from spice import Spice

client = Spice()

messages: List[SpiceMessage] = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "list 5 random words"},
]
response = await client.get_response(messages=messages, model="gpt-4-0125-preview")

print(response.text)

# You can set a default model for the client instead of passing it with each call
client = Spice(default_text_model="claude-3-opus-20240229")

# You can easily load prompts from files, directories, or even urls.
client.load_prompt("prompt.txt", name="my prompt")

# Spice can also automatically render Jinja templates.
messages: List[SpiceMessage] = [
    {"role": "system", "content": client.get_rendered_prompt("my prompt", assistant_name="Ryan Reynolds")},
    {"role": "user", "content": "list 5 random words"},
]
stream = await client.stream_response(messages=messages)

async for text in stream:
    print(text, end="", flush=True)
# Retrieve the complete response from the stream
response = await stream.complete_response()

# Response always includes the final text, no need build it from the stream yourself
print(response.text)

# Response also includes helpful stats
print(f"Took {response.total_time:.2f}s")
print(f"Input/Output tokens: {response.input_tokens}/{response.output_tokens}")

# Commonly used models and providers have premade constants
from spice.models import GPT_4_0125_PREVIEW

# Alias models for easy configuration, even mixing providers
model_aliases = {
    "task1_model": GPT_4_0125_PREVIEW,
    "task2_model": "claude-3-opus-20240229",
    "task3_model": "claude-3-haiku-20240307",
}

client = Spice(model_aliases=model_aliases)

messages: List[SpiceMessage] = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "list 5 random words"},
]
responses = await asyncio.gather(
    client.get_response(messages=messages, model="task1_model"),
    client.get_response(messages=messages, model="task2_model"),
    client.get_response(messages=messages, model="task3_model"),
)

for i, response in enumerate(responses, 1):
    print(f"\nModel {i} response:")
    print(response.text)
    print(f"Characters per second: {response.characters_per_second:.2f}")
    if response.cost is not None:
        print(f"Cost: ${response.cost / 100:.4f}")

# Spice also tracks the total cost over multiple models and providers
print(f"Total Cost: ${client.total_cost / 100:.4f}")

client = Spice()

messages: List[SpiceMessage] = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "list 5 random words"},
]

# To use Azure, specify the provider and the deployment model name
response = await client.get_response(messages=messages, model="first-gpt35", provider="azure")
print(response.text)

# Alternatively, to make a model and it's provider known to Spice, create a custom Model object
from spice.models import TextModel
from spice.providers import AZURE

AZURE_GPT = TextModel("first-gpt35", AZURE, context_length=16385)
response = await client.get_response(messages=messages, model=AZURE_GPT)
print(response.text)

# Creating the model automatically registers it in Spice's model list, so listing the provider is no longer needed
response = await client.get_response(messages=messages, model="first-gpt35")
print(response.text)

client = Spice()

# Spice makes it easy to add images from files or the internet
from spice.spice_message import file_image_message, user_message

messages: List[SpiceMessage] = [user_message("What do you see?"), file_image_message("/path/to/image.png")]
response = await client.get_response(messages, GPT_4_1106_VISION_PREVIEW)
print(response.text)

# Alternatively, you can use the SpiceMessages wrapper to easily create your prompts
spice_messages: SpiceMessages = SpiceMessages(client)
spice_messages.add_user_message("What do you see?")
spice_messages.add_file_image_message("https://example.com/image.png")
response = await client.get_response(spice_messages, CLAUDE_3_OPUS_20240229)
print(response.text)

client = Spice()
input_texts = ["Once upon a time...", "Cinderella"]

# Spice can easily fetch embeddings and audio transcriptions
from spice.models import TEXT_EMBEDDING_ADA_002, WHISPER_1

embeddings = await client.get_embeddings(input_texts, TEXT_EMBEDDING_ADA_002)
transcription = await client.get_transcription("/path/to/audio/file", WHISPER_1)
print(transcription.text)