# Installation
! pip install smolagents
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/smolagents.git

from smolagents import CodeAgent, InferenceClientModel

model_id = "meta-llama/Llama-3.3-70B-Instruct"

model = InferenceClientModel(model_id=model_id, token="<YOUR_HUGGINGFACEHUB_API_TOKEN>")
agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
    "Could you give me the 118th number in the Fibonacci sequence?",
)

# !pip install 'smolagents[transformers]'
from smolagents import CodeAgent, TransformersModel

model_id = "meta-llama/Llama-3.2-3B-Instruct"

model = TransformersModel(model_id=model_id)
agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
    "Could you give me the 118th number in the Fibonacci sequence?",
)

# !pip install 'smolagents[litellm]'
from smolagents import CodeAgent, LiteLLMModel

model = LiteLLMModel(model_id="anthropic/claude-3-5-sonnet-latest", api_key="YOUR_ANTHROPIC_API_KEY") # 也可以使用 'gpt-4o'
agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
    "Could you give me the 118th number in the Fibonacci sequence?",
)

# !pip install 'smolagents[litellm]'
from smolagents import CodeAgent, LiteLLMModel

model = LiteLLMModel(
    model_id="ollama_chat/llama3.2", # 这个模型对于 agent 行为来说有点弱
    api_base="http://localhost:11434", # 如果需要可以替换为远程 open-ai 兼容服务器
    api_key="YOUR_API_KEY" # 如果需要可以替换为 API key
    num_ctx=8192 # https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator
)

agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
    "Could you give me the 118th number in the Fibonacci sequence?",
)

# !pip install 'smolagents[openai]'
from smolagents import CodeAgent, AzureOpenAIServerModel

model = AzureOpenAIServerModel(model_id="gpt-4o-mini")
agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
    "Could you give me the 118th number in the Fibonacci sequence?",
)

import os
from smolagents import CodeAgent, LiteLLMModel

AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-35-turbo-16k-deployment" # example of deployment name

os.environ["AZURE_API_KEY"] = "" # api_key
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2024-10-01-preview"

model = LiteLLMModel(model_id="azure/" + AZURE_OPENAI_CHAT_DEPLOYMENT_NAME)
agent = CodeAgent(tools=[], model=model, add_base_tools=True)

agent.run(
   "Could you give me the 118th number in the Fibonacci sequence?",
)

# !pip install 'smolagents[mlx-lm]'
from smolagents import CodeAgent, MLXModel

mlx_model = MLXModel("mlx-community/Qwen2.5-Coder-32B-Instruct-4bit")
agent = CodeAgent(model=mlx_model, tools=[], add_base_tools=True)

agent.run("Could you give me the 118th number in the Fibonacci sequence?")

from smolagents import CodeAgent

model = InferenceClientModel()
agent = CodeAgent(tools=[], model=model, additional_authorized_imports=['requests', 'bs4'])
agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?")

from smolagents import ToolCallingAgent

agent = ToolCallingAgent(tools=[], model=model)
agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?")

from smolagents import WebSearchTool

search_tool = WebSearchTool()
print(search_tool("Who's the current president of Russia?"))

from huggingface_hub import list_models

task = "text-classification"

most_downloaded_model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
print(most_downloaded_model.id)

from smolagents import tool

@tool
def model_download_tool(task: str) -> str:
    """
    This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub.
    It returns the name of the checkpoint.

    Args:
        task: The task for which to get the download count.
    """
    most_downloaded_model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
    return most_downloaded_model.id

from smolagents import Tool

class ModelDownloadTool(Tool):
    name = "model_download_tool"
    description = "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It returns the name of the checkpoint."
    inputs = {"task": {"type": "string", "description": "The task for which to get the download count."}}
    output_type = "string"

    def forward(self, task: str) -> str:
        most_downloaded_model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
        return most_downloaded_model.id

from smolagents import CodeAgent, InferenceClientModel
agent = CodeAgent(tools=[model_download_tool], model=InferenceClientModel())
agent.run(
    "Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
)

from smolagents import CodeAgent, InferenceClientModel, WebSearchTool, ManagedAgent

model = InferenceClientModel()

web_agent = CodeAgent(tools=[WebSearchTool()], model=model)

managed_web_agent = ManagedAgent(
    agent=web_agent,
    name="web_search",
    description="Runs web searches for you. Give it your query as an argument."
)

manager_agent = CodeAgent(
    tools=[], model=model, managed_agents=[managed_web_agent]
)

manager_agent.run("Who is the CEO of Hugging Face?")

from smolagents import (
    load_tool,
    CodeAgent,
    InferenceClientModel,
    GradioUI
)

# 从 Hub 导入工具
image_generation_tool = load_tool("m-ric/text-to-image")

model = InferenceClientModel(model_id=model_id)

# 使用图像生成工具初始化 agent
agent = CodeAgent(tools=[image_generation_tool], model=model)

GradioUI(agent).launch()

agent.push_to_hub("m-ric/my_agent")

agent.from_hub("m-ric/my_agent", trust_remote_code=True)