Scezui commited on
Commit
e436546
1 Parent(s): b221808

cleaned repo, changed api

Browse files
Layoutlmv3_inference/ocr.py CHANGED
@@ -92,8 +92,7 @@ def run_tesseract_on_preprocessed_image(preprocessed_image, image_path):
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  url = "https://api.ocr.space/parse/image"
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  # Define the API key and the language
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- # api_key = "K88232854988957" # Replace with your actual OCR Space API key
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- api_key = "K88232854988957"
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  language = "eng"
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  # Save the preprocessed image
 
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  url = "https://api.ocr.space/parse/image"
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  # Define the API key and the language
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+ api_key = os.getenv("ocr_space")
 
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  language = "eng"
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  # Save the preprocessed image
exp2.ipynb DELETED
@@ -1,183 +0,0 @@
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# import the necessary packages\n",
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- "from imutils.perspective import four_point_transform\n",
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- "import pytesseract\n",
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- "import argparse\n",
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- "import imutils\n",
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- "import cv2\n",
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- "import re"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 13,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "usage: ipykernel_launcher.py [-h] -i INPUT [-d DEBUG]\n",
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- "ipykernel_launcher.py: error: the following arguments are required: -i/--input\n"
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- ]
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- },
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- {
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- "ename": "SystemExit",
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- "evalue": "2",
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- "output_type": "error",
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- "traceback": [
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- "An exception has occurred, use %tb to see the full traceback.\n",
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- "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
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- ]
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- }
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- ],
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- "source": [
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- "input= \"sample_711.jpg\"\n",
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- "\n",
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- "# Construct the argument parser\n",
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- "ap = argparse.ArgumentParser()\n",
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- "ap.add_argument(\"-i\", \"--input\", required=True,\n",
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- "\thelp=\"path to input receipt image\")\n",
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- "ap.add_argument(\"-d\", \"--debug\", type=int, default=-1,\n",
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- "\thelp=\"whether or not we are visualizing each step of the pipeline\")\n",
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- "\n",
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- "# Parse the arguments\n",
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- "args = vars(ap.parse_args())\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 14,
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- "metadata": {},
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- "outputs": [
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- {
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- "ename": "NameError",
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- "evalue": "name 'args' is not defined",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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- "Cell \u001b[1;32mIn[14], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# load the input image from disk, resize it, and compute the ratio\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# of the *new* width to the *old* width\u001b[39;00m\n\u001b[0;32m 3\u001b[0m image\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample_711.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 4\u001b[0m orig \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[43margs\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 5\u001b[0m image \u001b[38;5;241m=\u001b[39m orig\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 6\u001b[0m image \u001b[38;5;241m=\u001b[39m imutils\u001b[38;5;241m.\u001b[39mresize(image, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m)\n",
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- "\u001b[1;31mNameError\u001b[0m: name 'args' is not defined"
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- ]
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- }
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- ],
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- "source": [
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- "# load the input image from disk, resize it, and compute the ratio\n",
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- "# of the *new* width to the *old* width\n",
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- "image= \"sample_711.jpg\"\n",
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- "orig = cv2.imread(args[\"image\"])\n",
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- "image = orig.copy()\n",
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- "image = imutils.resize(image, width=500)\n",
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- "ratio = orig.shape[1] / float(image.shape[1])"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# convert the image to grayscale, blur it slightly, and then apply\n",
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- "# edge detection\n",
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- "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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- "blurred = cv2.GaussianBlur(gray, (5, 5,), 0)\n",
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- "edged = cv2.Canny(blurred, 75, 200)\n",
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- "# check to see if we should show the output of our edge detection\n",
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- "# procedure\n",
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- "if args[\"debug\"] > 0:\n",
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- "\tcv2.imshow(\"Input\", image)\n",
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- "\tcv2.imshow(\"Edged\", edged)\n",
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- "\tcv2.waitKey(0)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# find contours in the edge map and sort them by size in descending\n",
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- "# order\n",
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- "cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\n",
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- "\tcv2.CHAIN_APPROX_SIMPLE)\n",
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- "cnts = imutils.grab_contours(cnts)\n",
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- "cnts = sorted(cnts, key=cv2.contourArea, reverse=True)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# initialize a contour that corresponds to the receipt outline\n",
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- "receiptCnt = None\n",
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- "# loop over the contours\n",
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- "for c in cnts:\n",
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- "\t# approximate the contour\n",
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- "\tperi = cv2.arcLength(c, True)\n",
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- "\tapprox = cv2.approxPolyDP(c, 0.02 * peri, True)\n",
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- "\t# if our approximated contour has four points, then we can\n",
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- "\t# assume we have found the outline of the receipt\n",
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- "\tif len(approx) == 4:\n",
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- "\t\treceiptCnt = approx\n",
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- "\t\tbreak\n",
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- "# if the receipt contour is empty then our script could not find the\n",
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- "# outline and we should be notified\n",
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- "if receiptCnt is None:\n",
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- "\traise Exception((\"Could not find receipt outline. \"\n",
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- "\t\t\"Try debugging your edge detection and contour steps.\"))"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# check to see if we should draw the contour of the receipt on the\n",
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- "# image and then display it to our screen\n",
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- "if args[\"debug\"] > 0:\n",
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- "\toutput = image.copy()\n",
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- "\tcv2.drawContours(output, [receiptCnt], -1, (0, 255, 0), 2)\n",
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- "\tcv2.imshow(\"Receipt Outline\", output)\n",
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- "\tcv2.waitKey(0)\n",
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- "# apply a four-point perspective transform to the *original* image to\n",
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- "# obtain a top-down bird's-eye view of the receipt\n",
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- "receipt = four_point_transform(orig, receiptCnt.reshape(4, 2) * ratio)\n",
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- "# show transformed image\n",
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- "cv2.imshow(\"Receipt Transform\", imutils.resize(receipt, width=500))\n",
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- "cv2.waitKey(0)"
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- ]
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- }
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- ],
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- "metadata": {
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- "kernelspec": {
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- "display_name": "mlenv",
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- "language": "python",
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- "name": "python3"
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- },
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- "language_info": {
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- "codemirror_mode": {
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- "name": "ipython",
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- "version": 3
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.11.4"
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- }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 2
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
experiment.ipynb DELETED
@@ -1,1337 +0,0 @@
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 17,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "# defining inference parameters\n",
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- "model_path = r\"C:\\Users\\Ayoo\\Desktop\\webapp\\model\" # path to Layoutlmv3 model\n",
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- "imag_path = r\"C:\\Users\\Ayoo\\Desktop\\webapp\\predictions\\imgs\" # images folder"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 33,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "^C\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "2023-12-16 02:35:50.587274: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
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- "WARNING:tensorflow:From C:\\Users\\Ayoo\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
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- "\n",
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- "c:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\site-packages\\transformers\\modeling_utils.py:881: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
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- " warnings.warn(\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Preparing for Inference\n",
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- "Starting\n",
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- "Preprocessing\n",
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- "Preprocessing done. Running OCR\n",
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- "JSON file saved\n",
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- "OCR done\n",
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- "Run Done\n",
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- "Cleaned Tesseract output done\n",
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- "Word list done\n",
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- "Box list done\n",
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- "Prepared for Inference Batch\n",
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- "Running Flattened Output\n",
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- "Ready for Annotation\n",
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- "Annotating Images\n"
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- ]
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- }
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- ],
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- "source": [
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- "! python predictions\\inference\\run_inference.py --model_path {model_path} --images_path {imag_path}"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 20,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Looking for C:\\Users\\Ayoo\\.keras-ocr\\craft_mlt_25k.h5\n"
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- ]
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- },
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- {
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- "ename": "KeyboardInterrupt",
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- "evalue": "",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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- "Cell \u001b[1;32mIn[20], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkeras_ocr\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m pipeline\u001b[38;5;241m=\u001b[39m\u001b[43mkeras_ocr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
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- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\site-packages\\keras_ocr\\pipeline.py:20\u001b[0m, in \u001b[0;36mPipeline.__init__\u001b[1;34m(self, detector, recognizer, scale, max_size)\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, detector\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, recognizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, max_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2048\u001b[39m):\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m detector \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 20\u001b[0m detector \u001b[38;5;241m=\u001b[39m \u001b[43mdetection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recognizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 22\u001b[0m recognizer \u001b[38;5;241m=\u001b[39m recognition\u001b[38;5;241m.\u001b[39mRecognizer()\n",
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- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\site-packages\\keras_ocr\\detection.py:686\u001b[0m, in \u001b[0;36mDetector.__init__\u001b[1;34m(self, weights, load_from_torch, optimizer, backbone_name)\u001b[0m\n\u001b[0;32m 682\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[0;32m 683\u001b[0m pretrained_key \u001b[38;5;129;01min\u001b[39;00m PRETRAINED_WEIGHTS\n\u001b[0;32m 684\u001b[0m ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSelected weights configuration not found.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 685\u001b[0m weights_config \u001b[38;5;241m=\u001b[39m PRETRAINED_WEIGHTS[pretrained_key]\n\u001b[1;32m--> 686\u001b[0m weights_path \u001b[38;5;241m=\u001b[39m \u001b[43mtools\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_verify\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights_config\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43murl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights_config\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfilename\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[43m \u001b[49m\u001b[43msha256\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights_config\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msha256\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 690\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 691\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 692\u001b[0m weights_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\site-packages\\keras_ocr\\tools.py:527\u001b[0m, in \u001b[0;36mdownload_and_verify\u001b[1;34m(url, sha256, cache_dir, verbose, filename)\u001b[0m\n\u001b[0;32m 525\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m filepath)\n\u001b[0;32m 526\u001b[0m urllib\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39murlretrieve(url, filepath)\n\u001b[1;32m--> 527\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m sha256 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m sha256 \u001b[38;5;241m==\u001b[39m \u001b[43msha256sum\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\n\u001b[0;32m 529\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError occurred verifying sha256.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m filepath\n",
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- "import keras_ocr\n",
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1167
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1171
- " ('fes',\n",
1172
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1173
- " [ 631.39746, 3039.5918 ],\n",
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- " [ 631.39746, 3077.666 ],\n",
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- " [ 552.0762 , 3077.666 ]], dtype=float32)),\n",
1176
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1177
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1181
- " ('is',\n",
1182
- " array([[ 158.64258, 3137.9502 ],\n",
1183
- " [ 206.23535, 3137.9502 ],\n",
1184
- " [ 206.23535, 3185.543 ],\n",
1185
- " [ 158.64258, 3185.543 ]], dtype=float32)),\n",
1186
- " ('official',\n",
1187
- " array([[ 282.3838 , 3137.9502 ],\n",
1188
- " [ 456.89062, 3137.9502 ],\n",
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- " [ 456.89062, 3185.543 ],\n",
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1191
- " ('receift',\n",
1192
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1195
- " [ 472.75488, 3185.543 ]], dtype=float32)),\n",
1196
- " ('in',\n",
1197
- " array([[ 222.09961, 3141.123 ],\n",
1198
- " [ 269.69238, 3141.123 ],\n",
1199
- " [ 269.69238, 3182.37 ],\n",
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1201
- ]
1202
- },
1203
- "execution_count": 4,
1204
- "metadata": {},
1205
- "output_type": "execute_result"
1206
- }
1207
- ],
1208
- "source": [
1209
- "pipeline.recognize([r\"temp\\20230508_122035_preprocessed.png\"])"
1210
- ]
1211
- },
1212
- {
1213
- "cell_type": "code",
1214
- "execution_count": 5,
1215
- "metadata": {},
1216
- "outputs": [
1217
- {
1218
- "name": "stdout",
1219
- "output_type": "stream",
1220
- "text": [
1221
- "Requirement already satisfied: requests in c:\\users\\ayoo\\anaconda3\\envs\\mlenv\\lib\\site-packages (2.31.0)\n",
1222
- "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\ayoo\\anaconda3\\envs\\mlenv\\lib\\site-packages (from requests) (3.2.0)\n",
1223
- "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\ayoo\\anaconda3\\envs\\mlenv\\lib\\site-packages (from requests) (3.4)\n",
1224
- "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\ayoo\\anaconda3\\envs\\mlenv\\lib\\site-packages (from requests) (1.26.16)\n",
1225
- "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ayoo\\anaconda3\\envs\\mlenv\\lib\\site-packages (from requests) (2023.7.22)\n"
1226
- ]
1227
- }
1228
- ],
1229
- "source": [
1230
- "!pip install requests"
1231
- ]
1232
- },
1233
- {
1234
- "cell_type": "code",
1235
- "execution_count": 16,
1236
- "metadata": {},
1237
- "outputs": [
1238
- {
1239
- "name": "stdout",
1240
- "output_type": "stream",
1241
- "text": [
1242
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Columbia Tower', 'Words': [{'WordText': '7th', 'Left': 13.0, 'Top': 2116.0, 'Height': 36.0, 'Width': 72.0}, {'WordText': 'Floor', 'Left': 90.0, 'Top': 2116.0, 'Height': 36.0, 'Width': 126.0}, {'WordText': 'The', 'Left': 220.0, 'Top': 2116.0, 'Height': 36.0, 'Width': 76.0}, {'WordText': 'Columbia', 'Left': 301.0, 'Top': 2116.0, 'Height': 36.0, 'Width': 189.0}, {'WordText': 'Tower', 'Left': 495.0, 'Top': 2116.0, 'Height': 36.0, 'Width': 108.0}], 'MaxHeight': 36.0, 'MinTop': 2116.0}, {'LineText': 'Ortigas Avenue, Mandaluyong', 'Words': [{'WordText': 'Ortigas', 'Left': 33.0, 'Top': 2161.0, 'Height': 49.0, 'Width': 154.0}, {'WordText': 'Avenue', 'Left': 192.0, 'Top': 2161.0, 'Height': 49.0, 'Width': 159.0}, {'WordText': ',', 'Left': 192.0, 'Top': 2161.0, 'Height': 49.0, 'Width': 159.0}, {'WordText': 'Mandaluyong', 'Left': 358.0, 'Top': 2161.0, 'Height': 49.0, 'Width': 248.0}], 'MaxHeight': 49.0, 'MinTop': 2161.0}, {'LineText': 'City', 'Words': [{'WordText': 'City', 'Left': 29.0, 'Top': 2214.0, 'Height': 42.0, 'Width': 94.0}], 'MaxHeight': 42.0, 'MinTop': 2214.0}, {'LineText': 'TIN: 000-390-189-000', 'Words': [{'WordText': 'TIN', 'Left': 13.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 90.0}, {'WordText': ':', 'Left': 13.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 90.0}, {'WordText': '000', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '-', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '390', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '-', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '189', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '-', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}, {'WordText': '000', 'Left': 109.0, 'Top': 2266.0, 'Height': 46.0, 'Width': 331.0}], 'MaxHeight': 46.0, 'MinTop': 2266.0}, {'LineText': 'BIR ACCI #', 'Words': [{'WordText': 'BIR', 'Left': 10.0, 'Top': 2318.0, 'Height': 39.0, 'Width': 78.0}, {'WordText': 'ACCI', 'Left': 93.0, 'Top': 2318.0, 'Height': 39.0, 'Width': 98.0}, {'WordText': '#', 'Left': 195.0, 'Top': 2318.0, 'Height': 39.0, 'Width': 30.0}], 'MaxHeight': 40.0, 'MinTop': 2318.0}, {'LineText': '116-000390189-000346 19602', 'Words': [{'WordText': '116', 'Left': 33.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 430.0}, {'WordText': '-', 'Left': 33.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 430.0}, {'WordText': '000390189', 'Left': 33.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 430.0}, {'WordText': '-', 'Left': 33.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 430.0}, {'WordText': '000346', 'Left': 33.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 430.0}, {'WordText': '19602', 'Left': 468.0, 'Top': 2366.0, 'Height': 43.0, 'Width': 118.0}], 'MaxHeight': 43.0, 'MinTop': 2366.0}, {'LineText': 'AcciDate: 08/01/2020', 'Words': [{'WordText': 'AcciDate', 'Left': 13.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 194.0}, {'WordText': ':', 'Left': 13.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 194.0}, {'WordText': '08', 'Left': 212.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 266.0}, {'WordText': '/', 'Left': 213.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 266.0}, {'WordText': '01', 'Left': 212.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 266.0}, {'WordText': '/', 'Left': 213.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 266.0}, {'WordText': '2020', 'Left': 212.0, 'Top': 2419.0, 'Height': 42.0, 'Width': 266.0}], 'MaxHeight': 42.0, 'MinTop': 2419.0}, {'LineText': '07/31/2025', 'Words': [{'WordText': '07', 'Left': 29.0, 'Top': 2470.0, 'Height': 44.0, 'Width': 219.0}, {'WordText': '/', 'Left': 29.0, 'Top': 2470.0, 'Height': 44.0, 'Width': 219.0}, {'WordText': '31', 'Left': 29.0, 'Top': 2470.0, 'Height': 44.0, 'Width': 219.0}, {'WordText': '/', 'Left': 29.0, 'Top': 2470.0, 'Height': 44.0, 'Width': 219.0}, {'WordText': '2025', 'Left': 29.0, 'Top': 2470.0, 'Height': 44.0, 'Width': 219.0}], 'MaxHeight': 44.0, 'MinTop': 2470.0}, {'LineText': 'Permit #:', 'Words': [{'WordText': 'Permit', 'Left': 10.0, 'Top': 2526.0, 'Height': 40.0, 'Width': 142.0}, {'WordText': '#:', 'Left': 156.0, 'Top': 2527.0, 'Height': 39.0, 'Width': 46.0}], 'MaxHeight': 40.0, 'MinTop': 2526.0}, {'LineText': 'FP112018-074-0194656-00002', 'Words': [{'WordText': 'FP112018', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '-', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '074', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '-', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '0194656', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '-', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}, {'WordText': '00002', 'Left': 33.0, 'Top': 2572.0, 'Height': 39.0, 'Width': 554.0}], 'MaxHeight': 39.0, 'MinTop': 2572.0}, {'LineText': 'Get a chance to win a trip for', 'Words': [{'WordText': 'Get', 'Left': 29.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 78.0}, {'WordText': 'a', 'Left': 112.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 34.0}, {'WordText': 'chance', 'Left': 151.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 146.0}, {'WordText': 'to', 'Left': 302.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 54.0}, {'WordText': 'win', 'Left': 361.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 83.0}, {'WordText': 'a', 'Left': 448.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 34.0}, {'WordText': 'trip', 'Left': 487.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 107.0}, {'WordText': 'for', 'Left': 599.0, 'Top': 2679.0, 'Height': 39.0, 'Width': 69.0}], 'MaxHeight': 39.0, 'MinTop': 2679.0}, {'LineText': '2 to Korea when you buy PISO', 'Words': [{'WordText': '2', 'Left': 52.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 34.0}, {'WordText': 'to', 'Left': 91.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 58.0}, {'WordText': 'Korea', 'Left': 154.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 122.0}, {'WordText': 'when', 'Left': 281.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 102.0}, {'WordText': 'you', 'Left': 388.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 78.0}, {'WordText': 'buy', 'Left': 471.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 78.0}, {'WordText': 'PISO', 'Left': 554.0, 'Top': 2731.0, 'Height': 39.0, 'Width': 91.0}], 'MaxHeight': 39.0, 'MinTop': 2731.0}, {'LineText': 'worth of 7-Eleven items. Earn', 'Words': [{'WordText': 'worth', 'Left': 29.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 116.0}, {'WordText': 'of', 'Left': 152.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 61.0}, {'WordText': '7', 'Left': 219.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 184.0}, {'WordText': '-', 'Left': 219.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 184.0}, {'WordText': 'Eleven', 'Left': 219.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 184.0}, {'WordText': 'items', 'Left': 409.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 135.0}, {'WordText': '.', 'Left': 409.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 135.0}, {'WordText': 'Earn', 'Left': 550.0, 'Top': 2783.0, 'Height': 49.0, 'Width': 99.0}], 'MaxHeight': 49.0, 'MinTop': 2783.0}, {'LineText': '3 eRaffle entries when you buy', 'Words': [{'WordText': '3', 'Left': 29.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 34.0}, {'WordText': 'eRaffle', 'Left': 68.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 166.0}, {'WordText': 'entries', 'Left': 239.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 161.0}, {'WordText': 'when', 'Left': 404.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 102.0}, {'WordText': 'you', 'Left': 512.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 78.0}, {'WordText': 'buy', 'Left': 594.0, 'Top': 2832.0, 'Height': 39.0, 'Width': 70.0}], 'MaxHeight': 39.0, 'MinTop': 2832.0}, {'LineText': 'discounted booster Items. Per', 'Words': [{'WordText': 'discounted', 'Left': 33.0, 'Top': 2888.0, 'Height': 42.0, 'Width': 221.0}, {'WordText': 'booster', 'Left': 259.0, 'Top': 2888.0, 'Height': 42.0, 'Width': 168.0}, {'WordText': 'Items', 'Left': 432.0, 'Top': 2888.0, 'Height': 42.0, 'Width': 137.0}, {'WordText': '.', 'Left': 432.0, 'Top': 2888.0, 'Height': 42.0, 'Width': 137.0}, {'WordText': 'Per', 'Left': 574.0, 'Top': 2888.0, 'Height': 42.0, 'Width': 68.0}], 'MaxHeight': 42.0, 'MinTop': 2888.0}, {'LineText': 'DTI FAIR TRADE Permit Number:', 'Words': [{'WordText': 'DTI', 'Left': 29.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 78.0}, {'WordText': 'FAIR', 'Left': 113.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 100.0}, {'WordText': 'TRADE', 'Left': 218.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 121.0}, {'WordText': 'Permit', 'Left': 344.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 142.0}, {'WordText': 'Number', 'Left': 491.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 151.0}, {'WordText': ':', 'Left': 491.0, 'Top': 2933.0, 'Height': 42.0, 'Width': 151.0}], 'MaxHeight': 42.0, 'MinTop': 2933.0}, {'LineText': '163019 Series of 2023..', 'Words': [{'WordText': '163019', 'Left': 117.0, 'Top': 2988.0, 'Height': 40.0, 'Width': 135.0}, {'WordText': 'Series', 'Left': 257.0, 'Top': 2988.0, 'Height': 40.0, 'Width': 145.0}, {'WordText': 'of', 'Left': 407.0, 'Top': 2988.0, 'Height': 40.0, 'Width': 55.0}, {'WordText': '2023', 'Left': 467.0, 'Top': 2988.0, 'Height': 40.0, 'Width': 165.0}, {'WordText': '..', 'Left': 467.0, 'Top': 2988.0, 'Height': 40.0, 'Width': 165.0}], 'MaxHeight': 40.0, 'MinTop': 2988.0}, {'LineText': 'facebook.com/711philippines.', 'Words': [{'WordText': 'facebook.com', 'Left': 52.0, 'Top': 3037.0, 'Height': 42.0, 'Width': 590.0}, {'WordText': '/', 'Left': 52.0, 'Top': 3037.0, 'Height': 42.0, 'Width': 590.0}, {'WordText': '711philippines', 'Left': 52.0, 'Top': 3037.0, 'Height': 42.0, 'Width': 590.0}, {'WordText': '.', 'Left': 52.0, 'Top': 3037.0, 'Height': 42.0, 'Width': 590.0}], 'MaxHeight': 42.0, 'MinTop': 3037.0}, {'LineText': '- THIS IS AN OFFICIAL RECEIPT -', 'Words': [{'WordText': '-', 'Left': 0.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 46.0}, {'WordText': 'THIS', 'Left': 52.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 98.0}, {'WordText': 'IS', 'Left': 155.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 57.0}, {'WordText': 'AN', 'Left': 219.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 52.0}, {'WordText': 'OFFICIAL', 'Left': 276.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 184.0}, {'WordText': 'RECEIPT', 'Left': 466.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 167.0}, {'WordText': '-', 'Left': 638.0, 'Top': 3138.0, 'Height': 46.0, 'Width': 30.0}], 'MaxHeight': 46.0, 'MinTop': 3138.0}], 'HasOverlay': True}, 'TextOrientation': '0', 'FileParseExitCode': 1, 'ParsedText': '7-ELEVEN.\\nNHJ Convenience Store\\nOwned & Operated by: Nancy A.\\nClimacosa\\nVATREGTIN #933-598-685-002\\nPoblacion, Leon, Iloilo,\\nPhilippines\\nlel #: NULL\\n05/01/2023 (Mon) 23:00:57\\nRCPT #2481347\\nROPT CNTHO\\nSTORE#3058\\nSN# :XTI43170\\nMIN #: 18112011091411051\\nSTAFF: Angelica Duante\\n7FKoreanßun\\nNissinYaSaBeet77g\\nBBHOTDOGCREMYCHEES\\n39.00 Х 6\\nchocvron? in1Ch020g\\n15.00 X\\n2\\n55.004\\n40.000\\n234.000\\n30.000\\nTotal (10)\\nCASH\\nCHANGE\\n359.00\\n1000.00\\n641.00\\nVATable\\nVAT_Tax\\nZero_Rated\\nVAT_Exempted\\n320.54\\n38.46\\n0.00\\n0.00\\nSold To: 9906087698684\\nName:\\nAddress:\\nTIN:\\nPhilippine Seven Corporation\\n7th Floor The Columbia Tower\\nOrtigas Avenue, Mandaluyong\\nCity\\nTIN: 000-390-189-000\\nBIR ACCI #\\n116-000390189-000346 19602\\nAcciDate: 08/01/2020\\n07/31/2025\\nPermit #:\\nFP112018-074-0194656-00002\\nGet a chance to win a trip for\\n2 to Korea when you buy PISO\\nworth of 7-Eleven items. Earn\\n3 eRaffle entries when you buy\\ndiscounted booster Items. Per\\nDTI FAIR TRADE Permit Number:\\n163019 Series of 2023..\\nfacebook.com/711philippines.\\n- THIS IS AN OFFICIAL RECEIPT -', 'ErrorMessage': '', 'ErrorDetails': ''}], 'OCRExitCode': 1, 'IsErroredOnProcessing': False, 'ProcessingTimeInMilliseconds': '2593', 'SearchablePDFURL': 'Searchable PDF not generated as it was not requested.'}\n"
1243
- ]
1244
- }
1245
- ],
1246
- "source": [
1247
- "# Import requests library\n",
1248
- "import requests\n",
1249
- "\n",
1250
- "# Define the OCR API endpoint\n",
1251
- "url = \"https://api.ocr.space/parse/image\"\n",
1252
- "\n",
1253
- "# Define the API key and the language\n",
1254
- "api_key = \"K88232854988957\"\n",
1255
- "language = \"eng\"\n",
1256
- "\n",
1257
- "# Define the image file path\n",
1258
- "image_file = r\"C:\\Users\\Ayoo\\Desktop\\webapp\\predictions\\imgs\\20230508_122035.jpg\"\n",
1259
- "\n",
1260
- "# Open the image file as binary\n",
1261
- "with open(image_file, \"rb\") as f:\n",
1262
- " # Define the payload for the API request\n",
1263
- " payload = {\n",
1264
- " \"apikey\": api_key,\n",
1265
- " \"language\": language,\n",
1266
- " \"isOverlayRequired\": True, # Optional, set to True if you want the coordinates of the words\n",
1267
- " \"OCREngine\": 2 # OCR Engine 2 for Layoutlmv3\n",
1268
- " }\n",
1269
- " # Define the file parameter for the API request\n",
1270
- " file = {\n",
1271
- " \"file\": f\n",
1272
- " }\n",
1273
- " # Send the POST request to the OCR API\n",
1274
- " response = requests.post(url, data=payload, files=file)\n",
1275
- "\n",
1276
- "# Check the status code of the response\n",
1277
- "if response.status_code == 200:\n",
1278
- " # Parse the JSON response\n",
1279
- " result = response.json()\n",
1280
- " # Print the parsed text\n",
1281
- " print(result)\n",
1282
- "else:\n",
1283
- " # Print the error message\n",
1284
- " print(\"Error: \" + response.text)\n"
1285
- ]
1286
- },
1287
- {
1288
- "cell_type": "code",
1289
- "execution_count": 13,
1290
- "metadata": {},
1291
- "outputs": [
1292
- {
1293
- "ename": "TypeError",
1294
- "evalue": "Object of type Response is not JSON serializable",
1295
- "output_type": "error",
1296
- "traceback": [
1297
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
1298
- "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
1299
- "Cell \u001b[1;32mIn[13], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Assuming 'response' is the JSON response from the OCR API\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m)\n",
1300
- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\json\\__init__.py:238\u001b[0m, in \u001b[0;36mdumps\u001b[1;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 233\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONEncoder\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 235\u001b[0m \u001b[43m \u001b[49m\u001b[43mskipkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_ascii\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_ascii\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 236\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_circular\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_circular\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 237\u001b[0m \u001b[43m \u001b[49m\u001b[43mseparators\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseparators\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m--> 238\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
1301
- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\json\\encoder.py:202\u001b[0m, in \u001b[0;36mJSONEncoder.encode\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 200\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterencode(o, _one_shot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chunks, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m--> 202\u001b[0m chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(chunks)\n\u001b[0;32m 203\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(chunks)\n",
1302
- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\json\\encoder.py:439\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[1;34m(o, _current_indent_level)\u001b[0m\n\u001b[0;32m 437\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCircular reference detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 438\u001b[0m markers[markerid] \u001b[38;5;241m=\u001b[39m o\n\u001b[1;32m--> 439\u001b[0m o \u001b[38;5;241m=\u001b[39m \u001b[43m_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 440\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[0;32m 441\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m markers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
1303
- "File \u001b[1;32mc:\\Users\\Ayoo\\anaconda3\\envs\\mlenv\\Lib\\json\\encoder.py:180\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[1;34m(self, o)\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault\u001b[39m(\u001b[38;5;28mself\u001b[39m, o):\n\u001b[0;32m 162\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;124;03m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[0;32m 164\u001b[0m \u001b[38;5;124;03m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 178\u001b[0m \n\u001b[0;32m 179\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 180\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mObject of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 181\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not JSON serializable\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
1304
- "\u001b[1;31mTypeError\u001b[0m: Object of type Response is not JSON serializable"
1305
- ]
1306
- }
1307
- ],
1308
- "source": [
1309
- "import json\n",
1310
- "\n",
1311
- "# Assuming 'response' is the JSON response from the OCR API\n",
1312
- "print(json.dumps(response, indent=4))\n"
1313
- ]
1314
- }
1315
- ],
1316
- "metadata": {
1317
- "kernelspec": {
1318
- "display_name": "mlenv",
1319
- "language": "python",
1320
- "name": "python3"
1321
- },
1322
- "language_info": {
1323
- "codemirror_mode": {
1324
- "name": "ipython",
1325
- "version": 3
1326
- },
1327
- "file_extension": ".py",
1328
- "mimetype": "text/x-python",
1329
- "name": "python",
1330
- "nbconvert_exporter": "python",
1331
- "pygments_lexer": "ipython3",
1332
- "version": "3.11.5"
1333
- }
1334
- },
1335
- "nbformat": 4,
1336
- "nbformat_minor": 2
1337
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # 3058,BHu Convenience,"Poblacion , Leon , Iloila .",05 / 00 / 2023,06 : 54 : 23,NESTEALEMICETSDOML,35.000,108,4.00 67.86
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- 000036410,BVSU Multi Purpose Cooperative,Luna Street Lapaz Iloslo City,10 - 25 - 2023,01 : 29 : 49 PM,COXE 195ML,13.00,13.00,
 
 
 
 
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- # 3058,BHu Convenience,"Poblacion , Leon , Iloila .",05 / 00 / 2023,06 : 54 : 23,NESTEALEMICETSDOML,35.000,108,4.00 67.86
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5
- 000036410,BVSU Multi Purpose Cooperative,Luna Street Lapaz Iloslo City,10 - 25 - 2023,01 : 29 : 49 PM,COXE,13.00,13.00,
6
- 000036410,BVSU Multi Purpose Cooperative,Luna Street Lapaz Iloslo City,10 - 25 - 2023,01 : 29 : 49 PM,COXE 195ML,13.00,13.00,
 
 
 
 
 
 
 
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{"WordText": "ILOILO", "Left": 476.0, "Top": 1728.0, "Height": 46.0, "Width": 134.0}], "MaxHeight": 46.0, "MinTop": 1728.0}, {"LineText": "TIN M", "Words": [{"WordText": "TIN", "Left": 13.0, "Top": 1780.0, "Height": 43.0, "Width": 70.0}, {"WordText": "M", "Left": 88.0, "Top": 1780.0, "Height": 43.0, "Width": 40.0}], "MaxHeight": 43.0, "MinTop": 1780.0}, {"LineText": ":944185225-000", "Words": [{"WordText": ":", "Left": 184.0, "Top": 1780.0, "Height": 46.0, "Width": 302.0}, {"WordText": "944185225", "Left": 184.0, "Top": 1780.0, "Height": 46.0, "Width": 302.0}, {"WordText": "-", "Left": 184.0, "Top": 1780.0, "Height": 46.0, "Width": 302.0}, {"WordText": "000", "Left": 184.0, "Top": 1780.0, "Height": 46.0, "Width": 302.0}], "MaxHeight": 46.0, "MinTop": 1780.0}, {"LineText": "Accred. #: 00000000000", "Words": [{"WordText": "Accred", "Left": 13.0, "Top": 1833.0, "Height": 46.0, "Width": 144.0}, {"WordText": ".", "Left": 13.0, "Top": 1833.0, "Height": 46.0, "Width": 144.0}, {"WordText": "#:", "Left": 163.0, "Top": 1833.0, "Height": 46.0, "Width": 40.0}, {"WordText": "00000000000", "Left": 209.0, "Top": 1833.0, "Height": 46.0, "Width": 234.0}], "MaxHeight": 46.0, "MinTop": 1833.0}, {"LineText": "05/08/2015-07/31/2025", "Words": [{"WordText": "05", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "/", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "08", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "/", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "2015", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "-", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "07", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "/", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "31", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "/", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}, {"WordText": "2025", "Left": 13.0, "Top": 1886.0, "Height": 46.0, "Width": 449.0}], "MaxHeight": 46.0, "MinTop": 1886.0}, {"LineText": "Pernit #:FP0-0000000-00000", "Words": [{"WordText": "Pernit", "Left": 10.0, "Top": 1938.0, "Height": 49.0, "Width": 141.0}, {"WordText": "#:", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}, {"WordText": "FP0", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}, {"WordText": "-", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}, {"WordText": "0000000", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}, {"WordText": "-", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}, {"WordText": "00000", "Left": 157.0, "Top": 1938.0, "Height": 49.0, "Width": 410.0}], "MaxHeight": 49.0, "MinTop": 1938.0}, {"LineText": "THANK YOU", "Words": [{"WordText": "THANK", "Left": 423.0, "Top": 1991.0, "Height": 43.0, "Width": 121.0}, {"WordText": "YOU", "Left": 549.0, "Top": 1991.0, "Height": 43.0, "Width": 71.0}], "MaxHeight": 43.0, "MinTop": 1991.0}, {"LineText": "PLEASE COME AGAIM", "Words": [{"WordText": "PLEASE", "Left": 308.0, "Top": 2043.0, "Height": 43.0, "Width": 172.0}, {"WordText": "COME", "Left": 485.0, "Top": 2043.0, "Height": 43.0, "Width": 97.0}, {"WordText": "AGAIM", "Left": 587.0, "Top": 2043.0, "Height": 43.0, "Width": 118.0}], "MaxHeight": 43.0, "MinTop": 2043.0}, {"LineText": "THIS SERVE AS AN OFFICIAL RECEIPE", "Words": [{"WordText": "THIS", "Left": 171.0, "Top": 2092.0, "Height": 50.0, "Width": 94.0}, {"WordText": "SERVE", "Left": 271.0, "Top": 2092.0, "Height": 50.0, "Width": 119.0}, {"WordText": "AS", "Left": 396.0, "Top": 2092.0, "Height": 50.0, "Width": 56.0}, {"WordText": "AN", "Left": 459.0, "Top": 2092.0, "Height": 50.0, "Width": 56.0}, {"WordText": "OFFICIAL", "Left": 521.0, "Top": 2092.0, "Height": 50.0, "Width": 188.0}, {"WordText": "RECEIPE", "Left": 715.0, "Top": 2092.0, "Height": 50.0, "Width": 158.0}], "MaxHeight": 50.0, "MinTop": 2092.0}], "HasOverlay": true}, "TextOrientation": "0", "FileParseExitCode": 1, "ParsedText": "BVSU Multi Purpose Cooperative\nLuna Street Lapaz\nIloslo City\nBy: WVSU MPC\nVAT TIN NO. :000-000-000-000\nSerial. No. WIES7WS\nMach ID: 000000000000000000\n000036410\n10-25-2023\nTime\ngampay\nInvoice d\nDate\nCashier\nSold TO:\nBus. Name:\nTIN:\nAddress:\nQTV\nDESC\nCOXE 195ML\nNet Of VAT\nTotal Que\nCash Payment\nSold To:\nBus. Name:\nTIN:\nAddress:\n01:29:49 PM\nTotal\n13.00\n18.61\n13.00\n20.00\nVAT Amt\nTotal Que\nChange\n7.00\nTr 8: 000036513\nSUPPLIER : AVAMICA COMPUTER TECHNOLOGIES\nADDRESS :BLK. 20 LOT 55 DECA HOMES\nPANDAC PAVIA ILOILO\nTIN M\n:944185225-000\nAccred. #: 00000000000\n05/08/2015-07/31/2025\nPernit #:FP0-0000000-00000\nTHANK YOU\nPLEASE COME AGAIM\nTHIS SERVE AS AN OFFICIAL RECEIPE", "ErrorMessage": "", "ErrorDetails": ""}], "OCRExitCode": 1, "IsErroredOnProcessing": false, "ProcessingTimeInMilliseconds": "656", "SearchablePDFURL": "Searchable PDF not generated as it was not requested."}
 
 
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output_folders/20240517_115717/uploads/receipt_19.jpg DELETED
Binary file (611 kB)
 
output_folders/20240517_115717/uploads/sample_711.jpg DELETED
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packages.txt DELETED
@@ -1 +0,0 @@
1
- python3-opencv
 
 
requirements+old.txt DELETED
@@ -1,255 +0,0 @@
1
-
2
-
3
-
4
- absl-py==2.0.0
5
- aiofiles==23.2.1
6
- aiohttp==3.8.5
7
- aiosignal==1.3.1
8
- altair==5.1.1
9
- annotated-types==0.6.0
10
- anyio==3.7.1
11
- argon2-cffi==23.1.0
12
- argon2-cffi-bindings==21.2.0
13
- asttokens==2.2.1
14
- astunparse==1.6.3
15
- async-timeout==4.0.3
16
- attrs==23.1.0
17
- Babel==2.12.1
18
- backcall==0.2.0
19
- # backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work
20
- beautifulsoup4==4.12.2
21
- bleach==6.0.0
22
- blinker==1.6.2
23
- blis==0.7.11
24
- Bottleneck==1.3.7
25
- bs4==0.0.1
26
- # cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
27
- cachetools==5.3.1
28
- catalogue==2.0.10
29
- certifi==2023.7.22
30
- cffi==1.15.1
31
- charset-normalizer==3.2.0
32
- click==8.1.7
33
- cloudpathlib==0.16.0
34
- colabcode==0.3.0
35
- colabconnect @ git+https://github.com/amitness/colab-connect.git@a196621de80d6eb1b73b5c8261cc223c96bae479
36
- colorama==0.4.6
37
- comm==0.1.3
38
- confection==0.1.4
39
- contourpy==1.1.0
40
- cycler==0.11.0
41
- cymem==2.0.8
42
- datasets @ git+https://github.com/huggingface/datasets.git@a1e1867e932f14233244fb25713f3c94c46ff50a
43
- debugpy==1.6.7
44
- decorator==5.1.1
45
- defusedxml==0.7.1
46
- dill==0.3.4
47
- editdistance==0.6.2
48
- efficientnet==1.0.0
49
- essential-generators==1.0
50
- et-xmlfile==1.1.0
51
- exceptiongroup==1.1.3
52
- executing==1.2.0
53
- fastai==1.0.61
54
- fastapi==0.109.2
55
- fastcore==1.5.29
56
- fastdownload==0.0.7
57
- fastjsonschema==2.18.0
58
- fastprogress==1.0.3
59
- ffmpy==0.3.2
60
- filelock==3.12.2
61
- Flask==2.3.3
62
- Flask-Cors==4.0.0
63
- flatbuffers==23.5.26
64
- fonttools==4.42.1
65
- frozenlist==1.4.0
66
- fsspec==2023.6.0
67
- gast==0.4.0
68
- gitdb==4.0.10
69
- GitPython==3.1.37
70
- google-auth==2.23.0
71
- google-auth-oauthlib==1.0.0
72
- google-pasta==0.2.0
73
- gradio==4.18.0
74
- gradio_client==0.10.0
75
- grpcio==1.58.0
76
- h11==0.14.0
77
- # h5py==3.9.0
78
- httpcore==1.0.2
79
- httpx==0.26.0
80
- huggingface-hub==0.20.3
81
- idna==3.4
82
- imageio==2.31.3
83
- imgaug==0.4.0
84
- # importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1688754491823/work
85
- importlib-resources==6.1.1
86
- imutils==0.5.4
87
- ipykernel==6.24.0
88
- ipython==8.14.0
89
- ipython-genutils==0.2.0
90
- itsdangerous==2.1.2
91
- jedi==0.18.2
92
- Jinja2==3.1.2
93
- joblib==1.3.2
94
- json5==0.9.14
95
- jsonify==0.5
96
- jsonschema==4.19.1
97
- jsonschema-specifications==2023.7.1
98
- jupyter-server==1.24.0
99
- jupyter_client==8.3.0
100
- jupyter_core==5.3.1
101
- jupyterlab==3.0.7
102
- jupyterlab-pygments==0.2.2
103
- jupyterlab_server==2.25.0
104
- keras==2.15.0
105
- Keras-Applications==1.0.8
106
- keras-ocr==0.9.3
107
- kiwisolver==1.4.5
108
- langcodes==3.3.0
109
- lazy_loader==0.3
110
- libclang==16.0.6
111
- Markdown==3.4.4
112
- markdown-it-py==3.0.0
113
- MarkupSafe==2.1.3
114
- matplotlib==3.7.2
115
- matplotlib-inline==0.1.6
116
- mdurl==0.1.2
117
- mistune==3.0.1
118
- ml-dtypes==0.2.0
119
- mpmath==1.3.0
120
- # mtcnn @ file:///home/conda/feedstock_root/build_artifacts/mtcnn_1629221401070/work
121
- multidict==6.0.4
122
- multiprocess==0.70.12.2
123
- murmurhash==1.0.10
124
- nbclassic==0.3.7
125
- nbclient==0.8.0
126
- nbconvert==7.8.0
127
- nbformat==5.9.2
128
- nest-asyncio==1.4.3
129
- networkx==3.1
130
- notebook==6.4.5
131
- notebook_shim==0.2.3
132
- npm==0.1.1
133
- numexpr==2.8.8
134
- numpy==1.24.3
135
- nvidia-ml-py3==7.352.0
136
- oauthlib==3.2.2
137
- opencv-python==4.8.1.78
138
- openpyxl==3.1.2
139
- opt-einsum==3.3.0
140
- optional-django==0.1.0
141
- orjson==3.9.13
142
- outcome==1.2.0
143
- packaging==23.1
144
- pandas==2.0.3
145
- pandocfilters==1.5.0
146
- parso==0.8.3
147
- pickleshare==0.7.5
148
- Pillow==9.5.0
149
- # platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1690813113769/work
150
- preshed==3.0.9
151
- prometheus-client==0.17.1
152
- prompt-toolkit==3.0.39
153
- protobuf==4.23.4
154
- psutil==5.9.5
155
- pure-eval==0.2.2
156
- pyarrow==13.0.0
157
- pyasn1==0.5.0
158
- pyasn1-modules==0.3.0
159
- pyclipper==1.3.0.post5
160
- pycparser==2.21
161
- pydantic==2.5.2
162
- pydantic_core==2.14.5
163
- pydeck==0.8.1b0
164
- pydicom==2.4.3
165
- pydub==0.25.1
166
- Pygments==2.15.1
167
- pyngrok==7.0.0
168
- pyparsing==3.0.9
169
- PySocks==1.7.1
170
- pytesseract==0.3.10
171
- python-dateutil==2.8.2
172
- python-multipart==0.0.9
173
- pytz==2023.3
174
- # pywin32==306
175
- pywinpty==2.0.11
176
- # PyYAML @ file:///D:/bld/pyyaml_1695373635661/work
177
- pyzmq==25.1.0
178
- referencing==0.30.2
179
- regex==2023.8.8
180
- requests==2.31.0
181
- requests-oauthlib==1.3.1
182
- rich==13.5.3
183
- rpds-py==0.10.3
184
- rsa==4.9
185
- ruff==0.2.1
186
- safetensors==0.3.2
187
- scikit-image==0.22.0
188
- scikit-learn==1.3.2
189
- scipy==1.11.2
190
- seaborn==0.12.2
191
- selenium==4.12.0
192
- semantic-version==2.10.0
193
- Send2Trash==1.8.2
194
- seqeval==1.2.2
195
- shapely==2.0.2
196
- shellingham==1.5.4
197
- six==1.16.0
198
- smart-open==6.4.0
199
- smmap==5.0.1
200
- sniffio==1.3.0
201
- sortedcontainers==2.4.0
202
- soupsieve==2.5
203
- spacy==3.7.2
204
- spacy-legacy==3.0.12
205
- spacy-loggers==1.0.5
206
- srsly==2.4.8
207
- stack-data==0.6.2
208
- starlette==0.36.3
209
- streamlit==1.27.0
210
- sympy==1.12
211
- tenacity==8.2.3
212
- tensorboard==2.15.1
213
- tensorboard-data-server==0.7.1
214
- tensorflow==2.15.0
215
- tensorflow-estimator==2.15.0
216
- tensorflow-intel==2.15.0
217
- tensorflow-io-gcs-filesystem==0.31.0
218
- termcolor==2.3.0
219
- terminado==0.17.1
220
- thinc==8.2.2
221
- threadpoolctl==3.2.0
222
- tifffile==2023.12.9
223
- tinycss2==1.2.1
224
- tokenizers==0.13.3
225
- toml==0.10.2
226
- tomlkit==0.12.0
227
- toolz==0.12.0
228
- torch==2.1.1
229
- torchvision==0.16.1
230
- tornado==6.3.2
231
- tqdm==4.66.1
232
- traitlets==5.9.0
233
- transformers==4.31.0
234
- trio==0.22.2
235
- trio-websocket==0.10.4
236
- typer==0.9.0
237
- typing_extensions==4.9.0
238
- tzdata==2023.3
239
- tzlocal==5.0.1
240
- urllib3==1.26.16
241
- uvicorn==0.27.1
242
- validators==0.22.0
243
- wasabi==1.1.2
244
- watchdog==3.0.0
245
- wcwidth==0.2.6
246
- weasel==0.3.4
247
- webencodings==0.5.1
248
- websocket-client==1.6.3
249
- websockets==11.0.3
250
- Werkzeug==2.3.7
251
- wrapt==1.14.1
252
- wsproto==1.2.0
253
- xxhash==3.3.0
254
- yarl==1.9.2
255
- # zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1689374466814/work
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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