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app.py

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+import pickle
+import streamlit as st
+from sklearn.metrics.pairwise import cosine_similarity
+from PIL import Image
+from scipy.sparse import csr_matrix
+
+
+@st.cache_data
+def get_recommendation(title):
+    idx = df1.index[df1['title'] == title][0]
+    poster = f'https://image.tmdb.org/t/p/w500/{df1.loc[idx, "poster_path"]}'
+
+    # Get the pairwise similarity scores of all movies with that movie
+    sim_scores = list(enumerate(
+        cosine_similarity(
+            tfidf_matrix,
+            tfidf_matrix[idx])))
+
+    # Sort the movies based on the similarity scores
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+
+    # Get the scores of the 10 most similar movies
+    sim_scores = sim_scores[1:13]
+
+    # Get the movie indices
+    movie_indices = [i[0] for i in sim_scores]
+
+    # Return the top 10 most similar movies
+    result = df1.iloc[movie_indices]
+
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    recommended_movie_overview = []
+
+    for i, j in enumerate(result.poster_path):
+        recommended_movie_names.append(result.iloc[i].title)
+        recommended_movie_posters.append(f'https://image.tmdb.org/t/p/w500/{j}')
+        recommended_movie_overview.append(result.iloc[i].overview)
+    return poster, recommended_movie_names, recommended_movie_posters, recommended_movie_overview
+
+
+image = Image.open('Movie recommender system.jpg')
+st.title("Movie Recommendation Engine")
+st.image(image)
+
+st.markdown('For this project, I developed a Content-Based Recommendation System that suggests movies based on attributes like genre, director, description, and actors. The system operates on the principle that users who enjoyed a specific movie or show are likely to appreciate other movies or shows with similar characteristics.')
+
+df1 = pickle.load(open('movie_list.pkl', 'rb'))
+tfidf_matrix = pickle.load(open('tfidf_matrix.pkl', 'rb'))
+
+movies_list = df1['title'].values
+selected_movie = st.sidebar.selectbox('Select a Movie', movies_list)  # Move selectbox to the sidebar
+
+if st.sidebar.button('Show Recommendation'):  # Move button to the sidebar
+    poster, recommended_movie_names, recommended_movie_posters, recommended_movie_overview = get_recommendation(
+        selected_movie)
+    st.image(poster, width=160)
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.image(recommended_movie_posters[0])
+        st.markdown(recommended_movie_names[0])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[0])
+
+        st.image(recommended_movie_posters[4])
+        st.markdown(recommended_movie_names[4])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[4])
+
+        st.image(recommended_movie_posters[8])
+        st.markdown(recommended_movie_names[8])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[8])
+
+    with col2:
+        st.image(recommended_movie_posters[1])
+        st.markdown(recommended_movie_names[1])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[1])
+
+        st.image(recommended_movie_posters[5])
+        st.markdown(recommended_movie_names[5])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[5])
+
+        st.image(recommended_movie_posters[9])
+        st.markdown(recommended_movie_names[9])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[9])
+
+    with col3:
+        st.image(recommended_movie_posters[2])
+        st.markdown(recommended_movie_names[2])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[2])
+
+        st.image(recommended_movie_posters[6])
+        st.markdown(recommended_movie_names[6])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[6])
+
+        st.image(recommended_movie_posters[10])
+        st.markdown(recommended_movie_names[10])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[10])
+
+    with col4:
+        st.image(recommended_movie_posters[3])
+        st.markdown(recommended_movie_names[3])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[3])
+
+        st.image(recommended_movie_posters[7])
+        st.markdown(recommended_movie_names[7])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[7])
+
+        st.image(recommended_movie_posters[11])
+        st.markdown(recommended_movie_names[11])
+        with st.expander("OverView"):
+            st.write(recommended_movie_overview[11])
+st.sidebar.markdown("Made by Uday Singh")

movie_list.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:24cf160e196ce93c871caf36bcb5d1b1ee16ba5b6ad20f44a6ac40556a4bf605
+size 53612081

requirements.txt

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+streamlit
+pillow
+scipy
+scikit-learn==1.0.2
+pandas==1.3.5
+numpy==1.21.6

tfidf_matrix.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8fdc5ad0bec25dad6f3c948c137a67b01506566f8efbaf0eebd44dbe30e514a0
+size 20298550