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# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

init27

import argparse
import cv2
import numpy as np

image = cv2.imread("data/blur.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Original", image)

ap = argparse.ArgumentParser()

init27

import argparse
import cv2

image = cv2.imread("data/ResizedImage.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Original", image)

kernelSizes = (15, 15)
blurred = cv2.blur(image, kernelSizes)
cv2.imshow("Average (15, 15)", blurred)

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import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Original", image)