roject Story

With the announcement of OpenAI, GPT's performance was further improved, and the GPT-4-Vision model for image capturing was introduced. Recently, many attempts have been made with the vision model, so I gave it a try. I thought it would be interesting to do a project utilizing my knowledge of AI and Prompt, and I was curious to see how well it could perform tasks that may be somewhat subjective, such as cooking meat.

Project flow

Recognize and store images with descriptions of meat (using GPT-4-vision)

• Recognize an image with a description of meat using GPT-4-vision. Store the recognized information in context.

Executability on the Streamlit web server

• The image recognition and storage process mentioned above can be executed on the Streamlit web server. User submission and recognition of images via Streamlit
• A user submits a photo through Streamlit. The submitted photo is recognized with GPT-4-vision, referring to the context and few-shot prompt created in the first step of the process.

Using Raspberry Pi and W5100S-EVB-PICO

• The above process runs on a Raspberry Pi.
• The W6100-EVB-PICO is used as a temperature sensor to measure the actual temperature of the meat and send this data to the Raspberry Pi. TCP Client server is used in this process.
• Based on the temperature data, the Raspberry Pi displays the programmed degree of doneness of the meat.

User questions and meat status checks

• The user can inquire about the doneness of the meat by asking a question. After the system answers the user's question and confirms that the meat is cooked, the user can consume the meat.

Source Code(S/W)

I've uploaded the code based on the code I studied to use Vision, but since there is a langchain library for LLM, I've also uploaded the langchain method for more efficient code refactoring.

function.py is the source code that defines the functions used and is written as a library, while app.py contains the code that runs Streamlit and uses the UI GPT.

!git clone <project>
streamlit run app.py

file and run it. Make sure to set the openAI api key to your own.

Prompt

mainprompt.txt

[instruction]
- You're a meat connoisseur, you're the best chef to know when meat is done, you're like Gordon Ramsay.
- {base64_image} to tell me when the meat is done.
- Make sure to take note of the {context}.
[output]
- doneness = "well done", "medium rare" , "rare", "raw"
- You should use your own judgment for the doneness of the meat. Make sure to output
- output the overall condition of the meat. In short
- Output format: 
ex) "I'm Gordon Ramsay Simon, a meat expert from the USA". According to your photo, the doneness of the meat is {meat doneness}. Enjoy your meal. 

Be sure to follow [instruction], [output] to output in Korean.

Systemprompt.txt

[instruction]
You recognize the doneness of the meat and related information from the image the other person sends you.

[output]
You describe the information you analyzed as best you can in text.

Source Code(H/W)

The above SW code is executed on Raspberry Pi, and we built an independent server by building a web server and separating the server from pico to ensure reliability.

W6100-evb-pico <-> main webserver(Streamlit)