# tools.py

import pandas as pd
from langchain_community.tools import DuckDuckGoSearchRun
from pathlib import Path
from PIL import Image
import pytesseract
from state import AgentState
def web_search_tool(state: AgentState) -> AgentState:
    """
    Expects: state["web_search_query"] is a non‐empty string.
    Returns: {"web_search_query": None, "web_search_result": <string>}
    We also clear web_search_query so we don’t loop forever.
    """
    query = state.get("web_search_query", "")
    if not query:
        return {}  # nothing to do
    
    # Run DuckDuckGo
    ddg = DuckDuckGoSearchRun()
    result_text = ddg.run(query)
    return {
        "web_search_query": None,
        "web_search_result": result_text
    }

def ocr_image_tool(state: AgentState) -> AgentState:
    """
    Expects: state["ocr_path"] is a path to an image file.
    Returns: {"ocr_path": None, "ocr_result": <string>}.
    """
    path = state.get("ocr_path", "")
    if not path:
        return {}
    try:
        img = Image.open(path)
        text = pytesseract.image_to_string(img)
        text = text.strip() or "(no visible text)"
    except Exception as e:
        text = f"Error during OCR: {e}"
    return {
        "ocr_path": None,
        "ocr_result": text
    }

def parse_excel_tool(state: AgentState) -> AgentState:
    """
    Expects: state["excel_path"] is a path to an .xlsx file,
             and state["excel_sheet_name"] optionally names a sheet.
    Returns: {"excel_path": None, "excel_sheet_name": None, "excel_result": <string>}.
    """
    path = state.get("excel_path", "")
    sheet = state.get("excel_sheet_name", "")
    if not path:
        return {}

    try:
        xls = pd.ExcelFile(path)
        if sheet and sheet in xls.sheet_names:
            df = pd.read_excel(xls, sheet_name=sheet)
        else:
            df = pd.read_excel(xls, sheet_name=xls.sheet_names[0])
        records = df.to_dict(orient="records")
        text = str(records)
    except Exception as e:
        text = f"Error reading Excel: {e}"
    return {
        "excel_path": None,
        "excel_sheet_name": None,
        "excel_result": text
    }


def run_tools(state: AgentState, tool_out: AgentState) -> AgentState:
    """
    Merges whatever partial state the tool wrapper returned (tool_out)
    into the main state. That is, combine previous keys with new keys:
      new_state = { **state, **tool_out }.
    This node should be wired as its own graph node, not as a transition function.
    """
    new_state = {**state, **tool_out}
    return new_state


import os





import os
import openai
from state import AgentState

def audio_transcriber_tool(state: AgentState) -> AgentState:
    """
    LangGraph tool for transcribing audio via OpenAI’s hosted Whisper API.
    Expects: state["audio_path"] to be a valid path to a .wav/.mp3/.m4a file.
    Returns:
      {
        "audio_path": None,
        "transcript": "<transcribed text or error message>"
      }
    If no valid audio_path is provided, returns {}.
    """
    path = state.get("audio_path", "")
    if not path or not os.path.exists(path):
        return {}

    try:
        openai.api_key = os.getenv("OPENAI_API_KEY")
        if not openai.api_key:
            raise RuntimeError("OPENAI_API_KEY is not set in environment.")

        with open(path, "rb") as audio_file:
            # For OpenAI Python library v0.27.0+:
            response = openai.Audio.transcribe("whisper-1", audio_file)
            # If using an older OpenAI library, use:
            # response = openai.Audio.create_transcription(file=audio_file, model="whisper-1")

        text = response["text"].strip()
    except Exception as e:
        text = f"Error during transcription: {e}"

    return {
        "audio_path": None,
        "transcript": text
    }