Upload app.py

app.py

@@ -1,74 +1,33 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
+_C='rating'
+_B='userId'
+_A='movieId'
+import gradio as gr,numpy as np,pandas as pd
 from scipy.sparse import csr_matrix
 from sklearn.neighbors import NearestNeighbors
-
-
-def create_matrix(df):
-    N = len(df['userId'].unique())
-    M = len(df['movieId'].unique())
-    # Map Ids to indices
-    user_mapper = dict(zip(np.unique(df["userId"]), list(range(N))))
-    movie_mapper = dict(zip(np.unique(df["movieId"]), list(range(M))))
-    # Map indices to IDs
-    user_inv_mapper = dict(zip(list(range(N)), np.unique(df["userId"])))
-    movie_inv_mapper = dict(zip(list(range(M)), np.unique(df["movieId"])))
-    user_index = [user_mapper[i] for i in df['userId']]
-    movie_index = [movie_mapper[i] for i in df['movieId']]
-    X = csr_matrix((df["rating"], (movie_index, user_index)), shape=(M, N))
-    return X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper
-
-
-def find_similar_movies(movie_id, X, k, metric='cosine', show_distance=False):
-    neighbour_ids = []
-    movie_ind = movie_mapper[movie_id]
-    movie_vec = X[movie_ind]
-    k += 1
-    kNN = NearestNeighbors(n_neighbors=k, algorithm="brute", metric=metric)
-    kNN.fit(X)
-    movie_vec = movie_vec.reshape(1, -1)
-    neighbour = kNN.kneighbors(movie_vec, return_distance=show_distance)
-    for i in range(0, k):
-        n = neighbour.item(i)
-        neighbour_ids.append(movie_inv_mapper[n])
-    neighbour_ids.pop(0)
-    return neighbour_ids
-
-
+def create_matrix(df):A=df;B=len(A[_B].unique());C=len(A[_A].unique());D=dict(zip(np.unique(A[_B]),list(range(B))));E=dict(zip(np.unique(A[_A]),list(range(C))));F=dict(zip(list(range(B)),np.unique(A[_B])));G=dict(zip(list(range(C)),np.unique(A[_A])));H=[D[A]for A in A[_B]];I=[E[A]for A in A[_A]];J=csr_matrix((A[_C],(I,H)),shape=(C,B));return J,D,E,F,G
+def find_similar_movies(movie_id,X,k,metric='cosine',show_distance=False):
+        A=[];D=movie_mapper[movie_id];B=X[D];k+=1;C=NearestNeighbors(n_neighbors=k,algorithm='brute',metric=metric);C.fit(X);B=B.reshape(1,-1);E=C.kneighbors(B,return_distance=show_distance)
+        for F in range(0,k):G=E.item(F);A.append(movie_inv_mapper[G])
+        A.pop(0);return A
 def recommend_movies(movie_name):
-    movie_id = [k for k, v in movie_titles.items() if movie_name.lower() in v.lower()]
-    if len(movie_id) == 0:
-        return "Movie not found. Please check the spelling and try again"
-    movie_id = movie_id[0]
-    similar_ids = find_similar_movies(movie_id, X, k=10)
-    recommendations = "\n".join([movie_titles[i] for i in similar_ids])
-    return recommendations
-
-
-# Load data
-ratings = pd.read_csv("ratings.csv")
-movies = pd.read_csv("movies.csv")
-n_ratings = len(ratings)
-n_movies = len(ratings['movieId'].unique())
-n_users = len(ratings['userId'].unique())
-user_freq = ratings[['userId', 'movieId']].groupby('userId').count().reset_index()
-user_freq.columns = ['userId', 'n_ratings']
-mean_rating = ratings.groupby('movieId')[['rating']].mean()
-lowest_rated = mean_rating['rating'].idxmin()
-highest_rated = mean_rating['rating'].idxmax()
-movie_stats = ratings.groupby('movieId')[['rating']].agg(['count', 'mean'])
-movie_stats.columns = movie_stats.columns.droplevel()
-X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_matrix(ratings)
-movie_titles = dict(zip(movies['movieId'], movies['title']))
-
-# Set up Gradio interface
-movie_name = gr.inputs.Textbox(label="Movie Name")
-outputs = gr.outputs.Textbox(label="Recommended Movies", type="text")
-
-iface = gr.Interface(
-    fn=recommend_movies, 
-    inputs=movie_name, 
-    outputs=outputs,
-    theme=gr.themes.Default(primary_hue="slate"))
-iface.launch()
+        A=[A for(A,B)in movie_titles.items()if movie_name.lower()in B.lower()]
+        if len(A)==0:return'Movie not found. Please check the spelling and try again'
+        A=A[0];B=find_similar_movies(A,X,k=10);C='\n'.join([movie_titles[A]for A in B]);return C
+ratings=pd.read_csv('ratings.csv')
+movies=pd.read_csv('movies.csv')
+n_ratings=len(ratings)
+n_movies=len(ratings[_A].unique())
+n_users=len(ratings[_B].unique())
+user_freq=ratings[[_B,_A]].groupby(_B).count().reset_index()
+user_freq.columns=[_B,'n_ratings']
+mean_rating=ratings.groupby(_A)[[_C]].mean()
+lowest_rated=mean_rating[_C].idxmin()
+highest_rated=mean_rating[_C].idxmax()
+movie_stats=ratings.groupby(_A)[[_C]].agg(['count','mean'])
+movie_stats.columns=movie_stats.columns.droplevel()
+X,user_mapper,movie_mapper,user_inv_mapper,movie_inv_mapper=create_matrix(ratings)
+movie_titles=dict(zip(movies[_A],movies['title']))
+movie_name=gr.inputs.Textbox(label='Movie Name')
+outputs=gr.outputs.Textbox(label='Recommended Movies',type='text')
+iface=gr.Interface(fn=recommend_movies,inputs=movie_name,outputs=outputs,theme=gr.themes.Default(primary_hue='slate'))
+iface.launch()