import pandas as pd
from transformers import pipeline
Load and preprocess data
For demonstration purposes, let's assume we have a CSV with component data
components_df = pd.read_csv('pc_components.csv')
Function to filter components based on budget and preferences
def filter_components(budget, preferences):
# Apply filtering logic here
compatible_components = components_df[
(components_df['price'] <= budget) &
(components_df['category'].isin(preferences['categories']))
]
return compatible_components
Define user preferences and budget
user_budget = 1000 # Example budget
user_preferences = {
'categories': ['CPU', 'GPU', 'RAM', 'Motherboard']
}
Get compatible components
selected_components = filter_components(user_budget, user_preferences)
Display the selected components
print(selected_components)
Example Hugging Face pipeline for recommendation (conceptual)
You would need to train a model suitable for your specific recommendation needs
recommender = pipeline('text-classification', model='your-trained-model')
Generate recommendations
recommendations = recommender("Recommend PC components within the budget")
print(recommendations)
