import os
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import tool
from langgraph.prebuilt import tools_condition
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
from langchain.prompts import PromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_core.messages import SystemMessage, HumanMessage
#from langchain_openai import ChatOpenAI  # OpenAI-compatible for Groq API
from ddgs import DDGS  # Updated DuckDuckGo Search
from dotenv import load_dotenv
import re
import json

# Load environment variables
load_dotenv()

# --- Define Tools ---
@tool
def python_code_executor(code: str) -> str:
    """Execute Python code and return the result as a string. Use for calculations or data processing."""
    try:
        local_vars = {}
        exec(code, {}, local_vars)
        return str(local_vars.get("result", "No result defined. Set 'result' variable."))
    except Exception as e:
        return f"Error: {str(e)}"

@tool
def download_file(url: str) -> str:
    """Download a file from URL and return its content (text if possible)."""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        return response.text[:1000]  # Truncate for brevity
    except Exception as e:
        return f"Error downloading: {str(e)}"

@tool
def duckduckgo_search(query: str) -> str:
    """Perform a DuckDuckGo search and return top results as a short summary."""
    try:
        with DDGS() as ddgs:
            results = list(ddgs.text(query, max_results=3))
        if not results:
            return "No good results found."
        return json.dumps([{"title": r["title"], "snippet": r["body"]} for r in results])
    except Exception as e:
        return f"Search error: {str(e)}"


# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# System message
sys_msg = SystemMessage(content=system_prompt)

tools = [
    python_code_executor,
    download_file,
    duckduckgo_search,
]

# Build graph function
#def build_graph(provider: str = "huggingface"):
def build_graph(provider: str = "google"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
            ),
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    builder.add_edge("tools", "assistant")

    # Compile graph
    return builder.compile()