from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import requests
import pytz
import yaml
from tools.final_answer import FinalAnswerTool

from Gradio_UI import GradioUI

@tool
def find_nearest_meteor(address: str) -> str:
    """A tool that finds the nearest meteor landing site based on a given address.
    
    Args:
        address: A string representing an address (e.g., '123 Main St, New York, NY')
    """
    import requests
    import math
    from urllib.parse import quote
    
    def geocode_address(address):
        """Convert address to latitude and longitude using OpenCage API."""
        base_url = "https://api.opencagedata.com/geocode/v1/json"
        api_key = "03c48dae07364cabb7f121d8c1519492"
        
        # URL encode the address
        encoded_address = quote(address)
        
        # Construct the full request URL
        url = f"{base_url}?q={encoded_address}&key={api_key}&no_annotations=1&language=en"
        
        headers = {
            'accept': 'application/json, text/javascript, */*; q=0.01',
            'accept-language': 'pt-BR,pt;q=0.9,en-US;q=0.8,en;q=0.7',
            'origin': 'https://www.gps-coordinates.net',
            'referer': 'https://www.gps-coordinates.net/',
            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36'
        }
        
        try:
            response = requests.get(url, headers=headers)
            data = response.json()
            
            if data['total_results'] > 0:
                location = data['results'][0]['geometry']
                return {
                    'lat': location['lat'],
                    'lng': location['lng'],
                    'formatted_address': data['results'][0]['formatted']
                }
            else:
                return None
        except Exception as e:
            return f"Error geocoding address: {str(e)}"
    
    def get_meteor_data():
        """Fetch meteor landing site data from NASA API."""
        url = "https://data.nasa.gov/resource/gh4g-9sfh.json"
        try:
            response = requests.get(url)
            return response.json()
        except Exception as e:
            return f"Error fetching meteor data: {str(e)}"
    
    def calculate_distance(lat1, lon1, lat2, lon2):
        """Calculate the great circle distance between two points on earth."""
        # Radius of earth in kilometers
        R = 6371.0
        
        # Convert latitude and longitude from degrees to radians
        lat1_rad = math.radians(lat1)
        lon1_rad = math.radians(lon1)
        lat2_rad = math.radians(lat2)
        lon2_rad = math.radians(lon2)
        
        # Difference in coordinates
        dlon = lon2_rad - lon1_rad
        dlat = lat2_rad - lat1_rad
        
        # Haversine formula
        a = math.sin(dlat / 2)**2 + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon / 2)**2
        c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
        distance = R * c
        
        return distance
    
    # Geocode the address
    location_data = geocode_address(address)
    if not location_data or isinstance(location_data, str):
        return f"Could not geocode address: {address}. {location_data if isinstance(location_data, str) else ''}"
    
    # Get meteor data
    meteor_data = get_meteor_data()
    if isinstance(meteor_data, str):
        return meteor_data
    
    # Calculate distance to each meteor
    nearest_meteor = None
    min_distance = float('inf')
    
    for meteor in meteor_data:
        # Check if meteor has geolocation data
        if 'geolocation' in meteor and meteor['geolocation'] and 'latitude' in meteor['geolocation'] and 'longitude' in meteor['geolocation']:
            try:
                meteor_lat = float(meteor['geolocation']['latitude'])
                meteor_lng = float(meteor['geolocation']['longitude'])
                
                distance = calculate_distance(
                    location_data['lat'], 
                    location_data['lng'], 
                    meteor_lat, 
                    meteor_lng
                )
                
                if distance < min_distance:
                    min_distance = distance
                    nearest_meteor = meteor
            except (ValueError, TypeError):
                # Skip entries with invalid coordinates
                continue
    
    if nearest_meteor:
        # Format the response
        name = nearest_meteor.get('name', 'Unknown')
        year = nearest_meteor.get('year', 'Unknown year')
        mass = nearest_meteor.get('mass', 'Unknown mass')
        recclass = nearest_meteor.get('recclass', 'Unknown classification')
        
        return (
            f"Nearest meteor to {location_data['formatted_address']}:\n"
            f"Name: {name}\n"
            f"Year: {year}\n"
            f"Classification: {recclass}\n"
            f"Mass (grams): {mass}\n"
            f"Distance: {min_distance:.2f} km\n"
            f"Coordinates: {nearest_meteor['geolocation']['latitude']}, {nearest_meteor['geolocation']['longitude']}"
        )
    else:
        return "No meteor landing sites found with valid coordinates."
@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' 

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=[find_nearest_meteor, 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()