# 🧰 Required libraries
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, tool
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
import json
import os
import gradio as gr

HF_TOKEN = os.getenv("HF_API_KEY")
client = InferenceClient(token=HF_TOKEN)

# 🛠️ Custom tool to get Hugging Face top daily paper
@tool
def get_hugging_face_top_daily_paper() -> str:
    """
    Returns the title of the most upvoted paper on Hugging Face daily papers.
    """
    try:
        url = "https://huggingface.co/papers"
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, "html.parser")

        # Use more general selector
        containers = soup.find_all('div', attrs={'data-props': True})
        top_paper = ""

        for container in containers:
            data_props = container.get('data-props', '')
            if data_props:
                try:
                    json_data = json.loads(data_props.replace('"', '"'))
                    if 'dailyPapers' in json_data:
                        top_paper = json_data['dailyPapers'][0]['title']
                        break
                except json.JSONDecodeError:
                    continue

        return top_paper or "No top paper found."
    except requests.exceptions.RequestException as e:
        return f"Error occurred while fetching the paper: {e}"

# 🚀 Run the agent with a sample query
if __name__ == "__main__":
    # 🤖 Load a model from Hugging Face Hub
    model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct", api_key=HF_TOKEN)

    # 🔧 Initialize tools
    search_tool = DuckDuckGoSearchTool()

    # 🧠 Build the agent
    agent = CodeAgent(
        tools=[search_tool, get_hugging_face_top_daily_paper],
        model=model,
        additional_authorized_imports=["requests", "bs4", "json"]
    )

    # 🖥️ Web interface
    def run_agent_interface(query):
        return agent.run(query)

    gr.Interface(fn=run_agent_interface, inputs="text", outputs="text").launch()