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| from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool | |
| import datetime | |
| import fitz | |
| import requests | |
| import spacy | |
| import pandas as pd | |
| import pytz | |
| import sys | |
| import yaml | |
| import subprocess | |
| 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 download_spacy_model(): | |
| try: | |
| subprocess.run(["python", "-m", "spacy", "download", "en_core_web_lg"], check=True) | |
| print("spaCy model downloaded successfully.") | |
| except subprocess.CalledProcessError as e: | |
| print(f"Error downloading spaCy model: {e}") | |
| def extract_text_from_pdf(pdf_path: str) -> str: | |
| """A tool that extracts text from a PDF file. | |
| Args: | |
| pdf_path: A string representing the path to the PDF file. | |
| """ | |
| document = fitz.open(pdf_path) | |
| text = "" | |
| # Iterate through each page | |
| for page_num in range(len(document)): | |
| page = document.load_page(page_num) | |
| text += page.get_text() | |
| return text | |
| def extract_entities(text: str, desired_entities: list) -> list: | |
| """A tool that extracts entities from a text using spaCy. | |
| Args: | |
| text: A string representing the text to extract entities from. | |
| desired_entities: A list of entities the user needs to be extracted. | |
| """ | |
| download_spacy_model() | |
| nlp = spacy.load("en_core_web_sm") | |
| doc = nlp(text) | |
| entities = [(ent.text, ent.label_) for ent in doc.ents if ent.label_ in desired_entities] | |
| return entities | |
| def create_table_with_specific_entities(text: str, desired_entities: list) -> pd.DataFrame: | |
| """A tool that creates a table with specific entities extracted from a text. | |
| Args: | |
| text: A string representing the text to extract entities | |
| from. | |
| desired_entities: A list of entities the user needs to be extracted. | |
| """ | |
| entities = extract_entities(text, desired_entities) | |
| df = pd.DataFrame(columns=desired_entities) | |
| for entity, label in entities: | |
| df.loc[len(df), label] = entity | |
| return df | |
| 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("%d-%m-%Y %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() | |
| 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=[get_current_time_in_timezone, extract_entities, final_answer], ## 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() |