Spaces:
Sleeping
Sleeping
File size: 1,841 Bytes
dd81387 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.feature_extraction.text import CountVectorizer
from typing import List, Dict
import os
from utils.config import Config
# Load the dataset (replace with the actual path to your dataset)
dataset_path = Config.read('app', 'dataset')
# Ensure the dataset exists
if not os.path.exists(dataset_path):
raise FileNotFoundError(f"The dataset file at {dataset_path} was not found.")
# Load the dataset
data = pd.read_pickle(dataset_path)
# Ensure the dataset has the necessary columns: 'asin', 'title', 'brand', 'medium_image_url'
required_columns = ['asin', 'title', 'brand', 'medium_image_url']
for col in required_columns:
if col not in data.columns:
raise ValueError(f"Missing required column: {col} in the dataset")
# Set up the vectorizer and fit the model
title_vectorizer = CountVectorizer()
title_features = title_vectorizer.fit_transform(data['title'])
# Function to calculate the bag-of-words model and return closest matches
def bag_of_words_model(query: str, num_results: int) -> List[Dict]:
# Transform the input query to the same feature space
query_vec = title_vectorizer.transform([query])
# Calculate pairwise distances between the query and all items in the corpus
pairwise_dist = pairwise_distances(title_features, query_vec, metric='cosine')
# Get the indices of the closest matches
indices = np.argsort(pairwise_dist.flatten())[0:num_results]
results = []
for idx in indices:
result = {
'asin': data['asin'].iloc[idx],
'brand': data['brand'].iloc[idx],
'title': data['title'].iloc[idx],
'url': data['medium_image_url'].iloc[idx],
}
results.append(result)
return results
|