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Update app.py
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app.py
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@@ -8,109 +8,109 @@ import random
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from Gradio_UI import GradioUI
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@tool
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def futuristic_profession_predictor(name: str) -> str:
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# Below is an example of a tool that does nothing. Amaze us with your creativity !
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@tool
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def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
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@tool
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def findLoveOfTwoNumbers(arg1:int, arg2:int)-> int:
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# Below is an example of a tool that does nothing. Amaze us with your creativity !
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@tool
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def personIdentifier(arg1:str)-> int: #it's important to specify the return type
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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final_answer = FinalAnswerTool()
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# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
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# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
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model = HfApiModel(
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max_tokens=2096,
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temperature=0.5,
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model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
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custom_role_conversions=None,
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)
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# Import tool from Hub
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image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
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with open("prompts.yaml", 'r') as stream:
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agent = CodeAgent(
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)
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from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
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from Gradio_UI import GradioUI
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# @tool
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# def futuristic_profession_predictor(name: str) -> str:
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# """Predicts the person's profession in the year 2050 based on their name.
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# Args:
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# name: The name of the person.
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# """
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# professions = [
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# "Quantum Data Alchemist",
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# "Neural Interface Designer",
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# "AI-Powered Philosopher",
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# "Martian Agriculture Specialist",
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# "Virtual Reality Psychologist",
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# "Holographic Content Creator",
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# "Synthetic Biology Engineer",
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# "Time Travel Consultant",
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# "Cybersecurity Shaman"
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# ]
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# prediction = random.choice(professions)
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# return f"In the year 2050, {name} will be a {prediction}!"
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# # Below is an example of a tool that does nothing. Amaze us with your creativity !
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# @tool
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# def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type
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# #Keep this format for the description / args / args description but feel free to modify the tool
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# """A tool that does nothing yet
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# Args:
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# arg1: the first argument
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# arg2: the second argument
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# """
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# return "What magic will you build ?"
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# @tool
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# def findLoveOfTwoNumbers(arg1:int, arg2:int)-> int:
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# """ A tool that returns Love of two numbers
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# Args:
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# arg1: the first argument
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# arg2: the second argument
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# """
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# return arg1+arg2 - 20
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# # Below is an example of a tool that does nothing. Amaze us with your creativity !
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# @tool
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# def personIdentifier(arg1:str)-> int: #it's important to specify the return type
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# #Keep this format for the description / args / args description but feel free to modify the tool
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# """A Tool that identifies the person. For example,
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# Task: Who is Ajit Kumar?
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# Answer: He is a professor at Shiv Nadar University.
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# Args:
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# arg1: the first argument
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# """
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# return "He is a professor at Shiv Nadar University"
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# @tool
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# def get_current_time_in_timezone(timezone: str) -> str:
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# """A tool that fetches the current local time in a specified timezone.
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# Args:
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# timezone: A string representing a valid timezone (e.g., 'America/New_York').
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# """
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# try:
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# # Create timezone object
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# tz = pytz.timezone(timezone)
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# # Get current time in that timezone
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# local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
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# return f"The current local time in {timezone} is: {local_time}"
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# except Exception as e:
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# return f"Error fetching time for timezone '{timezone}': {str(e)}"
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# final_answer = FinalAnswerTool()
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# # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
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# # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
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# model = HfApiModel(
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# max_tokens=2096,
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# temperature=0.5,
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# model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
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# custom_role_conversions=None,
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# )
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# # Import tool from Hub
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# image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
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# with open("prompts.yaml", 'r') as stream:
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# prompt_templates = yaml.safe_load(stream)
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# agent = CodeAgent(
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# model=model,
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# #tools=[final_answer, image_generation_tool, DuckDuckGoSearchTool()], ## add your tools here (don't remove final answer)
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# tools = [final_answer, futuristic_profession_predictor, personIdentifier, findLoveOfTwoNumbers],
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# max_steps=20,
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# verbosity_level=1,
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# grammar=None,
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# planning_interval=None,
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# name=None,
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# description=None,
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# prompt_templates=prompt_templates
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# )
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from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
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