# Set your API key
client = OpenAI(api_key="YOUR API KEY")

def ChatGPT_API(prompt):
  
# Create a request to the chat completions endpoint
  response = client.chat.completions.create(
    model="gpt-4o",
    
# Assign the role and content for the message
    messages=[{"role": "user", "content": prompt}], 
    temperature = 0) 
# Temperature: Controls the randomness of the response (0 is deterministic, 2 is highly random).
  return response.choices[0].message.content

# Specify the function with your prompt
response = ChatGPT_API("Specify your prompt here") 
# Example: "What is Artifical Intelligence?"
print(response)

import docx2txt
 
# read in word file
text = docx2txt.process("Path/to/your/file.docx")

# Craft a prompt to transform the text for writing improvement
prompt = f"""Proofread the text delimited by triple backticks without changing its structure.
 ```{text}```"""

# Using the fuction we defined earlier.
response = ChatGPT_API(prompt)

print("Before transformation:\n", text)
print("After transformation:\n", response)

code = """
income_split <- initial_split(income_frac, prop = 0.80, strata = income)
income_train <- training(income_split)
income_test <- testing(income_split)
"""

# Use a chain-of-thought prompt that asks the model to explain what the code does
prompt = f"""Explain what the code delimited by triple backticks does. Let's think step by step.```{code}```"""

response = ChatGPT_API(prompt)
print(response)

Sure, let's break down the code step by step to understand what it does.  
  
#
## Step 1: `initial_split`  
```R  
income_split <- initial_split(income_frac, prop = 0.80, strata = income)  

#
# Using Multimodality in the API

#
## Speech-to-Text Transcription with Whisper

Audio recordings or videos can be transformed into text using OpenAI's model `Whisper`. Using `Whisper` for transcription can be beneficial for a student or teacher. Whisper saves time by automating the transcription process, instead of manually transcribing lengthy audio recordings. It can be used in practice in the following ways:

1. **Note-taking during lectures:**Transcribe online lectures, allowing you to focus on understanding the content instead of taking extensive notes. Teachers can also benefit from this feature when recording their lectures or presentations.
2. **Improved documentation and follow-up during meetings:** Transcribing meetings allows you to have a detailed and accurate written record of the meeting. This makes it easier for you to track action items, decisions, and key points discussed during the meeting. This can lead to efficient follow-up, ensuring that tasks and responsibilities are properly documented and assigned. It can also be beneficial for you if you missed a meeting and can easily read back the transcripts.

> **Example**  
> The template supports audio files up to 25 MB. For larger files, check the following tutorial on our [website](https://tilburg.ai/2024/01/whisper-ai-transcription/)!

Python

Again just copy your code, and here there are 2 steps you need to take to have your transcription. First, enter your `api_key` and second, load in your file `audio_file` and that's it! *Those transcribing days are finally over ; )*

#
## From Audio to Transcript to Summary to PowerPoint in one Go

So far, when we've been using the OpenAI API, we have specified an input file or prompt to the model and received an output. This opens up a lot of opportunities, from asking a question and getting an answer to transcribing an audio or video file. But what if we could do more with the model's output?

Enter `model chaining`. **Chaining** is when models are combined by feeding the output from one model directly into another model as an input. We can chain multiple calls to the same model together or use different models. If we chain two text models together, we can ask the model to perform a task in one call to the API and send it back with an additional instruction. We can also combine two different types of models, like the `Whisper` model and a `text model`, to perform tasks like summarizing lectures and videos and converting them into a PowerPoint slide deck.

For the code below, all you need to do is the same two-step procedure: specify your `API key` and your `file`, and that's it!

> **Info**  
> In this instance, we have used a specific example of prompt engineering: `multi-step-prompting`. This approach breaking down a complex task into smaller, manageable steps, with each step incrementally contributing to the final desired outcome. By doing so, we can tackle complex or here problems that requires several transformations more efficiently and systematically. Naturally, you are encouraged to adjust the prompt for your specific needs!

Python

[Output](https://tilburgaigeneral.blob.core.windows.net/tilly-testing/images/812301b4-585a-4233-af5c-39dab3711bf1/f28cb5dfea14.txt)[Download](https://tilburgaigeneral.blob.core.windows.net/tilly-testing/images/812301b4-585a-4233-af5c-39dab3711bf1/f28cb5dfea14.txt)

Wow, in exactly 36.58 seconds:

- We have transcribed a video file and extracted the text.
- This text has been proofread, checked for spelling errors, and corrected.
- If the video had been in a different language than English, it would have been translated.
- After that, the video has been summarised.
- Finally, a PowerPoint slide deck has been created.

#
# Conclusion

In this article, we take a careful, *providing templates so non-coding users can use them*, but deep, *the template highlight how to use the API effectively,* dive into the unknown waters of the OpenAI API. Along the way, we also touch on prompt engineering with `chain-of-thought` and `multi-step prompting`. Our advice:

- Copy and paste the code, add your API key, and save it as a file.
- If you ever need it, grab the template code, add your prompt and your file, and press run code.