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app.py

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+######################################################
+# Importing necessary libraries
+import streamlit as st
+import pickle
+import pandas as pd
+
+#######################################################
+# Loading the pickle file
+content_dict= pickle.load(open('content_dict.pkl','rb'))
+
+# Converting dictionary into pandas DataFrame
+content= pd.DataFrame(content_dict)
+
+# Loding the pickle file
+similarity= pickle.load(open('cosine_similarity.pkl','rb'))
+
+#######################################################
+# Defining a function for recommendation system
+def recommend(title, cosine_sim=similarity, data=content):
+    recommended_content=[]
+    # Get the index of the input title in the programme_list
+    programme_list = data['title'].to_list()
+    index = programme_list.index(title)
+
+    # Create a list of tuples containing the similarity score and index 
+    # between the input title and all other programmes in the dataset
+    sim_scores = list(enumerate(cosine_sim[index]))
+
+    # Sort the list of tuples by similarity score in descending order
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)[1:11]
+
+    # Get the recommended movie titles and their similarity scores
+    recommend_index = [i[0] for i in sim_scores]
+    rec_movie = data['title'].iloc[recommend_index]
+    rec_score = [round(i[1], 4) for i in sim_scores]
+
+    # Create a pandas DataFrame to display the recommendations
+    rec_table = pd.DataFrame(list(zip(rec_movie, rec_score)), columns=['Recommendation', 'Similarity_score(0-1)'])
+    # recommended_content.append(rec_table['Recommendation'].values)
+
+    return rec_table['Recommendation'].values
+
+#######################################################
+# # Loading the pickle file
+# content_dict= pickle.load(open('content_dict.pkl','rb'))
+
+# # Converting dictionary into pandas DataFrame
+# content= pd.DataFrame(content_dict)
+
+# # Loding the pickle file
+# similarity= pickle.load(open('cosine_similarity.pkl','rb'))
+
+########################################################
+# Displaying title
+st.title("Netflix Recommender System")
+
+# Display dialogue box that contains content
+selected_content_name = st.selectbox(
+    'Which Movie/TV Show are you watching?',
+    content['title'].values)
+st.write('**Note**: We have the data till 2019 only.')
+#########################################################
+
+# Setting a button
+if st.button('Recommend'):
+    recommendations= recommend(title=selected_content_name)
+    st.write('**_You are watching:_**', selected_content_name)
+    st.write('**_Your top 10 recommendations:_**')
+    for num,i in enumerate(recommendations):
+        st.write(num+1,':', i)
+
+    # Last note
+    st.write('_Lights out, popcorn in hand, and let the movies begin! We hope our recommendations hit the spot._:smile:')

content.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e366a1e58d083ba23635855fb173d811a8ec30a1c54cd0a581cd52ee0b55cac
+size 10107932

content_dict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02c7c712ba520b713b35581143b0fe070710cf39ec225c71adbbce35b1a0dd66
+size 10312847

cosine_similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d7016172d6a2f870b8cbcdcabd7e75bc58535dc3e3e6cbe24fcca83a835eadb
+size 482983363

recommend.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1212366ceb56df7555e013fd1a567c4dfadd1a8c65059d9ca62438d7cec9f298
+size 45

tfidf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0397536ca0d45239f9622c039f91e443670e524085c405dfcc535b8764d292b3
+size 1324438

tfidf_matrix.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c99d328016ea1889fc88f3502e7ef9534f673842ad2270efd432057d0ebd26f3
+size 3424782