app.py

import gradio as gr
import pandas as pd
import numpy as np
from sklearn.decomposition import TruncatedSVD
import time
from model import MatrixFactorization, create_gradio_interface

try:
    # Load the preprocessed data
    print("Loading data...")
    df = pd.read_csv('data.csv')
    
    # Initialize and train the model
    print("Initializing model...")
    mf_recommender = MatrixFactorization(n_factors=50)  # Reduced for speed
    mf_recommender.fit(df)
    
    # Create and launch the Gradio interface
    print("Creating interface...")
    demo = create_gradio_interface(mf_recommender)
    demo.launch(share=True)
except Exception as e:
    print(f"Error: {str(e)}")