A local version of WebSim.AI, the prompt to webpage engine. Infinite possibilities to cure your boredom. ( I made it so you dont have to.... Youll thank me later)
Environment Variables for Initialization:
I recommend using environment variables to initialize the following parameters when using lm-studio:
API_URL: The URL for the API you’re interacting with.
API_KEY: Your API key for authentication.
model: The specific model you want to use.
Listening on 0.0.0.0:
To make the Flask application listen on all available network interfaces, set the host to 0.0.0.0 in your Flask app configuration.
Docker release:
build the Dockerfile and use CI/CD to make the docker image release.
recommended Dockerfile
# Use the official Python image as the base
FROM python:3.9
# Set environment variable for API_URL (default value)
ENV LMSTUDIO_URL="http://localhost:1234/v1"
ENV LMSTUDIO_KEY="lm-studio"
ENV LMSTUDIO_MODEL="model-identifier"
# Set working directory
WORKDIR /app
# Copy requirements file and install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy your Python code into the container
COPY . .
# Expose port 5000 (assuming your app runs on this port)
EXPOSE 5000
# Command to run your app
CMD ["python", "main.py", "lmstudio"]
Environment Variables for Initialization: I recommend using environment variables to initialize the following parameters when using lm-studio: API_URL: The URL for the API you’re interacting with. API_KEY: Your API key for authentication. model: The specific model you want to use.
Listening on 0.0.0.0: To make the Flask application listen on all available network interfaces, set the host to 0.0.0.0 in your Flask app configuration.
Docker release: build the Dockerfile and use CI/CD to make the docker image release.
recommended Dockerfile