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import streamlit as st
import pickle
import pandas as pd
st.image("Netflix.png")
movies_list = pickle.load(open("content_dict.pkl",'br'))
movies = pd.DataFrame(movies_list)
similarity= pickle.load(open('cosine_similarity.pkl','rb'))
def recommend(title, cosine_sim=similarity, data=movies):
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 programs 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
# Displaying title
st.title(" Movie Recommender System ")
movie_list = movies['title'].values
selected_movie = st.selectbox(
"Type or select a movie from the dropdown",
movie_list
)
# Setting a button
if st.button('Show Recommendation'):
recommended_movie_names = recommend(selected_movie)
st.balloons()
for j in recommended_movie_names:
st.write(j) |