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Models/movies_list.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a49e245df2f07650b0636368563395731468f2fecf5711e41efcf03f7e94b992
+size 3243620

Models/similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f201811c220c7a2869c375cc3619929fe969d76795330ffd9148b12d87c0f51
+size 800000163

app.py

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+import streamlit as st
+import pickle
+import requests
+
+# Function to load movie data and similarity scores
+@st.cache_data
+def load_data():
+    movies = pickle.load(open("Models/movies_list.pkl", 'rb'))
+    similarity = pickle.load(open("Models/similarity.pkl", 'rb'))
+    return movies, similarity
+
+# Function to fetch movie posters,
+@st.cache_data
+def fetch_poster(movie_id):
+    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=f99f126bfd58ba9b4aa8a3e6db301b6e&language=en-US"
+    data = requests.get(url).json()
+    poster_path = data.get('poster_path')
+    full_path = f"https://image.tmdb.org/t/p/w500/{poster_path}"
+    return full_path
+
+# Function to recommend movies based on the similarity scores
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movies = []
+    recommended_posters = []
+    for i in distances[1:6]:
+        movie_id = movies.iloc[i[0]].id
+        recommended_movies.append(movies.iloc[i[0]].title)
+        recommended_posters.append(fetch_poster(movie_id))
+    return recommended_movies, recommended_posters
+
+# Load movies and similarity data
+movies, similarity = load_data()
+
+# Streamlit UI
+st.title("Movie Recommender System")
+
+# Dynamic movie selection with autocomplete feature
+movie_list = movies['title'].values
+selected_movie = st.selectbox("Type or select a movie from the dropdown", movie_list)
+
+if st.button("Show Recommendations"):
+    recommended_movie_names, recommended_movie_posters = recommend(selected_movie)
+    
+    # Displaying recommendations in a more visually appealing manner
+    cols = st.columns(5)
+    for i, (movie_name, movie_poster) in enumerate(zip(recommended_movie_names, recommended_movie_posters)):
+        with cols[i % 5]:
+            st.image(movie_poster, caption=movie_name, use_column_width=True)
+
+st.markdown("""
+<style>
+/* Custom CSS for improving the UI/UX of the Streamlit app */
+.streamlit-container {
+    margin-top: 20px;
+}
+.stButton>button {
+    width: 100%;
+    border-radius: 20px;
+    border: 1px solid #4CAF50;
+    background-color: #4CAF50;
+    color: white;
+    padding: 10px 24px;
+    cursor: pointer;
+    font-size: 18px;
+}
+.stButton>button:hover {
+    background-color: #45a049;
+}
+</style>
+""", unsafe_allow_html=True)

movie_helpers.py

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+import requests
+
+def fetch_poster(movie_id):
+    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=f99f126bfd58ba9b4aa8a3e6db301b6e&language=en-US"
+    data = requests.get(url).json()
+    poster_path = data['poster_path']
+    full_path = f"https://image.tmdb.org/t/p/w500/{poster_path}"
+    return full_path
+
+def recommend(movie, movies, similarity):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movies = []
+    recommended_posters = []
+    for i in distances[1:8]:  # Get top 5 recommendations
+        movie_id = movies.iloc[i[0]].id
+        recommended_movies.append(movies.iloc[i[0]].title)
+        recommended_posters.append(fetch_poster(movie_id))
+    return recommended_movies, recommended_posters

requirements.txt

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+h2o_wave
+pandas
+numpy
+requests
+gdown