from smolagents import CodeAgent,DuckDuckGoSearchTool, LiteLLMModel,load_tool,tool import datetime import requests import pytz import yaml from tools.final_answer import FinalAnswerTool from tools.visit_webpage import VisitWebpageTool import os from Gradio_UI import GradioUI from PIL import Image from duckduckgo_search import DDGS import datetime import time # @tool # def show_image(image : Image.Image )-> 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 shows image generated by image_generation_tool # Args: # image: the input image with the original type # """ # image.show() # return "image showed successfully!" @tool def browsing_tool_fetch_content(url: str, query_context: str) -> str: """ Placeholder function to simulate fetching full content from a URL. In a real scenario, this would use a library like 'requests' and 'BeautifulSoup' or a dedicated browsing/scraping API. The query_context is provided if the browsing tool can use it for better extraction. Args: url: the URL to fetch the content from. query_context: the context related to the URL. """ print(f"[Browsing Tool Stub] Attempting to fetch content for URL: {url} (context: '{query_context}')") # Simulate fetching content. Replace with actual fetching logic. # For demonstration, we'll return a placeholder. # In a real implementation, you'd handle potential errors (network issues, 404s, etc.) try: # Example (conceptual - requests/BeautifulSoup would be more robust): import requests from bs4 import BeautifulSoup response = requests.get(url, timeout=10) response.raise_for_status() # Raise an exception for HTTP errors soup = BeautifulSoup(response.content, 'html.parser') # Extract text - this is a simple example and might need refinement paragraphs = soup.find_all('p') fetched_text = "\n".join([p.get_text() for p in paragraphs]) if not fetched_text: # Fallback or more targeted extraction if

tags are not primary content holders fetched_text = soup.get_text(separator='\n', strip=True) return fetched_text # return f"Full content for {url} would be fetched here. This is a placeholder. Query context: {query_context}" except Exception as e: return f"Error fetching content from {url}: {str(e)}" @tool def search_duckduckgo(topic: str, max_results: int = 3) -> list: """ Searches DuckDuckGo for a given topic, retrieves search results, and then attempts to fetch the full content of each result URL. Args: topic: The topic to search for. max_results: The maximum number of search results to process. Returns: A list of dictionaries, where each dictionary represents a search result and contains: - 'title': The title of the search result. - 'href': The URL of the search result. - 'original_snippet': The original snippet from DuckDuckGo. - 'full_content': The fetched full content from the URL (or an error message/placeholder). """ print(f"Searching DuckDuckGo for: {topic} (max_results: {max_results})") detailed_results_list = [] try: # Get initial search results from DuckDuckGo initial_results = DDGS().text(topic, max_results=max_results) if not initial_results: print("No initial results found from DuckDuckGo.") return [] print(f"Found {len(initial_results)} initial results. Now fetching full content...") for result in initial_results: title = result.get('title', 'N/A') href = result.get('href', None) original_snippet = result.get('body', 'N/A') print(f"\nProcessing result: {title}") print(f" URL: {href}") full_content = "N/A" # Default if URL is missing or fetching fails if href: # Use the placeholder browsing tool to fetch full content # Pass the original 'topic' as query_context for the browsing tool full_content = browsing_tool_fetch_content(url=href, query_context=topic) else: print(" No URL found for this result, cannot fetch full content.") full_content = "No URL provided in search result." detailed_results_list.append({ 'title': title, 'href': href, 'original_snippet': original_snippet, 'full_content': full_content }) print(f" Full content (or placeholder/error): {full_content[:200]}...") # Print a snippet of fetched content except Exception as e: print(f"An error occurred during the search or content fetching process: {str(e)}") # Optionally, return partial results or an empty list depending on desired error handling # return detailed_results_list # Could return what was processed so far return [result['full_content'] for result in detailed_results_list] # @tool # def search_duckduckgo(topic : str)-> list: # """ # Searches DuckDuckGo for a given topic and returns a list of results. # Args: # topic: The topic to search for. # Returns: # A list of dictionaries, where each dictionary represents a search result # and contains keys like 'title', 'href', and 'body'. # """ # results = DDGS().text(topic, max_results=3) # return results @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() # 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' os.environ["GOOGLE_API_KEY"] = "AIzaSyBcJrlnDDdWtjUDiLrisSOPuaAGizCLKO4" gemini_api_key = os.environ.get("GOOGLE_API_KEY") try: # LiteLLM uses 'gemini/' prefix for Google AI Studio models gemini_model = LiteLLMModel( model_id="gemini/gemini-1.5-flash-latest", api_key=gemini_api_key, temperature = 0.5, max_tokens = 2096, custom_role_conversions=None ) print("Successfully initialized LiteLLMModel for Gemini 1.5 Flash.") except Exception as e: print(f"Failed to initialize LiteLLMModel: {e}") gemini_model = None # model = HfApiModel( # max_tokens=2096, # temperature=0.5, # model_id='google/gemma-2b-it',# it is possible that this model may be overloaded # custom_role_conversions=None, # ) search_tool = DuckDuckGoSearchTool() web_visit = VisitWebpageTool() # 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=gemini_model, tools=[final_answer,get_current_time_in_timezone, search_tool, web_visit, image_generation_tool], ## 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()