import cv2
import numpy as np
import constants
from langchain_core.prompts import ChatPromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.graph import StateGraph, END
from typing import TypedDict, List, Dict
import google.generativeai as genai

def image_to_fen(image_bytes):
 
    genai.configure(api_key=constants.API_KEY)

    model = genai.GenerativeModel(constants.MODEL)

    response = model.generate_content([
    {"mime_type": "image/jpeg", "data": image_bytes},
        "Describe the chessboard in this image and provide the FEN notation."
])
    
    print(response.text)

    return ''

if __name__ == '__main__':
    # Example usage:
    # 1.  Load an image from a file (replace with your image path)
    image_path = r"C:\Users\agazo\Downloads\cca530fc-4052-43b2-b130-b30968d8aa44_file.png"  # Replace with a valid image path
    try:
        with open(image_path, "rb") as image_file:
            image_bytes = image_file.read()
    except FileNotFoundError:
        print(f"Error: File not found at {image_path}.  Please make sure the path is correct and the file exists.")
        exit()

    # 2. Call the function
    fen = image_to_fen(image_bytes)
    print(f"FEN: {fen}")

    #resultado esperado do FEN; 1K6/1PP5/P2RBBqP/4n3/Q7/p2b4/1pp3pp/1k2r3 w - - 0 1