.

app.py

@@ -1,9 +1,11 @@
+
 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())
@@ -18,6 +20,7 @@ def create_matrix(df):
     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]
@@ -25,22 +28,25 @@ def find_similar_movies(movie_id, X, k, metric='cosine', show_distance=False):
     k += 1
     kNN = NearestNeighbors(n_neighbors=k, algorithm="brute", metric=metric)
     kNN.fit(X)
-    movie_vec = movie_vec.reshape(1,-1)
+    movie_vec = movie_vec.reshape(1, -1)
     neighbour = kNN.kneighbors(movie_vec, return_distance=show_distance)
-    for i in range(0,k):
+    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):
+
+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"]
+    movie_id = movie_id[0]
     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])
+    recommendations = [movie_titles[i] for i in similar_ids]
     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")
@@ -58,12 +64,12 @@ X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_matrix(
 movie_titles = dict(zip(movies['movieId'], movies['title']))
 
 # Set up Gradio interface
-movie_id = gr.inputs.Number(default=3, label="Movie ID")
+movie_name = gr.inputs.Textbox(default="The Shawshank Redemption", label="Movie Name")
 iface = gr.Interface(
     fn=recommend_movies, 
-    inputs=movie_id, 
+    inputs=movie_name, 
     outputs="text",
     title="Movie Recommender System",
-    description="Enter a movie ID and get recommendations for similar movies."
+    description="Enter a movie name and get recommendations for similar movies."
 )
 iface.launch()