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import { BedrockChat } from "@langchain/community/chat_models/bedrock";
import { StateGraph, END, START } from "@langchain/langgraph";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { HumanMessage, AIMessage, ToolMessage } from "@langchain/core/messages";
import { z } from "zod";
import { MessagesAnnotation, Annotation } from "@langchain/langgraph";
import { tool } from "@langchain/core/tools";

ghandic

from enum import auto
from typing import List, Optional
from collections import Counter

from fastapi_utils.enums import StrEnum
from fastapi_utils.inferring_router import InferringRouter
from pydantic import BaseModel
from fastapi import HTTPException, Query, Depends

router = InferringRouter()

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find . -regex '.*.py' -type f | sed "s/\.py$//" | xargs -I% mv -iv %.py %.txt

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"""This module requires:
    - poppler-utils [apt-get upde && apt-get install poppler-utils]
    
    - pdf2image [pip install pdf2image]
    - pillow [pip install pillow]
    - boto3 [pip install boto3]
"""

import boto3
from pdf2image import convert_from_bytes

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import boto3
from PIL import Image
from io import BytesIO
import os

class S3ImagesInvalidExtension(Exception):
    pass

class S3ImagesUploadFailed(Exception):
    pass

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from PIL import Image
import numpy as np
import cv2
import time

# Helper function for jupyter notebooks
def showarray(a):
    return Image.fromarray(a)

def detect_boxes(img, min_percent_of_area=0.02, max_percent_of_area=2):

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import boto3
import pandas as pd
from io import StringIO

class S3DataFrame(pd.DataFrame):
    
	"""
	# Make a dataframe and upload it as csv
	s3df = S3DataFrame({'h1':[1], 'h2':[2]})
	s3df.to_s3(Bucket='bucket-name', 

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## Tesseract4
# 2 images into tiff: 843 msec 796
# 10 images into tiff: 1003 msec
# 53 images (same as page 1)  into tiff: 1740 msec

## Tesseract3
# 2 images into tiff: 727 msec
# 10 images into tiff: 816 msec
# 53 images (same as page 1)  into tiff: 1620 msec

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from PIL import Image
from PIL.PngImagePlugin import PngInfo

im = Image.open('example.png')

im_info = PngInfo()
im_info.add_text('Key Information', 'Value Information')

im.save('example.png', pnginfo=im_info)

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import imageio
from typing import NamedTuple

import cv2
import numpy as np

LabeledGIFState = NamedTuple('LabeledGIFState', [('image', np.ndarray), ('label', str)])

class GifSettings(object):
    red = [255, 0, 0]