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| from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool | |
| import datetime | |
| import requests | |
| import pytz | |
| import yaml | |
| import os | |
| from tools.final_answer import FinalAnswerTool | |
| from Gradio_UI import GradioUI | |
| def extract_knowledge_graph(text: str) -> str: | |
| """ | |
| Uses a Hugging Face chat model to extract entities and relations from the input text. | |
| Args: | |
| text: The input text string containing entities and relationships. | |
| Returns: | |
| A string representing the knowledge graph in triple format. | |
| """ | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| if not HF_TOKEN: | |
| return "Error: Hugging Face token not found." | |
| headers = { | |
| "Authorization": f"Bearer {HF_TOKEN}", | |
| "Content-Type": "application/json" | |
| } | |
| API_URL = "https://router.huggingface.co/hf-inference/models/meta-llama/Llama-3.2-11B-Vision-Instruct/v1/chat/completions" | |
| # Format the prompt | |
| prompt = f"""Extract all the entities and their relationships from this text as structured knowledge triples. | |
| Text: "{text}" | |
| Format: | |
| (Subject) -[Relation]-> (Object) | |
| """ | |
| payload = { | |
| "messages": [ | |
| {"role": "system", "content": "You are a helpful assistant that extracts knowledge graphs from text."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| "temperature": 0.5, | |
| "max_tokens": 1024 | |
| } | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code != 200: | |
| return f"API error: {response.status_code} - {response.text}" | |
| try: | |
| output = response.json()["choices"][0]["message"]["content"] | |
| return output | |
| except Exception as e: | |
| return f"Failed to parse response: {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() | |
| web_search = DuckDuckGoSearchTool() | |
| # 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, extract_knowledge_graph], ## 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() |