Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,57 @@
+
+import streamlit as st 
+import pickle 
+import pandas as pd 
+import requests
+
+def fetch_poster(movie_id):
+    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
+    data = response.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]
+    recomended_movies = [] 
+    recommended_movies_posters = []
+    for i in movies_list:
+        movie_id= movies.iloc[i[0]].movie_id
+        recomended_movies.append(movies.iloc[i[0]].title)
+        recommended_movies_posters.append(fetch_poster(movie_id))
+    return recomended_movies, recommended_movies_posters
+    
+
+movies_dict = pickle.load(open('movie_dict.pkl', 'rb'))
+movies = pd.DataFrame(movies_dict) 
+similarity = pickle.load(open('similarity.pkl', 'rb'))
+
+
+st.title('Movie Recommender System')
+
+selected_movie_name = st.selectbox(
+    'How',
+    movies['title'].values
+)
+
+if st.button('Recommend'):
+    recommended_movie_names, recommended_movie_posters = recommend(selected_movie_name)
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(recommended_movie_names[0])
+        st.image(recommended_movie_posters[0])
+    with col2:
+        st.text(recommended_movie_names[1])
+        st.image(recommended_movie_posters[1])
+
+    with col3:
+        st.text(recommended_movie_names[2])
+        st.image(recommended_movie_posters[2])
+    with col4:
+        st.text(recommended_movie_names[3])
+        st.image(recommended_movie_posters[3])
+    with col5:
+        st.text(recommended_movie_names[4])
+        st.image(recommended_movie_posters[4])
+