pypollinations 0.2.0

Python client for Pollinations AI API

pollinations-python

A Python wrapper for accessing Pollinations AI API endpoints.

Installation

pip install pypollinations

API documentation

API documentation

Usage

Image Generation

Code Example

import asyncio
from pypollinations import ImageClient, ImageGenerationRequest
from PIL import Image
from io import BytesIO

Client Setup and Image Generation

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

        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

        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

Text Generation

Basic Setup

import asyncio
from pypollinations import TextClient, TextGenerationRequest
from pypollinations.models.base import Message
from pypollinations.exceptions import PollinationsError

Text Generation Implementation

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

        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

        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

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.