from phi_3_vision_mlx import generate

generate('What is shown in this image?', 'https://collectionapi.metmuseum.org/api/collection/v1/iiif/344291/725918/main-image')

prompts = [
    "Explain the key concepts of quantum computing and provide a Rust code example demonstrating quantum superposition.",
    "Write a poem about the first snowfall of the year.",
    "Summarize the major events of the French Revolution.",
    "Describe a bustling alien marketplace on a distant planet with unique goods and creatures."
    "Implement a basic encryption algorithm in Python.",
]

# `Phi-3-Vision
generate(prompts, max_tokens=100)
# `Phi-3-Mini-128K
generate(prompts, max_tokens=100, blind_model=True)

# `Model quantization
generate("Explain the implications of quantum entanglement in quantum computing.", quantize_model=True)
# `Cache quantization
generate("Describe the potential applications of CRISPR gene editing in medicine.", quantize_cache=True)

from phi_3_vision_mlx import train_lora

train_lora(lora_layers=5, lora_rank=16, epochs=10, lr=1e-4, warmup=.5, mask_ratios=[.0], adapter_path='adapters', dataset_path = "JosefAlbers/akemiH_MedQA_Reason")

generate("Write a cosmic horror.", adapter_path='adapters')

from phi_3_vision_mlx import Agent

agent = Agent()
agent('Analyze this image and describe the architectural style:', 'https://images.metmuseum.org/CRDImages/rl/original/DP-19531-075.jpg')
agent('What historical period does this architecture likely belong to?')
agent.end()

agent('Plot a Lissajous Curve.')
agent('Modify the code to plot 3:4 frequency')
agent.end()

agent('Draw "A perfectly red apple, 32k HDR, studio lighting"')
agent.end()
agent('Speak "People say nothing is impossible, but I do nothing every day."')
agent.end()

from phi_3_vision_mlx import load_text

# Create tool
def add_text(prompt):
    prompt, path = prompt.split('@')
    return f'{load_text(path)}\n<|end|>\n<|user|>{prompt}'

# Chain tools
toolchain = """
    prompt = add_text(prompt)
    responses = generate(prompt, images)
    """

# Create agent
agent = Agent(toolchain, early_stop=100)

# Run agent
agent('How to inspect API endpoints? @https://raw.githubusercontent.com/gradio-app/gradio/main/guides/08_gradio-clients-and-lite/01_getting-started-with-the-python-client.md')

from phi_3_vision_mlx import VDB
import datasets

# User proxy
user_input = 'Comparison of Sortino Ratio for Bitcoin and Ethereum.'

# Create tool
def rag(prompt, repo_id="JosefAlbers/sharegpt_python_mlx", n_topk=1):
    ds = datasets.load_dataset(repo_id, split='train')
    vdb = VDB(ds)
    context = vdb(prompt, n_topk)[0][0]
    return f'{context}\n<|end|>\n<|user|>Plot: {prompt}'

# Chain tools
toolchain_plot = """
    prompt = rag(prompt)
    responses = generate(prompt, images)
    files = execute(responses, step)
    """

# Create agent
agent = Agent(toolchain_plot, False)

# Run agent
_, images = agent(user_input)

# Continued from Example 2
agent_writer = Agent(early_stop=100)
agent_writer(f'Write a stock analysis report on: {user_input}', images)

from phi_3_vision_mlx import benchmark

benchmark()