 'Year', 'Plot', 'Ratings', 'Genre'])
return_df['Title'] = title
return_df['Year'] = date
plot = []
genre = []
ratings = []
for i in title:
movie = GetMovie(title=i, api_key='65b3ddf')
mov = movie.get_data('Genre', 'Ratings', 'Plot')
plot.append(mov['Plot'])
ratings.append(mov['Ratings'][0]['Value'])
genre.append(mov['Genre'])
return_df['Plot'] = plot
return_df['Ratings'] = ratings
return_df['Genre'] = genre
return_df.sort_values(by=['Ratings'], inplace=True, ascending=False)
return return_df
def is_movie(file):
"""
:type file: Path
"""
title, year = get_movie_title_year(file)
if not (title and year):
print(' x Not a movie: missing title and/or year')
return False
movie = GetMovie(title=title, api_key=OMDBAPI_KEY)
try:
movie_year = int(movie.get_data('Year')['Year'])
except ValueError:
print(f' x Not a movie; not found on OMDB: {title}')
return False
if not str(year) == str(movie_year):
print(
f' x Not a movie: year on filename {year} does not match OMDB {movie_year}'
)
return False
return True
# %%
from omdbapi.movie_search import GetMovie
from keys import OMDB_API_KEY
movie = GetMovie(title="Interstellar", api_key=OMDB_API_KEY)
# movie.get_all_data().keys()
# movie.get_data('Title', 'Year', 'Rated', 'Released', 'Runtime', 'Genre', 'Director', 'Writer', 'Actors', 'Plot', 'Poster', 'Ratings', 'Metascore', 'imdbRating', 'imdbVotes', 'imdbID', 'Type')
title, year, rated = movie.get_data('Title', 'Year', 'Rated').values()
print(title, year, rated)
def get_omdb_movie_dataset():
##create new dataframe with following features
features = [
'Ranking among 250', 'Title', 'Metascore', 'Rotten Tomatoes Score',
'Year', 'Country', 'Runtime(min)', 'Language', 'Director', 'Writer',
'Production Company', 'Type', 'Plot'
]
df = pd.DataFrame(columns=features)
title_list = []
country_list = []
release_year_list = []
language_list = []
metascore_rating_list = []
rating_list = []
director_list = []
writer_list = []
plot_list = []
type_list = []
runtime_list = []
production_company_list = []
words = [
'Title', 'Metascore', 'Year', 'Country', 'Runtime', 'Language',
'Director', 'Writer', 'Production', 'Type', 'Plot'
]
##for each movie, extract corresponding 'Title','Metascore','Year',
##'Country','Runtime','Language','Production','Type','Plot' by using getMovie package from omdb library wrapper
for i in range(len(movie_list)):
movie = GetMovie(title=movie_list[i], api_key='6de2ae04', plot='full')
title = movie.get_data('Title')
title_list.append(title)
metascore_rating = movie.get_data('Metascore')
metascore_rating_list.append(metascore_rating)
release_year = movie.get_data('Year')
release_year_list.append(release_year)
country = movie.get_data('Country')
country_list.append(country)
runtime = movie.get_data('Runtime')
runtime_list.append(runtime)
language = movie.get_data('Language')
language_list.append(language)
rating = movie.get_data('Ratings')
rating_list.append(rating)
director = movie.get_data('Director')
director_list.append(director)
writer = movie.get_data('Writer')
writer_list.append(writer)
production_company = movie.get_data('Production')
production_company_list.append(production_company)
Type = movie.get_data('Type')
type_list.append(Type)
plot = movie.get_data('Plot')
plot_list.append(plot)
## each list constituted by dict which contain features name and corresponding feature value,
## below function only extract feature values
def transform(my_list, word):
new_value = []
for x in range(0, len(my_list)):
Str = my_list[x][word]
new_value.append(Str)
return new_value[i]
##rating list contains dict which included imdb rating score, rotten tomatoes score and metascore, we only
##need rotten tomato score from rating list by using below function
def transform_rotten_tomatoes_rating(My_list):
output_list = []
for rating in My_list:
rotten_tomato_value = rating['Ratings'][1]['Value'].replace(
'%', '')[:2]
output_list.append(rotten_tomato_value)
return output_list[i]
##apply above two functions to every list
title_ = transform(title_list, words[0])
metascore_ = transform(metascore_rating_list, words[1])
release_year_ = transform(release_year_list, words[2])
country_ = transform(country_list, words[3])
runtime_ = transform(runtime_list, words[4])[:3]
language_ = transform(language_list, words[5])
director_ = transform(director_list, words[6])
writer_ = transform(writer_list, words[7])
production_company_ = transform(production_company_list, words[8])
type_ = transform(type_list, words[9])
plot_ = transform(plot_list, words[10])
## if indexerror happens, just ignore it and append none to rotten_tomatoes_score
try:
rotten_tomatoes_score = transform_rotten_tomatoes_rating(
rating_list)
except IndexError:
rotten_tomatoes_score = ''
##append all above movies' feature to dataframe.
feature_dicts = {
'Ranking among 250': i + 1,
'Title': title_,
'Metascore': metascore_,
'Rotten Tomatoes Score': rotten_tomatoes_score,
'Year': release_year_,
'Country': country_,
'Runtime(min)': runtime_,
'Language': language_,
'Director': director_,
'Writer': writer_,
'Production Company': production_company_,
'Type': type_,
'Plot': plot_
}
df = df.append(
pd.DataFrame.from_records([feature_dicts],
columns=feature_dicts.keys()))
df = df[features]
##using movies ranking as index in dataframe
df['Metascore'] = df['Metascore'].str.replace(
'N/A', '') # removing comma from column data
df['Metascore'] = pd.to_numeric(df['Metascore'])
df['Metascore'].fillna(df['Metascore'].median(), inplace=True)
df['Rotten Tomatoes Score'] = pd.to_numeric(df['Rotten Tomatoes Score'])
df['Rotten Tomatoes Score'].fillna(df['Rotten Tomatoes Score'].median(),
inplace=True)
#df=df.set_index(['Ranking among 250'], drop=True)
return df
def omdb():
speak('enter name of movie:')
name = s_input()
movie = GetMovie(title=name,
api_key='Enter API key ') #get api key from imdb
print(movie.get_data('Title'))
print(movie.get_data('Yaer'))
print(movie.get_data('Rated'))
print(movie.get_data('Language'))
print(movie.get_data('Director'))
print(movie.get_data('Actors'))
print(movie.get_data('writer'))
print(movie.get_data('Ratings'))
print(movie.get_data('Runtime'))
print(movie.get_data('Boxoffice'))
print(movie.get_data('Genre'))
print(movie.get_data('imdbRating'))
speak(movie.get_data('Title'))
speak(movie.get_data('Yaer'))
speak(movie.get_data('Language'))
speak(movie.get_data('Director'))
speak(movie.get_data('Actors'))
speak(movie.get_data('writer'))
speak(movie.get_data('Runtime'))
speak(movie.get_data('Genre'))
speak(movie.get_data('imdbRating'))
from omdbapi.movie_search import GetMovie
import pandas as pd
from tabulate import tabulate as td
"Library Imported"
# In[ ]:
search = input("Enter the movie you want to search for :")
# In[ ]:
movie = GetMovie(title=search, api_key='723686e0', plot='full')
# In[ ]:
result = movie.get_data('Title', 'Year', 'Genre', 'Director', 'Actors',
'Rated', 'imdbRating', 'imdbVotes')
result_data = pd.DataFrame.from_dict(result, orient='index')
"Your Returned Result : "
# In[ ]:
print(td(result_data, tablefmt="rst"))
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
for heading in division:
movie_name = heading.find('h2')
movie_year = heading.find("span", {"class": "css-0 e1n7piqx10"})
movie_title.append(movie_name.text.title())
#movie_period.append(movie_year.tex[-4:])
division2 = soup.findAll('div', {'class': 'css-14yfd4f etvj1lh3'})
for heading2 in division2:
movie_name2 = heading2.find('h2')
movie_year2 = heading2.find("span", {"class": "css-0 etvj1lh8"})
movie_title.append(movie_name2.text.title())
#movie_period.append(movie_year2.text[-4:])
#querying omdb
for i in tqdm(movie_title):
movie_search = GetMovie(title=i, api_key=omdb_api_key)
results = movie_search.get_data('Title', 'Released', 'imdbID')
results['imdb_id'] = results.pop('imdbID')
results['title'] = results.pop('Title')
results['year'] = results.pop('Released')
movies.append(results)
with open('listfile', 'w') as f:
f.write('\u005B')
for items in movies:
f.write('%s' % items + '\u002C' + '\n')
f.close()
with open('listfile', 'a') as file:
file.write('\u005D')
file.close()