initial deployment

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+import pandas as pd
+
+
+# Load your model and data
+with open('final_02_model_biases_factors.pkl', 'rb') as file:
+    model_state = pickle.load(file)
+
+user_bias = model_state["user_bias"]
+movie_bias = model_state["movie_bias"]
+user_factors = model_state["user_factors"]
+movie_factors = model_state["movie_factors"]
+movies = pd.read_csv("movies.csv")
+
+with open('map_movie_to_idx.pkl', 'rb') as file:
+    map_movie_to_idx = pickle.load(file)
+
+with open('map_idx_to_movie.pkl', 'rb') as file:
+    map_idx_to_movie = pickle.load(file)
+
+def recommend_movies(dummy_movie_id, rating, latent_dim=10, lambda_param=0.1, tau=0.1):
+    # Map the dummy movie id to its index
+    dummy_movie_index = map_movie_to_idx[dummy_movie_id]
+
+    # Get the movie factors and bias for the dummy movie
+    dummy_movie_factors = movie_factors[:, dummy_movie_index]
+    dummy_movie_bias = movie_bias[dummy_movie_index]
+
+    # Initialize dummy user factors
+    dummy_user_factors = np.zeros(latent_dim)
+
+    # Calculate the dummy user factor
+    dummy_user_factor = np.linalg.inv(lambda_param * np.outer(dummy_movie_factors, dummy_movie_factors) +
+                                      tau * np.eye(latent_dim)) @ (lambda_param * dummy_movie_factors * (rating - dummy_movie_bias))
+
+    # Calculate the score for each movie
+    score = dummy_user_factor @ movie_factors + 0.05 * movie_bias
+
+    # Get the top 10 recommendations
+    recommend_movies_indices = np.argsort(score)[-10:]
+
+    # Map the indices back to movie titles
+    recommended_movies = [movies.loc[movies["movieId"] == map_idx_to_movie[movie]] for movie in recommend_movies_indices]
+
+    # return recommended_movies
+
+    # Format the output as HTML
+    html_output = "<div style='font-family: Arial, sans-serif;'>"
+    for i, movie in enumerate(recommended_movies):
+        title = movie["title"].values[0]
+        genres = movie["genres"].values[0]
+        html_output += f"<div style='margin-bottom: 10px;'><strong>{i+1}. {title}</strong><br>Genres: {genres}</div>"
+    html_output += "</div>"
+
+    return html_output
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=recommend_movies,
+    inputs=[
+        gr.Number(label="Movie ID"),
+        gr.Slider(0, 5, step=0.1, label="Rating")
+    ],
+    outputs="html",
+    title="Movie Recommendation System",
+    description="Enter a movie ID and rating to get top 10 movie recommendations."
+)
+
+iface.launch(share=True)

final_02_model_biases_factors.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d3b60725c32f7aefef716ffdff2d5570747c365b46d7270b4b1cc6d2af0c199
+size 19500750

map_idx_to_movie.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5f6ec3b73ab6d76843caadd26f1b99616aef2cec935fe9d71b57e10cb1695d3
+size 1122266

map_movie_to_idx.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:320c8f1d8c625c0f9449271101e2ab7bbecd061ae4eca24c767148c523bb4ee1
+size 1299169

requirements.txt

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+gradio
+pandas
+numpy