Update app.py
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app.py
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from smolagents import CodeAgent,
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import datetime
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import requests
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import pytz
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import yaml
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from tools.final_answer import FinalAnswerTool
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from Gradio_UI import GradioUI
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#
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@tool
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def
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Args:
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"""
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@tool
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def
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"""
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Args:
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"""
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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'
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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
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max_steps=6,
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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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GradioUI(agent).launch()
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from smolagents import CodeAgent, HfApiModel, tool
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import yaml
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from Gradio_UI import GradioUI
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import requests
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# Example tool to analyze waste data (Пример инструмента для анализа данных о вторсырье)
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@tool
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def analyze_waste_data(data: dict) -> str:
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"""Analyzes waste data and provides recommendations.
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(Анализирует данные о вторсырье и предоставляет рекомендации.)
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Args:
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data: A dictionary containing waste data, including types and quantities of waste.
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(Словарь с данными о вторсырье, включающий типы и количество отходов.)
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"""
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# Example analysis of data (Пример анализа данных)
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waste_types = data.get("types", {})
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recommendations = []
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for waste_type, amount in waste_types.items():
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if amount > 100: # Example threshold value (Пример порогового значения)
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recommendations.append(f"It is necessary to collect {waste_type}.")
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# (Необходимо вывезти {waste_type}.)
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return "\n".join(recommendations)
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# Example tool to visualize collection routes (Пример инструмента для визуализации маршрутов)
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@tool
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def visualize_collection_routes(locations: list) -> str:
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"""Visualizes optimal routes for waste collection.
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(Визуализирует оптимальные маршруты для сбора вторсырья.)
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Args:
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locations: A list of coordinates for container locations.
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(Список координат местоположений контейнеров.)
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"""
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# Example use of a maps API to visualize routes (Пример использования API карт для визуализации маршрутов)
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map_url = "https://maps.google.com/?q=" + "&q=".join([f"{lat},{lon}" for lat, lon in locations])
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return f"Optimal routes can be viewed at this link: {map_url}"
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# (Оптимальные маршруты можно посмотреть по ссылке: {map_url})
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# Load model and create agent (Загрузка модели и создание агента)
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final_answer = FinalAnswerTool()
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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',
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custom_role_conversions=None,
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)
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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, analyze_waste_data, visualize_collection_routes],
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max_steps=6,
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verbosity_level=1,
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prompt_templates=prompt_templates
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)
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GradioUI(agent).launch()
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