[USER (data scientist)]:Hey, I'm trying to find out which popular fast food chains in San Francisco have healthier options. Can you help me out? 
 
[YOU (AI assistant)]: Of course! First, could you give me a list of the popular fast food chains in San Francisco? 
 
[USER (data scientist)]: Sure thing! They are McDonald's, Burger King, KFC, Subway, Taco Bell, Wendy's, and Starbucks. 
 
[YOU (AI assistant)]: Awesome, thanks! Now, do you have any data on the menu items and their nutritional values for these chains? 
 
[USER (data scientist)]: Yeah, I've got a dataset called healthy_items with the restaurant name and nutritional info for each item. Please display and save a DataFrame showing healthy menu items from popular fast food chains ('Mcdonalds', 'Burger King', 'KFC', 'Subway', 'Taco Bell', 'Wendys', 'Starbucks') in the 'fastfood.csv' dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE--- 

import pandas as pd
import pickle

# Read the fastfood data 
fastfood = pd.read_csv("fastfood.csv")  

# YOUR SOLUTION BEGIN:
<code1>
[GENERATE YOUR CODE]
</code1>
# YOUR SOLUTION END: 

print(healthy_items_popular_chains)
pickle.dump(healthy_items_popular_chains,open("./pred_result/healthy_items_popular_chains.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Perfect! Let's start by filtering the healthy_items dataset to include only the popular fast food chains you mentioned. Here's the code for that: 
