Upload app.py

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import CountVectorizer
+from imdb import IMDb
+
+
+similarity = pickle.load(open('cosine_sim.pkl', 'rb'))
+movie_dict = pickle.load(open('movie_dict.pkl', 'rb'))
+movies = pd.DataFrame(movie_dict)
+
+programme_list=movies['title'].to_list()
+
+imdb = IMDb()
+def get_movie_id(movie_title):
+    """Get the IMDb ID of the movie using the IMDbPY library."""
+    try:
+
+        movies = imdb.search_movie(movie_title)
+        movie_id = movies[0].getID()  # get the ID of the first search result
+        return movie_id
+
+    except Exception as e:
+        st.error("Error: Failed to retrieve IMDb ID for the selected movie. Please try again with a different movie.")
+        st.stop()
+
+
+
+def get_poster_url(imdb_id):
+    """Get the URL of the poster image of the movie using the IMDbPY library."""
+    try:
+
+        movie = imdb.get_movie(imdb_id)
+        poster_url = movie['full-size cover url']
+        return poster_url
+
+    except Exception as e:
+        st.error("Error: Failed to retrieve poster URL for the selected movie. Please try again with a different movie.")
+        st.stop()
+
+
+
+def recommend(movie):
+    index = programme_list.index(movie)         
+    sim_score = list(enumerate(similarity[index])) #creates a list of tuples containing the similarity score and index between the input title and all other programmes in the dataset.
+    
+    #position 0 is the movie itself, thus exclude
+    sim_score = sorted(sim_score, key= lambda x: x[1], reverse=True)[1:6]  #sorts the list of tuples by similarity score in descending order.
+    recommend_index = [i[0] for i in sim_score]
+    rec_movie = movies['title'].iloc[recommend_index]
+    rec_movie_ids = [get_movie_id(title) for title in rec_movie]
+    return rec_movie, rec_movie_ids
+    
+st.set_page_config(page_title='Movie Recommender System', page_icon=':clapper:', layout='wide')
+st.title('Movie Recommender System')
+
+
+selected_movie_name = st.selectbox('Please select a Movie',
+sorted(movies['title'].values))
+
+if st.button('Recommend Me'):
+    try:
+
+        recommendations, rec_movie_ids = recommend(selected_movie_name)
+        # st.write(recommendations, rec_movie_ids)
+        # st.write(recommendations[6195])
+        final_movie_names = []
+        for i, rec_id in zip(recommendations, rec_movie_ids):
+            final_movie_names.append(i)
+            # st.write(i)
+            # poster_url = get_poster_url(rec_id)
+            # st.image(poster_url)
+
+
+        col1, col2, col3, col4, col5 = st.columns(5)
+        cols = [col1, col2, col3, col4, col5]
+        with col1:
+            st.text(final_movie_names[0])
+            poster_url = get_poster_url(rec_movie_ids[0])
+            st.image(poster_url)
+        with col2:
+            st.text(final_movie_names[1])
+            poster_url = get_poster_url(rec_movie_ids[1])
+            st.image(poster_url)
+        with col3:
+            st.text(final_movie_names[2])
+            poster_url = get_poster_url(rec_movie_ids[2])
+            st.image(poster_url)
+        with col4:
+            st.text(final_movie_names[3])
+            poster_url = get_poster_url(rec_movie_ids[3])
+            st.image(poster_url)
+        with col5:
+            st.text(final_movie_names[4])
+            poster_url = get_poster_url(rec_movie_ids[4])
+            st.image(poster_url)
+    except Exception as e:
+        st.write('An error occurred while generating recommendations:', e)
+