Update app.py

app.py

@@ -6,6 +6,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 from nltk.stem.porter import PorterStemmer
 import gradio as gr
 import nltk
+import difflib
 nltk.download('punkt')
 
 # Load data
@@ -72,22 +73,29 @@ similarity = cosine_similarity(vectors)
 # Recommendation function
 def recommend(movie):
     movie = movie.lower()
-    if movie not in new_df['title'].str.lower().values:
-        return ["Movie not found in database :( "]
 
+    titles = new_df['title'].str.lower().tolist()
+    # close_matches = difflib.get_close_matches(movie, titles, n=10, cutoff=0.4)
+    #
+    # if not close_matches:
+    #     return ["Movie not found"], []
+    #
+    # if movie not in new_df['title'].str.lower().values:
+    #     return ["Movie not found in database :( "]
+    # # movie = close_matches[0]
     index = new_df[new_df['title'].str.lower() == movie].index[0]
     distances = similarity[index]
     movie_list = sorted(enumerate(distances), reverse=True, key=lambda x: x[1])[1:6]
     return [new_df.iloc[i[0]].title for i in movie_list]
 
-
+movie_list = new_df['title'].tolist()
 # Gradio interface
 def recommend_interface(movie_name):
     return recommend(movie_name)
 
 
 demo = gr.Interface(fn=recommend_interface,
-                    inputs=gr.Dropdown(lines=1, placeholder="Enter a movie name..."),
+                    inputs=gr.Dropdown(movie_list, label="Select a movie..."),
                     outputs=gr.List(label="Top 5 Recommendations"),
                     title="Movie Recommender")