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| from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool | |
| import datetime | |
| import requests | |
| import pytz | |
| import yaml | |
| from tools.final_answer import FinalAnswerTool | |
| from Gradio_UI import GradioUI | |
| # Below is an example of a tool that does nothing. Amaze us with your creativity ! | |
| def my_custom_tool(arg1:str, arg2:int)-> str: #it's import to specify the return type | |
| #Keep this format for the description / args / args description but feel free to modify the tool | |
| """A tool that does nothing yet | |
| Args: | |
| arg1: the first argument | |
| arg2: the second argument | |
| """ | |
| return "What magic will you build ?" | |
| def analyze_text_sentiment(text: str) -> str: | |
| """A tool that analyzes the sentiment of provided text. | |
| Args: | |
| text: The text to analyze | |
| """ | |
| try: | |
| # Using a simple free API (Text Processing) | |
| url = "http://text-processing.com/api/sentiment/" | |
| payload = {"text": text} | |
| response = requests.post(url, data=payload) | |
| data = response.json() | |
| if response.status_code != 200: | |
| return "Error analyzing sentiment" | |
| label = data['label'] | |
| probability = data['probability'] | |
| return (f"Sentiment Analysis:\n" | |
| f"Sentiment: {label}\n" | |
| f"Positive: {probability['positive']:.2f}\n" | |
| f"Negative: {probability['negative']:.2f}\n" | |
| f"Neutral: {probability['neutral']:.2f}") | |
| except Exception as e: | |
| return f"Error analyzing sentiment: {str(e)}" | |
| def generate_dream_sequence(theme: str, intensity: int) -> str: | |
| """Generates a surreal dream sequence based on a theme and intensity level. | |
| Args: | |
| theme: A word or phrase to inspire the dream (e.g., 'forest', 'space') | |
| intensity: Dream weirdness level (1-10) | |
| """ | |
| try: | |
| if not 1 <= intensity <= 10: | |
| return "Intensity must be between 1 and 10" | |
| # Dream elements | |
| entities = ["whispering trees", "floating clocks", "velvet whales", "mirrored shadows", | |
| "singing stones", "dancing galaxies", "liquid mirrors", "feathered serpents"] | |
| actions = ["melted into", "chased", "wove through", "echoed across", "dissolved into", | |
| "spiraled around", "painted", "whispered to"] | |
| sensations = ["a hum of static", "a taste of rain", "a shimmer of gold", | |
| "a scent of forgotten time", "a pull of gravity", "a flicker of warmth"] | |
| import random | |
| random.seed(intensity + hash(theme)) | |
| dream_length = intensity // 2 + 1 | |
| dream = f"In a dream of {theme}, " | |
| for _ in range(dream_length): | |
| entity = random.choice(entities) | |
| action = random.choice(actions) | |
| next_entity = random.choice(entities) | |
| sensation = random.choice(sensations) | |
| dream += f"the {entity} {action} the {next_entity}, leaving {sensation}. " | |
| return dream.strip() | |
| except Exception as e: | |
| return f"Dream disrupted: {str(e)}" | |
| def get_current_time_in_timezone(timezone: str) -> str: | |
| """A tool that fetches the current local time in a specified timezone. | |
| Args: | |
| timezone: A string representing a valid timezone (e.g., 'America/New_York'). | |
| """ | |
| try: | |
| # Create timezone object | |
| tz = pytz.timezone(timezone) | |
| # Get current time in that timezone | |
| local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S") | |
| return f"The current local time in {timezone} is: {local_time}" | |
| except Exception as e: | |
| return f"Error fetching time for timezone '{timezone}': {str(e)}" | |
| final_answer = FinalAnswerTool() | |
| # 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: | |
| # model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' | |
| model = HfApiModel( | |
| max_tokens=2096, | |
| temperature=0.5, | |
| model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded | |
| custom_role_conversions=None, | |
| ) | |
| # Import tool from Hub | |
| image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) | |
| with open("prompts.yaml", 'r') as stream: | |
| prompt_templates = yaml.safe_load(stream) | |
| agent = CodeAgent( | |
| model=model, | |
| tools=[final_answer, analyze_text_sentiment, generate_dream_sequence], ## add your tools here (don't remove final answer) | |
| max_steps=6, | |
| verbosity_level=1, | |
| grammar=None, | |
| planning_interval=None, | |
| name=None, | |
| description=None, | |
| prompt_templates=prompt_templates | |
| ) | |
| GradioUI(agent).launch() |