utils.py

import os
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from joblib import dump, load
from sklearn.preprocessing import normalize
import re

def get_next_version(file_prefix, folder='RecommendationFiles/'):
    """Find the latest version of a file and return the next version's filename."""
    if not os.path.exists(folder):
        os.makedirs(folder)  # Ensure the folder exists

    # Regular expression to match files like 'file_0001.joblib'
    pattern = re.compile(rf"{file_prefix}_(\d+)\.joblib")
    files = [f for f in os.listdir(folder) if pattern.match(f)]
    
    # Extract version numbers from matching files
    versions = [int(pattern.match(f).group(1)) for f in files]
    
    # Determine the next version number
    next_version = max(versions) + 1 if versions else 1
    
    # Return the next version filename with the folder path
    return os.path.join(folder, f"{file_prefix}_{next_version:04d}.joblib")

def get_latest_version(file_prefix, folder='RecommendationFiles/'):
    """Find the latest version of a file to load."""
    if not os.path.exists(folder):
        raise FileNotFoundError(f"Folder '{folder}' does not exist")
    
    # Regular expression to match files like 'file_0001.joblib'
    pattern = re.compile(rf"{file_prefix}_(\d+)\.joblib")
    files = [f for f in os.listdir(folder) if pattern.match(f)]
    
    # Extract version numbers from matching files
    versions = [int(pattern.match(f).group(1)) for f in files]
    
    if versions:
        latest_version = max(versions)
        return os.path.join(folder, f"{file_prefix}_{latest_version:04d}.joblib")
    else:
        raise FileNotFoundError(f"No versions found for {file_prefix} in folder '{folder}'")


def recomienda_tf(new_basket, cestas, productos): 

    # Get the latest versions of the matrix and vectorizer from the folder
    tf_matrix_file = get_latest_version('count_matrix')
    count_vectorizer_file = get_latest_version('count_vectorizer')
    
    # Load the matrix TF and the vectorizer
    tf_matrix = load(tf_matrix_file)
    count = load(count_vectorizer_file)
                    
    # Convert the new basket into TF (Term Frequency) format
    new_basket_str = ' '.join(new_basket)
    new_basket_vector = count.transform([new_basket_str])
    new_basket_tf = normalize(new_basket_vector, norm='l1')  # Normalize the count matrix for the current basket

    # Compare the new basket with previous ones
    similarities = cosine_similarity(new_basket_tf, tf_matrix)
    
    # Get the indices of the most similar baskets
    similar_indices = similarities.argsort()[0][-4:]  # Top 4 most similar baskets
    
    # Create a dictionary to count recommendations
    recommendations_count = {}
    total_similarity = 0
    
    # Recommend products from similar baskets
    for idx in similar_indices:
        sim_score = similarities[0][idx]
        total_similarity += sim_score  # Sum of similarities
        products = cestas.iloc[idx]['Cestas'].split()
        
        unique_products = set(products)  # Use a set to get unique products
        
        for product in unique_products:
            if product.strip() not in new_basket:  # Avoid recommending items already in the basket
                recommendations_count[product.strip()] = recommendations_count.get(product.strip(), 0) + sim_score
    
    # Calculate the relative probability of each recommended product
    recommendations_with_prob = []
    if total_similarity > 0:
        recommendations_with_prob = [(product, score / total_similarity) for product, score in recommendations_count.items()]
    else:
        print("No se encontraron similitudes suficientes para calcular probabilidades.")
     
    # Sort recommendations by relevance score
    recommendations_with_prob.sort(key=lambda x: x[1], reverse=True)
    
    # Create a new DataFrame to store recommendations
    recommendations_data = []
    
    for product, score in recommendations_with_prob:
        # Search for the product description in the products DataFrame
        description = productos.loc[productos['ARTICULO'] == product, 'DESCRIPCION']
        if not description.empty:
            recommendations_data.append({
                'ARTICULO': product,
                'DESCRIPCION': description.values[0],
                'RELEVANCIA': score
            })
    recommendations_df = pd.DataFrame(recommendations_data)
    
    return recommendations_df.head(5)

def retroalimentacion(cestas, cesta_nueva):
    # Convert basket from list to string
    cesta_unida = ' '.join(cesta_nueva)
    
    # Add the new basket to the historical baskets if it doesn't already exist
    if not cestas['Cestas'].isin([cesta_unida]).any():
        cestas.loc[len(cestas)] = cesta_unida
        print("Cesta añadida.")
        
        # Re-save the updated baskets DataFrame
        cestas.to_csv('RecommendationFiles/cestas_final.csv', index=False)
    else:
        print("La cesta ya existe en el DataFrame.")
    
    # Re-vectorize the basket DataFrame
    count_vectorizer = CountVectorizer()
    count_vectorizer.fit(cestas['Cestas'])
    count_matrix = count_vectorizer.transform(cestas['Cestas'])
    tf_matrix = normalize(count_matrix, norm='l1')

    # Save new versions of the vectorizer and matrix
    count_vectorizer_file = get_next_version('count_vectorizer')
    tf_matrix_file = get_next_version('tf_matrix')
    
    dump(count_vectorizer, count_vectorizer_file)
    dump(tf_matrix, tf_matrix_file)

    return None