first commit

main.py

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+import streamlit as st
+import pickle
+import pandas as pd
+import requests
+
+def fetch_poster(movie_id):
+    data = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=3da50835eefad1461468d6eedd91edf7&language=en-US'.format(movie_id))
+    data = data.json()
+    return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
+
+def recommend(movie):
+    movie_index = movies[movies['title'] == movie].index[0]
+    distances = similarity[movie_index]
+    movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
+
+    recommended_movies = []
+    recommended_movies_posters = []
+    for i in movies_list:
+        movie_id = movies.iloc[i[0]].movie_id
+        recommended_movies.append(movies.iloc[i[0]].title)
+        recommended_movies_posters.append(fetch_poster(movie_id))
+    return recommended_movies,recommended_movies_posters
+
+movies_dict = pickle.load(open('movies.pkl','rb'))
+movies = pd.DataFrame(movies_dict)
+
+similarity = pickle.load(open('similarity.pkl','rb'))
+
+st.title('Movie Recommender System')
+
+selected_movie_name = st.selectbox('Name of Movies', movies['title'].values)
+
+if st.button('Recommend'):
+    names,posters = recommend(selected_movie_name)
+    col1, col2, col3, col4 ,col5 = st.columns(5)
+
+    with col1:
+        st.text(names[0])
+        st.image(posters[0])
+
+    with col2:
+        st.text(names[1])
+        st.image(posters[1])
+
+    with col3:
+        st.text(names[2])
+        st.image(posters[2])
+
+    with col4:
+        st.text(names[3])
+        st.image(posters[3])
+
+    with col5:
+        st.text(names[4])
+        st.image(posters[4])