Initial commit

.vs/VSWorkspaceState.json

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+{
+  "ExpandedNodes": [
+    ""
+  ],
+  "SelectedNode": "\\C:\\Python\\Programs\\Gradio\\HuggingSpace\\MovieRecommendations",
+  "PreviewInSolutionExplorer": false
+}

app.py

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+import gradio as gr
+import numpy as np
+import 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 recommend_movies(movie_id):
+    similar_ids = find_similar_movies(movie_id, X, k=10)
+    movie_title = movie_titles[movie_id]
+    recommendations = []
+    for i in similar_ids:
+        recommendations.append(movie_titles[i])
+    return recommendations
+
+# Load data
+ratings = pd.read_csv("https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv")
+movies = pd.read_csv("https://s3-us-west-2.amazonaws.com/recommender-tutorial/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_id = gr.inputs.Number(default=3, label="Movie ID")
+iface = gr.Interface(
+    fn=recommend_movies, 
+    inputs=movie_id, 
+    outputs="text",
+    title="Movie Recommender System",
+    description="Enter a movie ID and get recommendations for similar movies."
+)
+iface.launch()
+

requirements.txt

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+numpy
+pandas
+spicy
+sklearn