# MNNAI
This repository contains an example of how to use the mnnai library.
## Prerequisites
- Python 3.x
- MNNAI library installed. You can install it using pip:
```bash
pip install mnnai
```
## Usage
**Non-Streaming Chat**
```python
from mnnai import MNN
client = MNN(
key='MNN API KEY' # This is the default and can be omitted
)
chat_completion = client.chat.create(
messages=[
{
"role": "user",
"content": "What's the weather like in New York?",
}
],
model="gpt-4o-mini",
web_search=True # Internet search
)
print(chat_completion.choices[0].message.content)
```
**Streaming Chat**
```python
stream = client.chat.create(
messages=[
{
"role": "user",
"content": "Will the neural networks capture the world?",
}
],
model="gpt-4o-mini",
stream=True
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
```
**Image Generation**
```python
import base64
import os
response = client.images.create(
prompt="Draw a cute red panda",
model='dall-e-3'
)
image_base64 = response.data[0].url
os.makedirs('images', exist_ok=True)
for i, image_base64 in enumerate(image_base64):
image_data = base64.b64decode(image_base64)
with open(f'images/image_{i}.png', 'wb') as f:
f.write(image_data)
print("Images have been successfully downloaded!")
```
## Async usage
**Non-Streaming Chat**
```python
import asyncio
async def main():
chat_completion = await client.chat.async_create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-4o-mini",
)
print(chat_completion.choices[0].message.content)
asyncio.run(main())
```
**Streaming Chat**
```python
import asyncio
async def main():
stream = await client.chat.async_create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
async for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
asyncio.run(main())
```
**Image Generation**
```python
import asyncio
import base64
import os
async def main():
response = await client.images.async_create(
prompt="Draw a cute red panda",
model='dall-e-3'
)
image_base64 = response.data[0].url
os.makedirs('images', exist_ok=True)
for i, image_base64 in enumerate(image_base64):
image_data = base64.b64decode(image_base64)
with open(f'images/image_{i}.png', 'wb') as f:
f.write(image_data)
print("Images have been successfully downloaded!")
asyncio.run(main())
```
## Auxiliary functions
**Get models**
```python
print(client.GetModels())
```
**Configuring the client**
```python
from mnnai import MNN
client = MNN(
key='MNN API KEY',
max_retries=2, # Number of retries in case of failure
timeout=60, # Maximum amount of time the request will be processed
debug=True # Whether the application needs to be debugged
)
```
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## Discord
https://discord.gg/Ku2haNjFvj
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"description": "# MNNAI\n\nThis repository contains an example of how to use the mnnai library.\n\n## Prerequisites\n\n- Python 3.x\n- MNNAI library installed. You can install it using pip:\n\n```bash\npip install mnnai\n```\n\n## Usage\n\n**Non-Streaming Chat**\n\n```python\nfrom mnnai import MNN\n\nclient = MNN(\n key='MNN API KEY' # This is the default and can be omitted\n)\n\nchat_completion = client.chat.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"What's the weather like in New York?\",\n }\n ],\n model=\"gpt-4o-mini\",\n web_search=True # Internet search\n)\nprint(chat_completion.choices[0].message.content)\n```\n\n**Streaming Chat**\n\n```python\nstream = client.chat.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Will the neural networks capture the world?\",\n }\n ],\n model=\"gpt-4o-mini\",\n stream=True\n)\n\nfor chunk in stream:\n print(chunk.choices[0].delta.content or \"\", end=\"\")\n```\n\n**Image Generation**\n\n```python\nimport base64\nimport os\n\nresponse = client.images.create(\n prompt=\"Draw a cute red panda\",\n model='dall-e-3'\n)\n\nimage_base64 = response.data[0].url\n\nos.makedirs('images', exist_ok=True)\n\nfor i, image_base64 in enumerate(image_base64):\n image_data = base64.b64decode(image_base64)\n\n with open(f'images/image_{i}.png', 'wb') as f:\n f.write(image_data)\n\nprint(\"Images have been successfully downloaded!\")\n```\n\n## Async usage\n\n**Non-Streaming Chat**\n\n```python\nimport asyncio\n\nasync def main():\n chat_completion = await client.chat.async_create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Say this is a test\",\n }\n ],\n model=\"gpt-4o-mini\",\n )\n print(chat_completion.choices[0].message.content)\n\n\nasyncio.run(main())\n```\n\n**Streaming Chat**\n\n```python\nimport asyncio\n\nasync def main():\n stream = await client.chat.async_create(\n model=\"gpt-4o-mini\",\n messages=[{\"role\": \"user\", \"content\": \"Say this is a test\"}],\n stream=True,\n )\n async for chunk in stream:\n print(chunk.choices[0].delta.content or \"\", end=\"\")\n\n\nasyncio.run(main())\n```\n\n**Image Generation**\n\n```python\nimport asyncio\nimport base64\nimport os\n\nasync def main():\n response = await client.images.async_create(\n prompt=\"Draw a cute red panda\",\n model='dall-e-3'\n )\n\n image_base64 = response.data[0].url\n\n os.makedirs('images', exist_ok=True)\n\n for i, image_base64 in enumerate(image_base64):\n image_data = base64.b64decode(image_base64)\n\n with open(f'images/image_{i}.png', 'wb') as f:\n f.write(image_data)\n\n print(\"Images have been successfully downloaded!\")\n\n\nasyncio.run(main())\n```\n\n## Auxiliary functions \n\n**Get models**\n\n```python\nprint(client.GetModels())\n```\n\n**Configuring the client**\n\n```python\nfrom mnnai import MNN\n\nclient = MNN(\n key='MNN API KEY',\n max_retries=2, # Number of retries in case of failure\n timeout=60, # Maximum amount of time the request will be processed\n debug=True # Whether the application needs to be debugged\n)\n```\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n## Discord \nhttps://discord.gg/Ku2haNjFvj\n",
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