Update app.py

app.py

@@ -1,66 +1,66 @@
-import pickle
-import streamlit as st
-import requests
-
-def fetch_poster(movie_id):
-    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id)
-    data = requests.get(url)
-    data = data.json()
-    print("API Response for movie ID {}: {}".format(movie_id, data))  # Print API response for debugging
-    if 'poster_path' in data:
-        poster_path = data['poster_path']
-        full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
-        return full_path
-    else:
-        # Handle the case when 'poster_path' is not found
-        print("Poster path not found for movie ID:", movie_id)  # Print error message for debugging
-        return "Placeholder_for_missing_poster"
-
-def recommend(movie):
-    index = movies[movies['title'] == movie].index[0]
-    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
-    recommended_movie_names = []
-    recommended_movie_posters = []
-    for i in distances[1:6]:
-        # fetch the movie poster
-        movie_title = movies.iloc[i[0]]['title']  # Fetch the movie title
-        print("Fetching poster for movie:", movie_title)  # Print movie title for debugging
-        # Use the movie title to fetch the movie ID from the API
-        movie_id = fetch_movie_id(movie_title)
-        if movie_id:
-            recommended_movie_posters.append(fetch_poster(movie_id))
-            recommended_movie_names.append(movie_title)  # Append movie title instead of 'title' column from DataFrame
-
-    return recommended_movie_names, recommended_movie_posters
-
-# Function to fetch movie ID based on the title (modify this according to your API)
-def fetch_movie_id(movie_title):
-    # Implement the logic to fetch movie ID using the movie title
-    # For demonstration, I'm assuming a simple dictionary mapping of titles to IDs
-    movie_id_mapping = {
-        "Movie Title 1": "id1",
-        "Movie Title 2": "id2",
-        # Add more mappings as needed
-    }
-    return movie_id_mapping.get(movie_title, None)
-
-st.header('Movie Recommender System')
-movies = pickle.load(open('movie_list.pkl','rb'))
-similarity = pickle.load(open('similarity1.pkl','rb'))
-
-movie_list = movies['title'].values
-selected_movie = st.selectbox(
-    "Type or select a movie from the dropdown",
-    movie_list
-)
-
-if st.button('Show Recommendation'):
-    recommended_movie_names, recommended_movie_posters = recommend(selected_movie)
-    if not recommended_movie_names:
-        st.write("No recommendations found.")
-    else:
-        col1, col2, col3, col4, col5 = st.columns(5)
-        for name, poster in zip(recommended_movie_names, recommended_movie_posters):
-            with col1:
-                st.text(name)
-                st.image(poster)
+import pickle
+import streamlit as st
+import requests
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id)
+    data = requests.get(url)
+    data = data.json()
+    print("API Response for movie ID {}: {}".format(movie_id, data))  # Print API response for debugging
+    if 'poster_path' in data:
+        poster_path = data['poster_path']
+        full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+        return full_path
+    else:
+        # Handle the case when 'poster_path' is not found
+        print("Poster path not found for movie ID:", movie_id)  # Print error message for debugging
+        return "Placeholder_for_missing_poster"
+
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    for i in distances[1:6]:
+        # fetch the movie poster
+        movie_title = movies.iloc[i[0]]['title']  # Fetch the movie title
+        print("Fetching poster for movie:", movie_title)  # Print movie title for debugging
+        # Use the movie title to fetch the movie ID from the API
+        movie_id = fetch_movie_id(movie_title)
+        if movie_id:
+            recommended_movie_posters.append(fetch_poster(movie_id))
+            recommended_movie_names.append(movie_title)  # Append movie title instead of 'title' column from DataFrame
+
+    return recommended_movie_names, recommended_movie_posters
+
+# Function to fetch movie ID based on the title (modify this according to your API)
+def fetch_movie_id(movie_title):
+    # Implement the logic to fetch movie ID using the movie title
+    # For demonstration, I'm assuming a simple dictionary mapping of titles to IDs
+    movie_id_mapping = {
+        "Movie Title 1": "id1",
+        "Movie Title 2": "id2",
+        # Add more mappings as needed
+    }
+    return movie_id_mapping.get(movie_title, None)
+
+st.header('Movie Recommender System')
+movies = pickle.load(open('movie_list.pkl','rb'))
+similarity = pickle.load(open('similarity.pkl','rb'))
+
+movie_list = movies['title'].values
+selected_movie = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movie_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names, recommended_movie_posters = recommend(selected_movie)
+    if not recommended_movie_names:
+        st.write("No recommendations found.")
+    else:
+        col1, col2, col3, col4, col5 = st.columns(5)
+        for name, poster in zip(recommended_movie_names, recommended_movie_posters):
+            with col1:
+                st.text(name)
+                st.image(poster)