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| import streamlit as st | |
| import pickle | |
| import pandas as pd | |
| import requests | |
| def fetch_poster(movie_name): | |
| """ | |
| Fetch movie posters using the IMDb API via RapidAPI. | |
| Args: | |
| movie_name (str): The name of the movie for which to fetch the poster. | |
| Returns: | |
| str: The URL of the movie poster or a placeholder URL if not available. | |
| """ | |
| url = "https://imdb8.p.rapidapi.com/title/find" | |
| querystring = {"q": movie_name} # Search by movie name | |
| headers = { | |
| "x-rapidapi-key": "1fbfa1d6b2msh32cfe69cdbe3847p1a958ajsn576375deeb9c", # Replace with your RapidAPI key | |
| "x-rapidapi-host": "imdb8.p.rapidapi.com" | |
| } | |
| try: | |
| response = requests.get(url, headers=headers, params=querystring, timeout=30) | |
| response.raise_for_status() # Check for HTTP request errors | |
| data = response.json() | |
| results = data.get("results", []) | |
| if results: | |
| poster_url = results[0].get("image", {}).get("url") # Access the poster URL | |
| if poster_url: | |
| return poster_url | |
| st.warning("Poster not found for movie: {}".format(movie_name)) | |
| return "https://via.placeholder.com/500x750?text=No+Poster+Available" # Placeholder image | |
| except requests.exceptions.Timeout: | |
| st.error("The request timed out while fetching poster for movie: {}".format(movie_name)) | |
| return "https://via.placeholder.com/500x750?text=Timeout+Error" # Timeout placeholder | |
| except requests.exceptions.RequestException as e: | |
| st.error(f"An error occurred while fetching poster for {movie_name}: {e}") | |
| return "https://via.placeholder.com/500x750?text=Connection+Error" # Connection error placeholder | |
| def recommend(movie): | |
| """ | |
| Recommend movies based on similarity matrix and fetch posters using the IMDb API. | |
| Args: | |
| movie (str): The name of the movie for recommendations. | |
| Returns: | |
| list, list: Lists of recommended movie names and their poster URLs. | |
| """ | |
| try: | |
| movies_index = movies[movies["title"] == movie].index[0] | |
| distances = similarity[movies_index] | |
| movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6] | |
| recommended_movies = [] | |
| recommended_movies_posters = [] | |
| for i in movie_list: | |
| recommended_movies.append(movies.iloc[i[0]].title) | |
| # Fetch poster and handle any connection or data issues | |
| poster = fetch_poster(movies.iloc[i[0]].title) | |
| recommended_movies_posters.append(poster) | |
| return recommended_movies, recommended_movies_posters | |
| except IndexError: | |
| st.error("The movie is not found in the dataset.") | |
| return [], [] | |
| # Load movie data and similarity matrix from pickled files | |
| 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") | |
| # Movie selection dropdown | |
| selected_movie_name = st.selectbox( | |
| "Select a movie to get recommendations:", movies['title'].values | |
| ) | |
| # Display recommendations upon button click | |
| if st.button("Recommend"): | |
| names, posters = recommend(selected_movie_name) | |
| if names and posters and len(names) >= 5: | |
| 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]) | |
| else: | |
| st.write("No recommendations found for the selected movie or unable to fetch posters.") | |