Delete app.py

app.py

@@ -1,105 +0,0 @@
-# -*- coding: utf-8 -*-
-"""movie recommendation system.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1iy9ZFUvOu-sz4vP4--9-dr-T-8LMZNOP
-"""
-
-import numpy as np
-import pandas as pd
-import difflib
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import  cosine_similarity
-
-movies_df = pd.read_csv('/content/movies.csv')
-
-movies_df.head()
-
-movies_df.shape
-
-selected_features = ['genres', 'keywords', 'tagline', 'cast','director']
-
-print(selected_features)
-
-for feature in selected_features:
-  movies_df[feature]=movies_df[feature].fillna('')
-
-combined_features = movies_df['genres']+' '+movies_df['keywords']+' '+movies_df['tagline']+' '+movies_df['cast']+' '+movies_df['director']
-
-print(combined_features)
-
-vectorizer = TfidfVectorizer()
-
-feature_vectors = vectorizer.fit_transform(combined_features)
-
-print(feature_vectors)
-
-similarity = cosine_similarity(feature_vectors)
-
-print(similarity)
-
-print(similarity.shape)
-
-movie_name = input(' Enter name of the movie: ')
-
-list_of_all_titles = movies_df['title'].tolist()
-new_var = print(list_of_all_titles)
-
-find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles)
-print(find_close_match)
-
-close_match = find_close_match[0]
-print(close_match)
-
-index_of_the_movie = movies_df[movies_df.title == close_match]['index'].values[0]
-print(index_of_the_movie)
-
-similarity_score = list(enumerate(similarity[index_of_the_movie]))
-print(similarity_score)
-
-len(similarity_score)
-
-sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True)
-print(sorted_similar_movies)
-
-print('suggesting movies for you : \n')
-
-i = 1
-
-for movie in sorted_similar_movies:
-  index = movie[0]
-  title_from_index = movies_df[movies_df.index==index]['title'].values[0]
-  if (i<30):
-    print(i, '.',title_from_index)
-    i+=1
-
-"""movie recommendation system
-
-"""
-
-movie_name = input(' which is your favourite movie : ')
-
-list_of_all_titles = movies_df['title'].tolist()
-
-find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles)
-
-close_match = find_close_match[0]
-
-index_of_the_movie = movies_df[movies_df.title == close_match]['index'].values[0]
-
-similarity_score = list(enumerate(similarity[index_of_the_movie]))
-
-sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True)
-
-print('suggesting movies for you : \n')
-
-i = 1
-
-for movie in sorted_similar_movies:
-  index = movie[0]
-  title_from_index = movies_df[movies_df.index==index]['title'].values[0]
-  if (i<30):
-    print(i, '.',title_from_index)
-    i+=1