moVstudiocode documentation¶
Documentation goes here Movie recommendation code -This code gives out reccomendations for a movie =====
Change the movie_ids_to_query and movie_id to a certain number to find the movie for that movie id, and it will find similar movies
All movies come from The Movie Database (tmdb)
Movie recommendation code
This code gives out reccomendations for a movie
Change the movie_ids_to_query and movie_id to a certain number to find the movie for that movie id, and it will find similar movies
All movies come from The Movie Database (tmdb)
Ratings are based on vote average- the average rating from users on the site
Can add multiple movie ids to find listings for multiple movies
##Functions:
async main - finds the movies and displays their titles, ids, and ratings as a list
“”” moVstudiocode.py ==================================== This code gives out movie reccomendations
“””
import asyncio from tmdb import route, schema from dacite import from_dict import requests import json import matplotlib.pyplot as plt import networkx as nx
- async def main():
“”” Gets the movies from the online movie database and finds similar movies Parameters ———- movie_ids_to_query: list[int]
the id of the movie you want recommendations for
- movie_similarity_data: dict
the title and id of movies considered similar to the chosen movie, used for making the graph
“””
base = route.Base() base.key = “4ec39598d34ba6c46351241dcf930713” #The API key to access the movie database movie_ids_to_query = [777] #the movie you want to use
movie_id = 777 URL = f”https://api.themoviedb.org/3/movie/{movie_id}/similar”
params = { ‘api_key’: “4ec39598d34ba6c46351241dcf930713”, ‘language’: ‘en-US’, ‘page’: 1 }
response = requests.get(URL, params=params) movie_similarity_data = {}
- for movie_id in movie_ids_to_query:
# Fetch movie title movie_details_url = f”https://api.themoviedb.org/3/movie/{movie_id}” details_response = requests.get(movie_details_url, params=params)
movie_title = “Unknown” if details_response.status_code == 200:
movie_details = details_response.json() movie_title = movie_details.get(‘title’, ‘Unknown’) movie_rating = movie_details.get(‘vote_average’)
- else:
print(f”Error fetching details for movie ID {movie_id}: {details_response.status_code}”) continue
# Fetch similar movies similar_response = requests.get(URL, params=params)
- if similar_response.status_code == 200:
data = similar_response.json() similar_movies_list = [
{‘id’: m[‘id’], ‘title’: m[‘title’], ‘rating’: m[‘vote_average’]} for m in data[‘results’]
] movie_similarity_data[movie_id] = {
“title”: movie_title, “similar_movies”: similar_movies_list, “vote_average”: movie_rating
}
- else:
print(f”Error fetching similar movies for movie ID {movie_id}: {similar_response.status_code}”)
if response.status_code == 200:
data = response.json() movie = movie_id print(f”The movie you chose is: {movie_title}”) print(f”Similar movies to {movie_title}:”) for movie in data[‘results’]:
print(f”* {movie[‘title’]} (ID: {movie[‘id’]}) Rating:{movie[‘vote_average’]}”)
- else:
print(f”Error: {response.status_code}”)
return movie_similarity_data
- if __name__ == “__main__”:
“”” This part of the code runs the main function and builds a graph with the data collected
- collected data: dict
data collected from the main function about movie titles and ids
“”” print(“Finding similar movies”)
# Run the async function and get the result collected_data = asyncio.run(main())
# This is where the collected data is availible
# Build the NetworkX graph graph = nx.Graph()
- for movie_id, data in collected_data.items():
title = data[‘title’] graph.add_node(movie_id, title=title)
- for sim in data[‘similar_movies’]:
sim_id = sim[‘id’] sim_title = sim[‘title’]
- if not graph.has_node(sim_id):
graph.add_node(sim_id, title=sim_title)
# You can add weights later if you want (e.g., similarity score) graph.add_edge(movie_id, sim_id)
print(f”nGraph of all the recommended movies:”) print(f”Number of nodes: {graph.number_of_nodes()}”) print(f”Number of edges: {graph.number_of_edges()}”)
# Visualize plt.figure(figsize=(16, 12)) pos = nx.spring_layout(graph, k=0.5, iterations=50) # better layout
- nx.draw_networkx(
graph, pos=pos, with_labels=True, labels={n: f”{n}n{d[‘title’]}” for n, d in graph.nodes(data=True)}, node_size=2000, node_color=’red’, font_size=9, font_weight=’light’, edge_color=’gray’, alpha=0.9
)
plt.title(‘Movie Similarity Network (TMDB Similar Movies)’, size=18) plt.axis(‘off’) plt.tight_layout() plt.show()