updated app with movies names and genre

app.py

@@ -1,89 +1,89 @@
-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 get_movie_id(title):
-    movie = movies[movies['title'].str.contains(title, case=False, na=False)]
-    if not movie.empty:
-        return movie.iloc[0]['movieId'], movie.iloc[0]['title'], movie.iloc[0]['genres']
-    return None, None, None
-
-def recommend_movies(movie_title, rating, latent_dim=10, lambda_param=0.1, tau=0.1):
-    movie_id, title, genres = get_movie_id(movie_title)
-    if movie_id is None:
-        return f"Movie '{movie_title}' not found."
-
-    # Map the dummy movie id to its index
-    dummy_movie_index = map_movie_to_idx[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]
-
-    # Format the output as HTML
-    html_output = "<div style='font-family: Arial, sans-serif;'>"
-    html_output += f"<div style='margin-bottom: 20px;'><strong>Selected Movie:</strong><br>Title: {title}<br>Genres: {genres}</div>"
-    html_output += "<div style='margin-bottom: 20px;'><strong>Recommended Movies:</strong></div>"
-    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.Textbox(label="Movie Title"),
-        gr.Slider(0, 5, step=0.1, label="Rating")
-    ],
-    outputs="html",
-    title="Movie Recommendation System",
-    description="""
-    Enter a movie title and rating to get top 10 movie recommendations. 
-    I made this demo as part of a machine learning class taught by Prof. Ulich Paquet at AIMS South Africa.
-    Here are some sample movies you can try out of a database of over 62,000 movies:
-    - Toy Story
-    - Incredibles
-    - Lord of the rings
-    - Star Wars
-    - Terminator
-    """
-)
-
+import gradio as gr
+import pickle
+import numpy as np
+import pandas as pd
+
+# model weights pickled after training from scratch
+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 get_movie_id(title):
+    movie = movies[movies['title'].str.contains(title, case=False, na=False)]
+    if not movie.empty:
+        return movie.iloc[0]['movieId'], movie.iloc[0]['title'], movie.iloc[0]['genres']
+    return None, None, None
+
+def recommend_movies(movie_title, rating, latent_dim=10, lambda_param=0.1, tau=0.1):
+    movie_id, title, genres = get_movie_id(movie_title)
+    if movie_id is None:
+        return f"Movie '{movie_title}' not found."
+
+    # Map the dummy movie id to its index
+    dummy_movie_index = map_movie_to_idx[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]
+
+    # Format the output as HTML
+    html_output = "<div style='font-family: Arial, sans-serif;'>"
+    html_output += f"<div style='margin-bottom: 20px;'><strong>Selected Movie:</strong><br>Title: {title}<br>Genres: {genres}</div>"
+    html_output += "<div style='margin-bottom: 20px;'><strong>Recommended Movies:</strong></div>"
+    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.Textbox(label="Movie Title"),
+        gr.Slider(0, 5, step=0.1, label="Rating")
+    ],
+    outputs="html",
+    title="Movie Recommendation System",
+    description="""
+    Enter a movie title and rating to get top 10 movie recommendations. 
+    I made this demo as part of a machine learning class taught by Prof. Ulich Paquet at AIMS South Africa.
+    Here are some sample movies you can try out of a database of over 62,000 movies:
+    - Toy Story
+    - Incredibles
+    - Lord of the rings
+    - Star Wars
+    - Terminator
+    """
+)
+
 iface.launch(share=True)