# 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
from langchain.schema import HumanMessage 
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.
    """
    print("reached web search tool")
    query = state.get("web_search_query", "")
    if not query:
        return {}  # nothing to do
    
    # Run DuckDuckGo
    ddg = DuckDuckGoSearchRun()
    result_text = ddg.run(query)
    print(f"web_search_result: {result_text}")
    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>}.
    """
    print("reached ocr image tool")
    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}"
    print(f"ocr_result: {text}")
    return {
        "ocr_path": None,
        "ocr_result": text
    }

def parse_excel_tool(state: AgentState) -> AgentState:
    """
    Attempts to read an actual .xlsx file at state["excel_path"]. If the file isn’t found,
    scans the conversation history for a Markdown‐style table and returns that instead.
    Returns:
      {
        "excel_path": None,
        "excel_sheet_name": None,
        "excel_result": "<either CSV‐like text or extracted Markdown table>"
      }
    If neither a real file nor a table block is found, returns an error message.
    """
    path = state.get("excel_path", "")
    sheet = state.get("excel_sheet_name", "")
    if not path:
        return {}

    # 1) Try reading the real file first
    if os.path.exists(path):
        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)
            return {
                "excel_path": None,
                "excel_sheet_name": None,
                "excel_result": text
            }
        except Exception as e:
            # If there's an I/O or parsing error, fall through to table‐extraction
            print(f">>> parse_excel_tool: Error reading Excel file {path}: {e}")

    # 2) Fallback: extract a Markdown table from any HumanMessage in state["messages"]
    messages = state.get("messages", [])
    table_lines = []
    collecting = False

    for msg in messages:
        if isinstance(msg, HumanMessage):
            for line in msg.content.splitlines():
                # Start collecting when we see the first table header row
                if re.match(r"^\s*\|\s*[-A-Za-z0-9]", line):
                    collecting = True
                if collecting:
                    if not re.match(r"^\s*\|", line):
                        # stop when the block ends (blank line or non‐table line)
                        collecting = False
                        break
                    table_lines.append(line)
            if table_lines:
                break

    if not table_lines:
        return {
            "excel_path": None,
            "excel_sheet_name": None,
            "excel_result": "Error: No Excel file found and no Markdown table detected in prompt."
        }

    # Remove any separator rows like "| ---- | ---- |"
    clean_rows = [row for row in table_lines if not re.match(r"^\s*\|\s*-+", row)]
    table_block = "\n".join(clean_rows).strip()

    return {
        "excel_path": None,
        "excel_sheet_name": None,
        "excel_result": table_block
    }

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 {}.
    """
    print("reached audio transcriber tool")
    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}"
    print(f"transcript: {text}")
    return {
        "audio_path": None,
        "transcript": text
    }