Create short text content with PaLM API and Streamlit.

• Docker
• PaLM API support. The code expects that your location is supported by the PaLM API (check here), it is also expected that you have a working PaLM API key in the .env file at the project root.

This app supports both Docker and Python execution, Docker is the recommended one, and you can use the commands below to use the features. If you would like to use the Python environment execution, please take a look at the Makefile and Dockerfile files and reproduce those commands.

LLMs generate text guided by prompts provided to them, usually, for sophisticated outputs, we also provide some context as part of the prompt. In this case, our prompt context guides the model to generate short content. You can find that context at the app.py defined by the CONTEXT variable, this is the default one:

Explain the topic of "{}" in a short text format that is targeted at the general public.\n
The tone of the text should be informative and easy to understand with some examples.

Feel free to experiment with it as you like.

model: "models/text-bison-001"
temperature: 0.5
candidate_count: 3
top_k: 40
top_p: 0.95
max_output_tokens: 1024
stop_sequences: []
safety_settings: [
    {"category": "HARM_CATEGORY_DEROGATORY", "threshold": 4},
    {"category": "HARM_CATEGORY_TOXICITY", "threshold": 4},
    {"category": "HARM_CATEGORY_VIOLENCE", "threshold": 4},
    {"category": "HARM_CATEGORY_SEXUAL", "threshold": 4},
    {"category": "HARM_CATEGORY_MEDICAL", "threshold": 4},
    {"category": "HARM_CATEGORY_DANGEROUS", "threshold": 4},
]

• model: Model queried by the PaLM API.
• candidate_count: Number of outputs generated, if you change this, you might need to update the UI at the app.py file.
• max_output_tokens: Max number of output tokens generated by the LLM.
• stop_sequences: If provided, the model will stop generating text after this value.
• safety_settings: Various parameters to configure safety restrictions on the outputs.
• temperature, top_k and top_p: Control the LLM's behavior, there is a good explanation for them here.

Build Docker image

make build

Start Streamlit app

make app

Run code linter and formatter

make lint

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• Streamlit