Create app.py

app.py

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+# main code
+
+import streamlit as st
+import pickle
+import requests
+
+movies = pickle.load(open("movies_list.pkl", 'rb'))
+similarity = pickle.load(open("similarity.pkl",'rb'))
+movies_list = movies['title'].values
+st.header("Movie Recommender System")
+selectvalue = st.selectbox("Select movie from dropdown", movies_list)
+
+def fetch_poster(movie_id):
+    try:
+        url = "https://api.themoviedb.org/3/movie/{}?api_key=8cfe8dff1a6fff88fe27b573ee65c035&language=en-US".format(movie_id)
+        # Disablinb SSL verification for development
+        data = requests.get(url, verify=False)
+        data = data.json()
+        poster_path = data['poster_path']
+        if poster_path:
+            full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+            return full_path
+        else:
+            st.warning(f"No poster found for movie ID {movie_id}")
+            return None
+    except Exception as e:
+        st.error(f"Error fetching poster: {str(e)}")
+        return None
+
+def recommend(movie):
+    index = movies[movies['title']==movie].index[0]
+    distance = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda vector:vector[1])
+    recommend_movie = []
+    recommend_poster = []
+    for i in distance[1:6]:
+        movies_id = movies.iloc[i[0]].id
+        recommend_movie.append(movies.iloc[i[0]].title)
+        poster = fetch_poster(movies_id)
+        recommend_poster.append(poster)
+    return recommend_movie, recommend_poster
+
+if st.button("Show Recommend"):
+    movie_name, movie_poster = recommend(selectvalue)
+    cols = st.columns(5)
+    for idx, (col, name, poster) in enumerate(zip(cols, movie_name, movie_poster)):
+        with col:
+            st.text(name)
+            if poster:  # showing image if url exists
+                st.image(poster)
+            else:
+                st.write("No poster available")