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<article id="content">
<header>
<h1 class="title">Module <code>praisonai.deploy</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="praisonai.deploy.CloudDeployer"><code class="flex name class">
<span>class <span class="ident">CloudDeployer</span></span>
</code></dt>
<dd>
<div class="desc"><p>A class for deploying a cloud-based application.</p>
<h2 id="attributes">Attributes</h2>
<p>None</p>
<h2 id="methods">Methods</h2>
<p><strong>init</strong>(self):
Loads environment variables from .env file or system and sets them.</p>
<p>Loads environment variables from .env file or system and sets them.</p>
<h2 id="parameters">Parameters</h2>
<p>self: An instance of the CloudDeployer class.</p>
<h2 id="returns">Returns</h2>
<p>None</p>
<h2 id="raises">Raises</h2>
<p>None</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CloudDeployer:
"""
A class for deploying a cloud-based application.
Attributes:
None
Methods:
__init__(self):
Loads environment variables from .env file or system and sets them.
"""
def __init__(self):
"""
Loads environment variables from .env file or system and sets them.
Parameters:
self: An instance of the CloudDeployer class.
Returns:
None
Raises:
None
"""
# Load environment variables from .env file or system
load_dotenv()
self.set_environment_variables()
def create_dockerfile(self):
"""
Creates a Dockerfile for the application.
Parameters:
self: An instance of the CloudDeployer class.
Returns:
None
Raises:
None
This method creates a Dockerfile in the current directory with the specified content.
The Dockerfile is used to build a Docker image for the application.
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
set the working directory to /app, copy the current directory contents into the container,
install the required Python packages (flask, praisonai, gunicorn, and markdown),
expose port 8080, and run the application using Gunicorn.
"""
with open("Dockerfile", "w") as file:
file.write("FROM python:3.11-slim\n")
file.write("WORKDIR /app\n")
file.write("COPY . .\n")
file.write("RUN pip install flask praisonai==2.1.1 gunicorn markdown\n")
file.write("EXPOSE 8080\n")
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
def create_api_file(self):
"""
Creates an API file for the application.
Parameters:
self (CloudDeployer): An instance of the CloudDeployer class.
Returns:
None
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
"""
with open("api.py", "w") as file:
file.write("from flask import Flask\n")
file.write("from praisonai import PraisonAI\n")
file.write("import markdown\n\n")
file.write("app = Flask(__name__)\n\n")
file.write("def basic():\n")
file.write(" praisonai = PraisonAI(agent_file=\"agents.yaml\")\n")
file.write(" return praisonai.run()\n\n")
file.write("@app.route('/')\n")
file.write("def home():\n")
file.write(" output = basic()\n")
file.write(" html_output = markdown.markdown(output)\n")
file.write(" return f'<html><body>{html_output}</body></html>'\n\n")
file.write("if __name__ == \"__main__\":\n")
file.write(" app.run(debug=True)\n")
def set_environment_variables(self):
"""Sets environment variables with fallback to .env values or defaults."""
os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
def run_commands(self):
"""
Sets environment variables with fallback to .env values or defaults.
Parameters:
None
Returns:
None
Raises:
None
This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
- `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
- `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
- `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
"""
self.create_api_file()
self.create_dockerfile()
"""Runs a sequence of shell commands for deployment, continues on error."""
commands = [
"yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
"gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
"docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
"docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
"docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
"gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
]
for cmd in commands:
try:
subprocess.run(cmd, shell=True, check=True)
except subprocess.CalledProcessError as e:
print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
print(f"Continuing with the next command...")</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="praisonai.deploy.CloudDeployer.create_api_file"><code class="name flex">
<span>def <span class="ident">create_api_file</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Creates an API file for the application.</p>
<h2 id="parameters">Parameters</h2>
<p>self (CloudDeployer): An instance of the CloudDeployer class.</p>
<h2 id="returns">Returns</h2>
<p>None
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.</p></div>
</dd>
<dt id="praisonai.deploy.CloudDeployer.create_dockerfile"><code class="name flex">
<span>def <span class="ident">create_dockerfile</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Creates a Dockerfile for the application.</p>
<h2 id="parameters">Parameters</h2>
<p>self: An instance of the CloudDeployer class.</p>
<h2 id="returns">Returns</h2>
<p>None</p>
<h2 id="raises">Raises</h2>
<p>None
This method creates a Dockerfile in the current directory with the specified content.
The Dockerfile is used to build a Docker image for the application.
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
set the working directory to /app, copy the current directory contents into the container,
install the required Python packages (flask, praisonai, gunicorn, and markdown),
expose port 8080, and run the application using Gunicorn.</p></div>
</dd>
<dt id="praisonai.deploy.CloudDeployer.run_commands"><code class="name flex">
<span>def <span class="ident">run_commands</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p>
<h2 id="parameters">Parameters</h2>
<p>None</p>
<h2 id="returns">Returns</h2>
<p>None</p>
<h2 id="raises">Raises</h2>
<p>None
This method sets environment variables for the application. It uses the <code>os.environ</code> dictionary to set the following environment variables:</p>
<ul>
<li><code>OPENAI_MODEL_NAME</code>: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".</li>
<li><code>OPENAI_API_KEY</code>: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".</li>
<li><code>OPENAI_API_BASE</code>: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".</li>
</ul></div>
</dd>
<dt id="praisonai.deploy.CloudDeployer.set_environment_variables"><code class="name flex">
<span>def <span class="ident">set_environment_variables</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p></div>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="praisonai.deploy.CloudDeployer" href="#praisonai.deploy.CloudDeployer">CloudDeployer</a></code></h4>
<ul class="">
<li><code><a title="praisonai.deploy.CloudDeployer.create_api_file" href="#praisonai.deploy.CloudDeployer.create_api_file">create_api_file</a></code></li>
<li><code><a title="praisonai.deploy.CloudDeployer.create_dockerfile" href="#praisonai.deploy.CloudDeployer.create_dockerfile">create_dockerfile</a></code></li>
<li><code><a title="praisonai.deploy.CloudDeployer.run_commands" href="#praisonai.deploy.CloudDeployer.run_commands">run_commands</a></code></li>
<li><code><a title="praisonai.deploy.CloudDeployer.set_environment_variables" href="#praisonai.deploy.CloudDeployer.set_environment_variables">set_environment_variables</a></code></li>
</ul>
</li>
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</li>
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