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Explore Code Generation Using DeepSeek Coder Models

Last reviewed: 9 months ago

A handy way to explore all of the models available on Workers AI is to use a Jupyter Notebook.

You can download the DeepSeek Coder notebook or view the embedded notebook below.

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Exploring Code Generation Using DeepSeek Coder

AI Models being able to generate code unlocks all sorts of use cases. The DeepSeek Coder models @hf/thebloke/deepseek-coder-6.7b-base-awq and @hf/thebloke/deepseek-coder-6.7b-instruct-awq are now available on Workers AI.

Let’s explore them using the API!

import sys
!{sys.executable} -m pip install requests python-dotenv
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Requirement already satisfied: idna<4,>=2.5 in ./venv/lib/python3.12/site-packages (from requests) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in ./venv/lib/python3.12/site-packages (from requests) (2.1.0)
Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.12/site-packages (from requests) (2023.11.17)
import os
from getpass import getpass
from IPython.display import display, Image, Markdown, Audio
import requests
%load_ext dotenv
%dotenv

Configuring your environment

To use the API you’ll need your Cloudflare Account ID (head to Workers & Pages > Overview > Account details > Account ID) and a Workers AI enabled API Token.

If you want to add these files to your environment, you can create a new file named .env

Terminal window
CLOUDFLARE_API_TOKEN="YOUR-TOKEN"
CLOUDFLARE_ACCOUNT_ID="YOUR-ACCOUNT-ID"
if "CLOUDFLARE_API_TOKEN" in os.environ:
api_token = os.environ["CLOUDFLARE_API_TOKEN"]
else:
api_token = getpass("Enter you Cloudflare API Token")
if "CLOUDFLARE_ACCOUNT_ID" in os.environ:
account_id = os.environ["CLOUDFLARE_ACCOUNT_ID"]
else:
account_id = getpass("Enter your account id")

Generate code from a comment

A common use case is to complete the code for the user after they provide a descriptive comment.

model = "@hf/thebloke/deepseek-coder-6.7b-base-awq"
prompt = "# A function that checks if a given word is a palindrome"
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",
headers={"Authorization": f"Bearer {api_token}"},
json={"messages": [
{"role": "user", "content": prompt}
]}
)
inference = response.json()
code = inference["result"]["response"]
display(Markdown(f"""
```python
{prompt}
{code.strip()}
```
"""))
# A function that checks if a given word is a palindrome
def is_palindrome(word):
# Convert the word to lowercase
word = word.lower()
# Reverse the word
reversed_word = word[::-1]
# Check if the reversed word is the same as the original word
if word == reversed_word:
return True
else:
return False
# Test the function
print(is_palindrome("racecar")) # Output: True
print(is_palindrome("hello")) # Output: False

Assist in debugging

We’ve all been there, bugs happen. Sometimes those stacktraces can be very intimidating, and a great use case of using Code Generation is to assist in explaining the problem.

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"
system_message = "The user is going to give you code that isn't working. Explain to the user what might be wrong"
code = """# Welcomes our user
def hello_world(first_name="World"):
print(f"Hello, {name}!")
"""
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",
headers={"Authorization": f"Bearer {api_token}"},
json={"messages": [
{"role": "system", "content": system_message},
{"role": "user", "content": code},
]}
)
inference = response.json()
response = inference["result"]["response"]
display(Markdown(response))

The error in your code is that you are trying to use a variable name which is not defined anywhere in your function. The correct variable to use is first_name. So, you should change f"Hello, {name}!" to f"Hello, {first_name}!".

Here is the corrected code:

# Welcomes our user
def hello_world(first_name="World"):
print(f"Hello, {first_name}")

Now, when you call hello_world(), it will print “Hello, World” by default. If you call hello_world("John"), it will print “Hello, John”.

Write tests!

Writing unit tests is a common best practice. With the enough context, it’s possible to write unit tests.

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"
system_message = "The user is going to give you code and would like to have tests written in the Python unittest module."
code = """
class User:
def __init__(self, first_name, last_name=None):
self.first_name = first_name
self.last_name = last_name
if last_name is None:
self.last_name = "Mc" + self.first_name
def full_name(self):
return self.first_name + " " + self.last_name
"""
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",
headers={"Authorization": f"Bearer {api_token}"},
json={"messages": [
{"role": "system", "content": system_message},
{"role": "user", "content": code},
]}
)
inference = response.json()
response = inference["result"]["response"]
display(Markdown(response))

Here is a simple unittest test case for the User class:

import unittest
class TestUser(unittest.TestCase):
def test_full_name(self):
user = User("John", "Doe")
self.assertEqual(user.full_name(), "John Doe")
def test_default_last_name(self):
user = User("Jane")
self.assertEqual(user.full_name(), "Jane McJane")
if __name__ == '__main__':
unittest.main()

In this test case, we have two tests:

  • test_full_name tests the full_name method when the user has both a first name and a last name.
  • test_default_last_name tests the full_name method when the user only has a first name and the last name is set to “Mc” + first name.

If all these tests pass, it means that the full_name method is working as expected. If any of these tests fail, it

Fill-in-the-middle Code Completion

A common use case in Developer Tools is to autocomplete based on context. DeepSeek Coder provides the ability to submit existing code with a placeholder, so that the model can complete in context.

Warning: The tokens are prefixed with <| and suffixed with |> make sure to copy and paste them.

model = "@hf/thebloke/deepseek-coder-6.7b-base-awq"
code = """
<|fim▁begin|>import re
from jklol import email_service
def send_email(email_address, body):
<|fim▁hole|>
if not is_valid_email:
raise InvalidEmailAddress(email_address)
return email_service.send(email_address, body)<|fim▁end|>
"""
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",
headers={"Authorization": f"Bearer {api_token}"},
json={"messages": [
{"role": "user", "content": code}
]}
)
inference = response.json()
response = inference["result"]["response"]
display(Markdown(f"""
```python
{response.strip()}
```
"""))
is_valid_email = re.match(r"[^@]+@[^@]+\.[^@]+", email_address)

Experimental: Extract data into JSON

No need to threaten the model or bring grandma into the prompt. Get back JSON in the format you want.

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"
# Learn more at https://json-schema.org/
json_schema = """
{
"title": "User",
"description": "A user from our example app",
"type": "object",
"properties": {
"firstName": {
"description": "The user's first name",
"type": "string"
},
"lastName": {
"description": "The user's last name",
"type": "string"
},
"numKids": {
"description": "Amount of children the user has currently",
"type": "integer"
},
"interests": {
"description": "A list of what the user has shown interest in",
"type": "array",
"items": {
"type": "string"
}
},
},
"required": [ "firstName" ]
}
"""
system_prompt = f"""
The user is going to discuss themselves and you should create a JSON object from their description to match the json schema below.
<BEGIN JSON SCHEMA>
{json_schema}
<END JSON SCHEMA>
Return JSON only. Do not explain or provide usage examples.
"""
prompt = """Hey there, I'm Craig Dennis and I'm a Developer Educator at Cloudflare. My email is craig@cloudflare.com.
I am very interested in AI. I've got two kids. I love tacos, burritos, and all things Cloudflare"""
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",
headers={"Authorization": f"Bearer {api_token}"},
json={"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
]}
)
inference = response.json()
response = inference["result"]["response"]
display(Markdown(f"""
```json
{response.strip()}
```
"""))
{
"firstName": "Craig",
"lastName": "Dennis",
"numKids": 2,
"interests": ["AI", "Cloudflare", "Tacos", "Burritos"]
}