from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
import datetime
import fitz
import requests
import spacy
import pandas as pd
import pytz
import sys
import yaml
import subprocess
from tools.final_answer import FinalAnswerTool

from Gradio_UI import GradioUI


# Below is an example of a tool that does nothing. Amaze us with your creativity !
@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 ?"

def download_spacy_model():
    try:
        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_lg"], check=True)
        print("spaCy model downloaded successfully.")
    except subprocess.CalledProcessError as e:
        print(f"Error downloading spaCy model: {e}")

@tool
def extract_text_from_pdf(pdf_path: str) -> str:
    """A tool that extracts text from a PDF file.
    Args:
        pdf_path: A string representing the path to the PDF file.
    """
    document = fitz.open(pdf_path)
    text = ""

    # Iterate through each page
    for page_num in range(len(document)):
        page = document.load_page(page_num)
        text += page.get_text()

    return text

@tool
def extract_entities(text: str, desired_entities: list) -> list:
    """A tool that extracts entities from a text using spaCy.
    Args:
        text: A string representing the text to extract entities from.
        desired_entities: A list of entities the user needs to be extracted.
    """
    download_spacy_model()

    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)

    entities = [(ent.text, ent.label_) for ent in doc.ents if ent.label_ in desired_entities]
    return entities

@tool
def create_table_with_specific_entities(text: str, desired_entities: list) -> pd.DataFrame:
    """A tool that creates a table with specific entities extracted from a text.
    Args:
        text: A string representing the text to extract entities
        from.
        desired_entities: A list of entities the user needs to be extracted.
    """
    entities = extract_entities(text, desired_entities)

    df = pd.DataFrame(columns=desired_entities)

    for entity, label in entities:
        df.loc[len(df), label] = entity

    return df


@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("%d-%m-%Y %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()
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=[get_current_time_in_timezone, extract_entities, 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()