| Name | pypollinations JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Python client for Pollinations AI API |
| upload_time | 2024-12-14 20:12:08 |
| maintainer | None |
| docs_url | None |
| author | Krishnatejaswi S |
| requires_python | <4.0,>=3.10 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pollinations-python
A Python wrapper for accessing Pollinations AI API endpoints.
## Installation
```bash
pip install pypollinations
```
## API documentation
[API documentation](docs/api.md)
## Usage
### Image Generation
#### Code Example
```python
import asyncio
from pypollinations import ImageClient, ImageGenerationRequest
from PIL import Image
from io import BytesIO
```
#### Client Setup and Image Generation
```python
async def generate_image(
save_image_path: str = "./examples/generated_images/",
image_name: str = "image.png"
):
client = ImageClient()
try:
request = ImageGenerationRequest(
prompt="A beautiful sunset over mountains with snow peaks",
width=1024,
height=768,
model="flux",
nologo=True,
)
response = await client.generate(request)
print(f"Image URL: {response.url}")
print(f"Seed: {response.seed}")
```
#### Image Saving
```python
image_data = response.image_bytes
try:
image_data = Image.open(BytesIO(image_data))
image_data.save(save_image_path + image_name)
print(f"Image saved to {save_image_path}")
except Exception as e:
print(f"Error: {e}")
```
#### Model Listing and Main Execution
```python
models = await client.list_models()
print("\nAvailable models:")
print("\n".join(models))
except Exception as e:
print(f"Error: {e}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(generate_image())
```
> Output
> [](./examples/generated_images/image.png)
### Text Generation
#### Basic Setup
```python
import asyncio
from pypollinations import TextClient, TextGenerationRequest
from pypollinations.models.base import Message
from pypollinations.exceptions import PollinationsError
```
#### Text Generation Implementation
```python
async def generate_text():
client = TextClient()
try:
request = TextGenerationRequest(
messages=[Message(role="user", content="What is artificial intelligence?")],
model="openai",
jsonMode=True,
seed=41,
temperature=0.5,
frequency_penalty=0.0,
presence_penalty=0.0,
top_p=1.0,
system="You are a helpful AI assistant.",
)
```
#### Response Handling
```python
print("Generating response...\n")
try:
response = await client.generate(request)
print(f"Response: {response.content}")
print(f"Model: {response.model}")
print(f"Seed: {response.seed}")
print(f"Temperature: {response.temperature}")
print(f"Frequency penalty: {response.frequency_penalty}")
print(f"Presence penalty: {response.presence_penalty}")
print(f"Top p: {response.top_p}")
```
#### Error Handling and Model Listing
```python
except Exception as e:
print(f"Failed to generate response: {e}")
raise
print("\nFetching available models...")
try:
models = await client.list_models()
print("\nAvailable models:")
for model in models:
print(f"- {model['name']}: {model.get('type', 'unknown')}")
except Exception as e:
print(f"Failed to fetch models: {e}")
except PollinationsError as e:
print(f"API Error: {e}")
except Exception as e:
print(f"Unexpected error: {type(e).__name__}: {e}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(generate_text())
```
> Output
```text
Generating response...
Response: Artificial Intelligence (AI) is a broad field of computer science dedicated to creating smart machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. Here are some key aspects of AI:
1. **Machine Learning (ML)**: A subset of AI that involves training algorithms to learn from data, make predictions, or improve performance over time.
2. **Deep Learning (DL)**: A subset of machine learning that uses neural networks with many layers to analyze and classify data, often used for tasks like image and speech recognition.
3. **Natural Language Processing (NLP)**: A branch of AI focused on enabling computers to understand, interpret, and generate human language.
4. **Computer Vision**: A field of AI that deals with enabling computers to interpret and understand the visual world, often using data from cameras and sensors.
5. **Robotics**: AI is used to develop robots that can perform tasks autonomously or with guidance, often incorporating computer vision and other AI subfields.
6. **Expert Systems**: These are AI systems that use knowledge and inference rules to provide explanations or make decisions in specific domains.
AI has a wide range of applications, from voice assistants like Siri and Alexa to self-driving cars, medical diagnosis, fraud detection, and more. The goal of AI is to augment and enhance human capabilities, automate routine tasks, and solve complex problems efficiently.
Model: openai
Seed: 41
Temperature: 0.5
Frequency penalty: 0.0
Presence penalty: 0.0
Top p: 1.0
Fetching available models...
Available models:
- openai: chat
- mistral: chat
- mistral-large: chat
- llama: completion
- command-r: chat
- unity: chat
- midijourney: chat
- rtist: chat
- searchgpt: chat
- evil: chat
- qwen-coder: chat
- p1: chat
```
## Features
- Easy integration with Pollinations AI services
- Support for various AI models
- Asynchronous requests support
## License
This project is licensed under the [Apache License 2.0](LICENSE).
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"description": "# pollinations-python\n\nA Python wrapper for accessing Pollinations AI API endpoints.\n\n## Installation\n\n```bash\npip install pypollinations\n```\n\n## API documentation\n\n[API documentation](docs/api.md)\n\n## Usage\n\n### Image Generation\n\n#### Code Example\n```python\nimport asyncio\nfrom pypollinations import ImageClient, ImageGenerationRequest\nfrom PIL import Image\nfrom io import BytesIO\n```\n\n#### Client Setup and Image Generation\n```python\nasync def generate_image(\n save_image_path: str = \"./examples/generated_images/\", \n image_name: str = \"image.png\"\n):\n client = ImageClient()\n try:\n request = ImageGenerationRequest(\n prompt=\"A beautiful sunset over mountains with snow peaks\",\n width=1024,\n height=768,\n model=\"flux\",\n nologo=True,\n )\n response = await client.generate(request)\n print(f\"Image URL: {response.url}\")\n print(f\"Seed: {response.seed}\")\n```\n\n#### Image Saving\n```python\n image_data = response.image_bytes\n try:\n image_data = Image.open(BytesIO(image_data))\n image_data.save(save_image_path + image_name)\n print(f\"Image saved to {save_image_path}\")\n except Exception as e:\n print(f\"Error: {e}\")\n```\n\n#### Model Listing and Main Execution\n```python\n models = await client.list_models()\n print(\"\\nAvailable models:\")\n print(\"\\n\".join(models))\n\n except Exception as e:\n print(f\"Error: {e}\")\n finally:\n await client.close()\n\nif __name__ == \"__main__\":\n asyncio.run(generate_image())\n```\n\n> Output\n> [](./examples/generated_images/image.png)\n\n### Text Generation\n\n#### Basic Setup\n```python\nimport asyncio\nfrom pypollinations import TextClient, TextGenerationRequest\nfrom pypollinations.models.base import Message\nfrom pypollinations.exceptions import PollinationsError\n```\n\n#### Text Generation Implementation\n```python\nasync def generate_text():\n client = TextClient()\n try:\n request = TextGenerationRequest(\n messages=[Message(role=\"user\", content=\"What is artificial intelligence?\")],\n model=\"openai\",\n jsonMode=True,\n seed=41,\n temperature=0.5,\n frequency_penalty=0.0,\n presence_penalty=0.0,\n top_p=1.0,\n system=\"You are a helpful AI assistant.\",\n )\n```\n\n#### Response Handling\n```python\n print(\"Generating response...\\n\")\n try:\n response = await client.generate(request)\n print(f\"Response: {response.content}\")\n print(f\"Model: {response.model}\")\n print(f\"Seed: {response.seed}\")\n print(f\"Temperature: {response.temperature}\")\n print(f\"Frequency penalty: {response.frequency_penalty}\")\n print(f\"Presence penalty: {response.presence_penalty}\")\n print(f\"Top p: {response.top_p}\")\n```\n\n#### Error Handling and Model Listing\n```python\n except Exception as e:\n print(f\"Failed to generate response: {e}\")\n raise\n\n print(\"\\nFetching available models...\")\n try:\n models = await client.list_models()\n print(\"\\nAvailable models:\")\n for model in models:\n print(f\"- {model['name']}: {model.get('type', 'unknown')}\")\n except Exception as e:\n print(f\"Failed to fetch models: {e}\")\n\n except PollinationsError as e:\n print(f\"API Error: {e}\")\n except Exception as e:\n print(f\"Unexpected error: {type(e).__name__}: {e}\")\n finally:\n await client.close()\n\nif __name__ == \"__main__\":\n asyncio.run(generate_text())\n```\n\n> Output\n\n```text\nGenerating response...\n\nResponse: Artificial Intelligence (AI) is a broad field of computer science dedicated to creating smart machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. Here are some key aspects of AI:\n\n1. **Machine Learning (ML)**: A subset of AI that involves training algorithms to learn from data, make predictions, or improve performance over time.\n\n2. **Deep Learning (DL)**: A subset of machine learning that uses neural networks with many layers to analyze and classify data, often used for tasks like image and speech recognition.\n\n3. **Natural Language Processing (NLP)**: A branch of AI focused on enabling computers to understand, interpret, and generate human language.\n\n4. **Computer Vision**: A field of AI that deals with enabling computers to interpret and understand the visual world, often using data from cameras and sensors.\n\n5. **Robotics**: AI is used to develop robots that can perform tasks autonomously or with guidance, often incorporating computer vision and other AI subfields.\n\n6. **Expert Systems**: These are AI systems that use knowledge and inference rules to provide explanations or make decisions in specific domains.\n\nAI has a wide range of applications, from voice assistants like Siri and Alexa to self-driving cars, medical diagnosis, fraud detection, and more. The goal of AI is to augment and enhance human capabilities, automate routine tasks, and solve complex problems efficiently.\nModel: openai\nSeed: 41\nTemperature: 0.5\nFrequency penalty: 0.0\nPresence penalty: 0.0\nTop p: 1.0\n\nFetching available models...\n\nAvailable models:\n- openai: chat\n- mistral: chat\n- mistral-large: chat\n- llama: completion\n- command-r: chat\n- unity: chat\n- midijourney: chat\n- rtist: chat\n- searchgpt: chat\n- evil: chat\n- qwen-coder: chat\n- p1: chat\n```\n\n## Features\n\n- Easy integration with Pollinations AI services\n- Support for various AI models\n- Asynchronous requests support\n\n## License\n\nThis project is licensed under the [Apache License 2.0](LICENSE).\n",
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