app.py

from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, InferenceClientModel, LiteLLMModel, tool
import os
import requests
import pytz
import yaml

from datetime import datetime
from Gradio_UI import GradioUI
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Configuration
HF_MODEL_ID     = os.getenv("HF_MODEL_ID", "Qwen/Qwen2.5-Coder-32B-Instruct")
OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
OLLAMA_MODEL_ID = os.getenv("OLLAMA_MODEL_ID", "qwen2.5-coder:32b")

def is_ollama_available(base_url=None, timeout=2):
    """Check if Ollama service is running and the specified model exists."""
    if base_url is None:
        base_url = OLLAMA_BASE_URL
    try:
        response = requests.get(f"{base_url}/api/tags", timeout=timeout)
        if response.status_code != 200:
            print(f"Ollama service check failed: HTTP {response.status_code} from {base_url}/api/tags")
            return False

        # Parse the response to get available models
        data = response.json()
        available_models = [model.get('name', '') for model in data.get('models', [])]

        # Check if the model exists in available models
        if OLLAMA_MODEL_ID not in available_models:
            print(f"Model '{OLLAMA_MODEL_ID}' not found in Ollama.")
            print(f"Available models: {', '.join(available_models) if available_models else 'None'}")
            return False

        print(f"Ollama service is available and model '{OLLAMA_MODEL_ID}' found.")
        return True
    except (requests.RequestException, ConnectionError) as e:
        print(f"Failed to connect to Ollama service at {base_url}: {type(e).__name__}: {e}")
        return False
    except (ValueError, KeyError) as e:
        print(f"Failed to parse Ollama API response: {type(e).__name__}: {e}")
        return False

@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.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)}"
    
@tool
def write_to_markdown(content: str, filename: str) -> None:
    """
    Write the given content to a markdown file.

    Args:
        content: The text content to write to the file
        filename: The name of the markdown file (will automatically add .md extension if not present)

    Example:
        >>> write_to_markdown("# Hello World\\nThis is a test.", "output.md")
    """
    # Add .md extension if not present
    if not filename.endswith('.md'):
        filename += '.md'

    # Write content to file
    with open(filename, 'w', encoding='utf-8') as f:
        f.write(content)

    print(f"Content successfully written to {filename}")

# Instantiate the DuckDuckGoSearchTool
search_tool = DuckDuckGoSearchTool(max_results=5, rate_limit=2.0)

# Instantiate the FinalAnswerTool
final_answer = FinalAnswerTool()

# Check if Ollama is available and configure the model accordingly
if is_ollama_available():
    print("Ollama detected - using LiteLLMModel with local Ollama instance")
    model = LiteLLMModel(
        model_id=f"ollama_chat/{OLLAMA_MODEL_ID}",  # Adjust model name based on what you have in Ollama
        api_base=OLLAMA_BASE_URL,
        api_key="ollama",
        num_ctx=8192,  # Important: Ollama's default 2048 may cause failures
        max_tokens=2096,
        temperature=0.5,
    )
else:
    print("Ollama not available - falling back to InferenceClientModel")
    # 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 = InferenceClientModel(
        max_tokens=2096,
        temperature=0.5,
        model_id=HF_MODEL_ID,  # it is possible that this model may be overloaded
        custom_role_conversions=None,
    )

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)
    
agent = CodeAgent(
    model=model,
    tools=[
	    final_answer,
	    search_tool,
	    get_current_time_in_timezone,
        write_to_markdown,
    ], ## add your tools here (don't remove final answer)
    max_steps=6,
    verbosity_level=1,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()