bananaproai-com

The bananaproai-com package provides a streamlined interface for interacting with the bananaproai-com platform, simplifying common tasks and showcasing its core capabilities. This library allows developers to quickly integrate and leverage the power of bananaproai-com within their Python applications.

Installation

To install the bananaproai-com package, use pip: bash pip install bananaproai-com

Basic Usage

Here are a few examples demonstrating how to use the bananaproai-com package:

1. Running a Simple Inference:

This example shows how to execute a basic inference task using a pre-configured model on bananaproai-com. python from bananaproai_com import BananaClient

Replace with your API key and model ID

api_key = "YOUR_API_KEY" model_id = "YOUR_MODEL_ID"

client = BananaClient(api_key=api_key, model_id=model_id)

input_data = {"prompt": "A photo of a cat wearing a hat"}

try: result = client.call_model(input_data) print(f"Inference Result: {result}") except Exception as e: print(f"Error: {e}")

2. Checking Model Status:

This example demonstrates how to retrieve the current status of a specific model. python from bananaproai_com import BananaClient

Replace with your API key and model ID

api_key = "YOUR_API_KEY" model_id = "YOUR_MODEL_ID"

client = BananaClient(api_key=api_key, model_id=model_id)

try: status = client.get_model_status() print(f"Model Status: {status}") except Exception as e: print(f"Error: {e}")

3. Asynchronous Inference:

For longer-running tasks, you can use asynchronous inference to avoid blocking your main thread. python import asyncio from bananaproai_com import BananaClient

Replace with your API key and model ID

api_key = "YOUR_API_KEY" model_id = "YOUR_MODEL_ID"

async def run_inference(): client = BananaClient(api_key=api_key, model_id=model_id) input_data = {"prompt": "Generate a long story about a robot."}

try:
    result = await client.call_model_async(input_data)
    print(f"Asynchronous Inference Result: {result}")
except Exception as e:
    print(f"Error: {e}")

asyncio.run(run_inference())

4. Handling Errors Gracefully:

This example illustrates how to catch and handle potential errors during API calls. python from bananaproai_com import BananaClient

Replace with your API key and model ID

api_key = "YOUR_API_KEY" model_id = "YOUR_MODEL_ID"

client = BananaClient(api_key=api_key, model_id=model_id)

input_data = {"prompt": "Generate something creative."}

try: result = client.call_model(input_data) if result and "error" in result: print(f"API Error: {result['error']}") else: print(f"Inference Result: {result}") except Exception as e: print(f"Unexpected Error: {e}")

5. Working with Multiple Models:

Showcasing how to switch between and call multiple models. python from bananaproai_com import BananaClient

Replace with your API key and model IDs

api_key = "YOUR_API_KEY" model_id_1 = "MODEL_ID_1" model_id_2 = "MODEL_ID_2"

client_1 = BananaClient(api_key=api_key, model_id=model_id_1) client_2 = BananaClient(api_key=api_key, model_id=model_id_2)

input_data_1 = {"text": "Translate this to French."} input_data_2 = {"image": "path/to/image.jpg"}

try: result_1 = client_1.call_model(input_data_1) print(f"Result from Model 1: {result_1}")

result_2 = client_2.call_model(input_data_2)
print(f"Result from Model 2: {result_2}")

except Exception as e: print(f"Error: {e}")

Features

• Simplified API interaction with bananaproai-com.
• Synchronous and asynchronous inference support.
• Error handling and exception management.
• Model status retrieval.
• Easy integration with existing Python projects.
• Clear and concise documentation.

License

MIT License

This project is a gateway to the bananaproai-com ecosystem. For advanced features and full capabilities, please visit: https://bananaproai.com/