adds year-filter to functions

pages/1 - Popularity-Based Recommender.py

@@ -16,12 +16,16 @@ movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] =
 movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = 'A ' + movies['title']
 movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = movies['title'].str.replace(", A", '', regex=True)
 
+# extract year from title and store it in new column
+movies= movies.assign(year = lambda df_ : df_['title'].replace(r'(.*)\((\d{4})\)', r'\2', regex= True))
+movies.year = pd.to_numeric(movies.year, errors= 'coerce').fillna(0).astype('int')
+
 # INSTRUCTIONS:
 st.title("Popularity-Based Recommender")
 
 # FUNCTIONS:
 
-def get_popular_recommendations(n, genres):
+def get_popular_recommendations(n, genres, time_range):
     recommendations = (
         ratings
             .groupby('movieId')
@@ -29,6 +33,7 @@ def get_popular_recommendations(n, genres):
             .merge(movies, on='movieId')
             .assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
             [lambda df: df["genres"].str.contains(genres, regex=True)]
+            .loc[lambda df : ((df['year'] >= time_range[0]) & ( df['year'] <= time_range[1]))]
             .sort_values('combined_rating', ascending=False)
             .head(n)
             [['title', 'avg_rating', 'genres']]
@@ -36,7 +41,7 @@ def get_popular_recommendations(n, genres):
     )
     return recommendations
 
-def get_popular_recommendations_streaming(n, genres, country, url, headers):
+def get_popular_recommendations_streaming(n, genres, time_range, country, url, headers):
     recommendations = (
         ratings
             .groupby('movieId')
@@ -44,6 +49,7 @@ def get_popular_recommendations_streaming(n, genres, country, url, headers):
             .merge(movies, on='movieId')
             .assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
             [lambda df: df["genres"].str.contains(genres, regex=True)]
+            .loc[lambda df : ((df['year'] >= time_range[0]) & ( df['year'] <= time_range[1]))]
             .sort_values('combined_rating', ascending=False)
             .head(n)
             [['title', 'avg_rating', 'genres', 'movieId']]
@@ -88,6 +94,11 @@ Move the slider to the desired number of recommendations you wish to receive.
 """)
 number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)
 
+st.write("""
+Move the sliders to choose a timeperiod for your recommendations.
+""")
+time_range = st.slider('Time-period:', min_value=1900, max_value=2018, value=(1900, 2018), step=1)
+
 st.write("""
 __Optional__: You can narrow down the recommendations by picking one or several genre(s).  
 However, the more genres you choose, the fewer movies will be recommended.
@@ -103,6 +114,7 @@ Select none if you don't want to get streaming links.
 streaming_country = st.selectbox('Optional: Country for streaming information', ('none', 'de', 'us'))
 
 # API INFORMATION:
+# Streaming availability
 url = "https://streaming-availability.p.rapidapi.com/get/basic"
 headers = {
 	"X-RapidAPI-Key": st.secrets["api_key"],
@@ -113,7 +125,11 @@ headers = {
 
 if st.button("Get Recommendations"):
     if streaming_country == 'none':
-        st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
+        st.write(get_popular_recommendations(number_of_recommendations, genres_regex, time_range))
     else: 
-        st.write("Double-click on a Streaming-Availability cell to see all options.")
-        st.write(get_popular_recommendations_streaming(number_of_recommendations, genres_regex, streaming_country, url, headers))
+        try:
+            recommendations = get_popular_recommendations_streaming(number_of_recommendations, genres_regex, time_range, streaming_country, url, headers)
+            st.write("Double-click on a Streaming-Availability cell to see all options.", recommendations)
+        except:
+            recommendations = get_popular_recommendations(number_of_recommendations, genres_regex, time_range)
+            st.write('Error: Streaming information could not be gathered. Providing output without streaming availability instead.', recommendations)

pages/2 - Similarity-Based Recommender.py

@@ -3,6 +3,7 @@ import pandas as pd
 from sklearn.metrics.pairwise import cosine_similarity
 import re
 import requests
+import api_keys
 
 # DATA:
 movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/movies.csv')
@@ -17,6 +18,10 @@ movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] =
 movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = 'A ' + movies['title']
 movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = movies['title'].str.replace(", A", '', regex=True)
 
+# extract year from title and store it in new column
+movies= movies.assign(year = lambda df_ : df_['title'].replace(r'(.*)\((\d{4})\)', r'\2', regex= True))
+movies.year = pd.to_numeric(movies.year, errors= 'coerce').fillna(0).astype('int')
+
 # create "database" to use for recommendations
 movie_user_matrix = (
                 ratings
@@ -33,7 +38,7 @@ st.title("User-Based Recommender")
 
 # FUNCTIONS:
 
-def get_similar_recommendations(movie_title, n, genres):
+def get_similar_recommendations(movie_title, n, genres, time_range):
     
     # select similarity for chosen movie
     similarities = pd.DataFrame(
@@ -45,6 +50,7 @@ def get_similar_recommendations(movie_title, n, genres):
         similarities
             .merge(movies, how= 'left', left_index = True, right_on = 'title')
             [lambda df: df["genres"].str.contains(genres, regex=True)]
+            .loc[lambda df : ((df['year'] >= time_range[0]) & ( df['year'] <= time_range[1]))]
             .head(n)
             [['title', 'genres']]
             )
@@ -53,7 +59,7 @@ def get_similar_recommendations(movie_title, n, genres):
 
     return recommendations
 
-def get_similar_recommendations_streaming(movie_title, n, genres, country, url, headers):
+def get_similar_recommendations_streaming(movie_title, n, genres, time_range, country, url, headers):
 
     # select similarity for chosen movie
     similarities = pd.DataFrame(
@@ -65,6 +71,7 @@ def get_similar_recommendations_streaming(movie_title, n, genres, country, url,
         similarities
             .merge(movies, how= 'left', left_index = True, right_on = 'title')
             [lambda df: df["genres"].str.contains(genres, regex=True)]
+            .loc[lambda df : ((df['year'] >= time_range[0]) & ( df['year'] <= time_range[1]))]
             .head(n)
             [['title', 'genres', 'movieId']]
             )
@@ -125,6 +132,11 @@ Move the slider to the desired number of recommendations you wish to receive.
 """)
 number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)
 
+st.write("""
+Move the sliders to choose a timeperiod for your recommendations.
+""")
+time_range = st.slider('Time-period:', min_value=1900, max_value=2018, value=(1900, 2018), step=1)
+
 st.write("""
 __Optional__: You can narrow down the recommendations by picking one or several genre(s).  
 However, the more genres you choose, the fewer movies will be recommended.
@@ -153,5 +165,9 @@ if st.button("Get Recommendations"):
     if streaming_country == 'none':
         st.write(get_similar_recommendations(movie_title, number_of_recommendations, genres_regex))
     else:
-        st.write("Double-click on the Streaming-Availability column to see all links.")
-        st.write(get_similar_recommendations_streaming(movie_title, number_of_recommendations, genres_regex, streaming_country, url, headers))
+        try:
+            recommendations = get_similar_recommendations_streaming(movie_title, number_of_recommendations, genres_regex, streaming_country, url, headers)
+            st.write("Double-click on a Streaming-Availability cell to see all options.", recommendations)
+        except:
+            recommendations = get_similar_recommendations(movie_title, number_of_recommendations, genres_regex)
+            st.write('Error: Streaming information could not be gathered. Providing output without streaming availability instead.', recommendations)