Added all files for deployment

.gitignore

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+venv

README.md

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+# Movie-Recommender-System

app.py

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+import streamlit as st
+import pickle
+import requests
+
+movies_df = pickle.load(open('movie.pkl', 'rb'))
+movie_list = movies_df['title'].values
+
+similarity = pickle.load(open('similarity.pkl', 'rb'))
+
+
+def fetch_poster(id):
+    response = requests.get("https://api.themoviedb.org/3/movie/{}?api_key=b25587cadc014bbcf38f0b9c3d577fb0".format(id))
+    data = response.json()
+    poster = "https://image.tmdb.org/t/p/w500/" + data['poster_path']
+    return  poster
+
+
+# creating the function to recommend movie
+def recommend(selected_movie, movies_df):
+    index = movies_df[movies_df['title'] == selected_movie].index[0]
+    distances = similarity[index]
+    movies = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
+
+    recommended_movies = []
+    recommended_movies_poster = []
+    for movi in movies:
+        recommended_movies.append(movies_df.iloc[movi[0]].title)
+        recommended_movies_poster.append(fetch_poster(movies_df.iloc[movi[0]].id))
+    return recommended_movies , recommended_movies_poster
+
+
+st.title('Movie Recommender System')
+selected_movie = st.selectbox('Select your movie', movie_list)
+if st.button('Recommend'):
+    names, posters = recommend(selected_movie, movies_df)
+
+    col1, col2, col3, col4, col5 = st.columns(5)
+
+    with col1:
+        st.subheader(names[0])
+        st.image(posters[0])
+
+    with col2:
+        st.subheader(names[1])
+        st.image(posters[1])
+
+    with col3:
+        st.subheader(names[2])
+        st.image(posters[2])
+
+    with col4:
+        st.subheader(names[3])
+        st.image(posters[3])
+
+    with col5:
+        st.subheader(names[4])
+        st.image(posters[4])
+