Spaces:
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Sleeping
jxp commited on
Commit ·
a86e3b2
0
Parent(s):
Dummy agent exercise complete
Browse files- .gitignore +13 -0
- .python-version +1 -0
- README.md +0 -0
- dummy_agent.py +37 -0
- main.py +6 -0
- prompts.py +35 -0
- pyproject.toml +9 -0
- uv.lock +547 -0
.gitignore
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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# sensitive files
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hf_token.txt
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.python-version
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3.12
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README.md
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File without changes
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dummy_agent.py
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from huggingface_hub import InferenceClient
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from prompts import SYSTEM_PROMPT
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with open("hf_token.txt") as f:
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hf_token = f.read().strip()
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client = InferenceClient(model="meta-llama/Llama-4-Scout-17B-16E-Instruct", token=hf_token)
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def get_weather(location):
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return f"the weather in {location} is sunny with low temperatures. \n"
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": "What's the weather in London?"},
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]
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output = client.chat.completions.create(
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messages=messages,
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stream=False,
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max_tokens=1024,
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stop=["Observation:"]
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)
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# print(output.choices[0].message.content)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": "What's the weather in London?"},
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{"role": "assistant", "content": output.choices[0].message.content + "Observation:\n" + get_weather('London')},
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]
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output = client.chat.completions.create(
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messages=messages,
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stream=False,
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max_tokens=1024,
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)
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print(output.choices[0].message.content)
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main.py
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def main():
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print("Hello from hf-agents-course!")
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if __name__ == "__main__":
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main()
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prompts.py
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SYSTEM_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
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get_weather: Get the current weather in a given location
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The way you use the tools is by specifying a json blob.
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Specifically, this json should have an `action` key (with the name of the tool to use) and an `action_input` key (with the input to the tool going here).
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The only values that should be in the "action" field are:
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get_weather: Get the current weather in a given location, args: {"location": {"type": "string"}}
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example use :
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{{
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"action": "get_weather",
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"action_input": {"location": "New York"}
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}}
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ALWAYS use the following format:
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Question: the input question you must answer
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Thought: you should always think about one action to take. Only one action at a time in this format:
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Action:
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$JSON_BLOB (inside markdown cell)
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Observation: the result of the action. This Observation is unique, complete, and the source of truth.
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(this Thought/Action/Observation can repeat N times, you should take several steps when needed. The $JSON_BLOB must be formatted as markdown and only use a SINGLE action at a time.)
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You must always end your output with the following format:
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Now begin! Reminder to ALWAYS use the exact characters `Final Answer:` when you provide a definitive answer. """
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pyproject.toml
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[project]
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name = "hf-agents-course"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"huggingface-hub[inference]>=0.34.4",
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]
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uv.lock
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