model.py

from dotenv import load_dotenv, find_dotenv
import os
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_tavily import TavilySearch
from langchain_community.document_loaders import AsyncHtmlLoader
from langchain.tools import tool
from langchain.prompts import ChatPromptTemplate
from langchain.agents import AgentExecutor, create_tool_calling_agent
from prompt import get_prompt
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader

import re

# --- Define Tools ---
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract b from a."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divide a by b, error on zero."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Compute a mod b."""
    return a % b

@tool
def wiki_search(query: str) -> dict:
    """Search Wikipedia and return up to 2 documents."""
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content}" for d in docs]
    return {"wiki_results": "\n---\n".join(results)}

@tool
def web_search(query: str) -> dict:
    """Do a web search with Tavily and return up to 4 results."""
    docs = TavilySearchResults(max_results=4).invoke(query=query)
    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content}" for d in docs]
    return {"web_results": "\n---\n".join(results)}

@tool
def arxiv_search(query: str) -> dict:
    """Search Arxiv and return up to 3 docs."""
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    results = [f"<Document source=\"{d.metadata['source']}\" page=\"{d.metadata.get('page','')}\"/>\n{d.page_content[:1000]}" for d in docs]
    return {"arxiv_results": "\n---\n".join(results)}

class Model:
    def __init__(self):        
        #load_dotenv(find_dotenv())
        self.token = os.getenv("HF_TOKEN")
        self.system_prompt = get_prompt()
        print(f"system_prompt: {self.system_prompt}")
        self.agent_executor = self.setup_model()

    def get_answer(self, question: str) -> str:
        try:
            result = self.agent_executor.invoke({"input": question})
        except BaseException as e:
            print(f"An error occurred: {e}")
            result = {"FINAL_ANSWER":"ERROR"}

        # The final answer is typically in the 'output' key of the result dictionary
        final_answer = result['output']

        pattern = r'FINAL_ANSWER:"(.*?)"'
        match = re.search(pattern, final_answer, re.DOTALL)
        if match:
            final_answer_value = match.group(1)
            print(f"The extracted FINAL_ANSWER is: {final_answer_value}")  
        else:
            print("ERROR: Pattern not found.: {r}")
            final_answer_value = "ERROR"

        return final_answer_value


    def setup_model(self):
        search = DuckDuckGoSearchRun()
        tavily_search_tool = TavilySearch(
            api_key=os.getenv("TAVILY_API_KEY"),
            max_results=5,
            topic="general",
        )
        # # Define a tool for the agent to use
        tools = [
            multiply,
            add,
            subtract,
            divide,
            modulus,
            wiki_search,
            tavily_search_tool,
            arxiv_search,
        ]
        
        llm = HuggingFaceEndpoint(
            repo_id="Qwen/Qwen3-Next-80B-A3B-Thinking",
            huggingfacehub_api_token=self.token,
            temperature=0
        )

        chat = ChatHuggingFace(llm=llm).bind_tools(tools)

        # Create the ReAct prompt template
        prompt = ChatPromptTemplate.from_messages(
            [
                ("system", self.system_prompt),  # Use the new, detailed ReAct prompt
                ("placeholder", "{agent_scratchpad}"),
                ("human", "{input}"),
            ]
        )

        # Create the agent
        agent = create_tool_calling_agent(chat, tools, prompt)

        # Create the agent executor
        return AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)



def main():
    load_dotenv(find_dotenv())
    model = Model()
    response = model.get_answer("Where were the Vietnamese specimens described by Kuznetzov in Nedoshivina's 2010 paper eventually deposited? Just give me the city name without abbreviations.")
    print(f"the output is: {response}")

if __name__ == "__main__":
    main()