Spaces:
Sleeping
Sleeping
Antony-gitau
commited on
Commit
·
6640c83
1
Parent(s):
4276044
initial deployment
Browse files- app.py +71 -0
- final_02_model_biases_factors.pkl +3 -0
- map_idx_to_movie.pkl +3 -0
- map_movie_to_idx.pkl +3 -0
- movies.csv +0 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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import pickle
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import numpy as np
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import pandas as pd
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# Load your model and data
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with open('final_02_model_biases_factors.pkl', 'rb') as file:
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model_state = pickle.load(file)
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user_bias = model_state["user_bias"]
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movie_bias = model_state["movie_bias"]
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user_factors = model_state["user_factors"]
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movie_factors = model_state["movie_factors"]
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movies = pd.read_csv("movies.csv")
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with open('map_movie_to_idx.pkl', 'rb') as file:
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map_movie_to_idx = pickle.load(file)
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with open('map_idx_to_movie.pkl', 'rb') as file:
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map_idx_to_movie = pickle.load(file)
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def recommend_movies(dummy_movie_id, rating, latent_dim=10, lambda_param=0.1, tau=0.1):
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# Map the dummy movie id to its index
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dummy_movie_index = map_movie_to_idx[dummy_movie_id]
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# Get the movie factors and bias for the dummy movie
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dummy_movie_factors = movie_factors[:, dummy_movie_index]
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dummy_movie_bias = movie_bias[dummy_movie_index]
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# Initialize dummy user factors
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dummy_user_factors = np.zeros(latent_dim)
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# Calculate the dummy user factor
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dummy_user_factor = np.linalg.inv(lambda_param * np.outer(dummy_movie_factors, dummy_movie_factors) +
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tau * np.eye(latent_dim)) @ (lambda_param * dummy_movie_factors * (rating - dummy_movie_bias))
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# Calculate the score for each movie
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score = dummy_user_factor @ movie_factors + 0.05 * movie_bias
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# Get the top 10 recommendations
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recommend_movies_indices = np.argsort(score)[-10:]
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# Map the indices back to movie titles
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recommended_movies = [movies.loc[movies["movieId"] == map_idx_to_movie[movie]] for movie in recommend_movies_indices]
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# return recommended_movies
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# Format the output as HTML
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html_output = "<div style='font-family: Arial, sans-serif;'>"
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for i, movie in enumerate(recommended_movies):
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title = movie["title"].values[0]
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genres = movie["genres"].values[0]
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html_output += f"<div style='margin-bottom: 10px;'><strong>{i+1}. {title}</strong><br>Genres: {genres}</div>"
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html_output += "</div>"
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return html_output
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# Define the Gradio interface
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iface = gr.Interface(
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fn=recommend_movies,
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inputs=[
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gr.Number(label="Movie ID"),
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gr.Slider(0, 5, step=0.1, label="Rating")
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],
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outputs="html",
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title="Movie Recommendation System",
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description="Enter a movie ID and rating to get top 10 movie recommendations."
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)
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iface.launch(share=True)
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final_02_model_biases_factors.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:0d3b60725c32f7aefef716ffdff2d5570747c365b46d7270b4b1cc6d2af0c199
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size 19500750
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map_idx_to_movie.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:d5f6ec3b73ab6d76843caadd26f1b99616aef2cec935fe9d71b57e10cb1695d3
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size 1122266
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map_movie_to_idx.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:320c8f1d8c625c0f9449271101e2ab7bbecd061ae4eca24c767148c523bb4ee1
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size 1299169
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movies.csv
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The diff for this file is too large to render.
See raw diff
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requirements.txt
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gradio
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pandas
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numpy
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