from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_core.messages import SystemMessage
from tools.searchtools import wiki_search, web_search, arxiv_search, get_youtube_transcript
from tools.mathtools import multiply, add, subtract, divide, modulus, power, square_root
from tools.codetools import execute_code_multilang
from tools.documenttools import create_file_with_content, read_file_content, download_file_from_url, extract_text_from_image, analyze_csv_file, analyze_excel_file
from tools.imagetools import analyze_image, transform_image, draw_on_image, generate_simple_image, combine_images
from tools.audiotools import transcribe_audio
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

load_dotenv()

# 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 = [
    web_search,
    wiki_search,
    arxiv_search,
    get_youtube_transcript,
    multiply,
    add,
    subtract,
    divide,
    modulus,
    power,
    square_root,
    create_file_with_content,
    read_file_content,
    download_file_from_url,
    extract_text_from_image,
    analyze_csv_file,
    analyze_excel_file,
    execute_code_multilang,
    analyze_image,
    transform_image,
    draw_on_image,
    generate_simple_image,
    combine_images,
    transcribe_audio,
]


# Build graph function
def build_graph():
    """Build the graph"""
    # Load environment variables from .env file
    llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)

    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        # Prepend system message to the current messages
        # Ensure sys_msg is only added if not already present or if it's the first turn
        current_messages = state["messages"]
        if not current_messages or current_messages[0].type != "system":
            # Or, if you want to ensure it's always the first message for each LLM call in this node:
            # updated_messages = [sys_msg] + [m for m in current_messages if m.type != "system"]
            # For simplicity, let's assume we add it if it's not the very first message overall.
            # A more robust check might be needed depending on multi-turn conversation flow.
            updated_messages = [sys_msg] + current_messages
        else:
            updated_messages = current_messages
        return {"messages": [llm_with_tools.invoke(updated_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()