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9b5b26a c19d193 8196a62 6aae614 8fe992b 9b5b26a f8ededc 50bdf93 704ca40 f8ededc 50bdf93 f8ededc 50bdf93 f8ededc 50bdf93 f8ededc 50bdf93 f8ededc 50bdf93 f8ededc 50bdf93 9b5b26a 8c01ffb 6aae614 9ad228b ae7a494 e121372 bf6d34c 29ec968 fe328e0 13d500a 8c01ffb 9b5b26a 8c01ffb 861422e 9b5b26a 8c01ffb 8fe992b a17cecc 8c01ffb 861422e 8fe992b 9b5b26a 8c01ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | 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
@tool
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}"
@tool
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() |