app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, LiteLLMModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool
import os
from Gradio_UI import GradioUI

from duckduckgo_search import DDGS

import datetime
import time



@tool
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 ?"


@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 <p> 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 detailed_results_list['full_content']




# @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()

# 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_duckduckgo, browsing_tool_fetch_content], ## 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()