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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[15]: | |
| import csv | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.cluster import KMeans | |
| import imdb | |
| import random | |
| import gradio as gr | |
| # In[2]: | |
| file1=open("movies.csv",'r') | |
| file2=open("movies3.csv",'r') | |
| csvreader1 = csv.reader(file1) | |
| csvreader2 = csv.reader(file2) | |
| # In[3]: | |
| pd_dict={'movie':[],'time_minute':[],'imdb_rating':[],'Action':[],'Adventure':[],'Fantasy':[], | |
| 'Sci-Fi':[],'Animation':[],'Comedy':[],'Family':[],'Mystery':[],'Romance':[],'Drama':[], | |
| 'Crime':[],'Thriller':[],'War':[],'Musical':[],'Biography':[]} | |
| lst=['Action','Adventure','Fantasy','Sci-Fi','Animation','Comedy', | |
| 'Family','Mystery','Romance','Drama','Crime','Thriller','War','Musical','Biography'] | |
| # In[4]: | |
| j=0 | |
| for row in csvreader2: | |
| if j==0: | |
| j=1 | |
| continue | |
| for i in range(len(lst)): | |
| pd_dict[lst[i]].append(int(row[i+1])) | |
| j=0 | |
| for row in csvreader1: | |
| if j==0: | |
| j=1 | |
| continue | |
| if j==250: | |
| break | |
| pd_dict['movie'].append(row[1]) | |
| pd_dict['time_minute'].append(int(row[3].split()[0])) | |
| pd_dict['imdb_rating'].append(float(row[4])) | |
| j+=1 | |
| # In[5]: | |
| df=pd.DataFrame(pd_dict) | |
| X=np.array(df.drop(['movie'], axis = 1)) | |
| # In[6]: | |
| kmeans = KMeans(max_iter=300).fit(X) | |
| # In[9]: | |
| movie_name="Oppenheimer" | |
| ia=imdb.IMDb() | |
| items = ia.search_movie(movie_name) | |
| code = items[0].getID() | |
| print(code) | |
| series = ia.get_movie(code) | |
| genre = series.data['genres'] | |
| time_minute=series.data['runtimes'] | |
| rating=series.data['rating'] | |
| x=[int(time_minute[0]),float(rating)] | |
| for i in lst: | |
| if i in genre: | |
| x.append(1) | |
| else: | |
| x.append(0) | |
| print(x) | |
| # In[13]: | |
| i=random.randint(1,249) | |
| test_x=X[i].reshape(1,-1) | |
| x=np.array(x) | |
| x=x.reshape(1,-1) | |
| # print(kmeans.predict(test_x)) | |
| # print(kmeans.predict(x)) | |
| pred=kmeans.predict(x) | |
| pred_test=kmeans.predict(test_x) | |
| while pred!=pred_test: | |
| i=random.randint(1,249) | |
| test_x=X[i].reshape(1,-1) | |
| x=np.array(x) | |
| x=x.reshape(1,-1) | |
| pred_test=kmeans.predict(test_x) | |
| print('Another movie like',movie_name,'is:',df['movie'][i]) | |
| # In[16]: | |
| def movie(txt): | |
| ia=imdb.IMDb() | |
| items = ia.search_movie(movie_name) | |
| code = items[0].getID() | |
| series = ia.get_movie(code) | |
| genre = series.data['genres'] | |
| time_minute=series.data['runtimes'] | |
| rating=series.data['rating'] | |
| x=[int(time_minute[0]),float(rating)] | |
| for i in lst: | |
| if i in genre: | |
| x.append(1) | |
| else: | |
| x.append(0) | |
| i=random.randint(1,249) | |
| test_x=X[i].reshape(1,-1) | |
| x=np.array(x) | |
| x=x.reshape(1,-1) | |
| # print(kmeans.predict(test_x)) | |
| # print(kmeans.predict(x)) | |
| pred=kmeans.predict(x) | |
| pred_test=kmeans.predict(test_x) | |
| while pred!=pred_test: | |
| i=random.randint(1,249) | |
| test_x=X[i].reshape(1,-1) | |
| x=np.array(x) | |
| x=x.reshape(1,-1) | |
| pred_test=kmeans.predict(test_x) | |
| return df['movie'][i] | |
| iface = gr.Interface(fn=movie, inputs="text", outputs="text") | |
| iface.launch() | |
| # In[ ]: | |