Dinesh Kumar Sarangapani dineshkumarsarangapani
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dineshkumarsarangapani
/ simplestatemachine.groovy
Created
January 19, 2024 07:41
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| class FileOperations { | |
| String currentState | |
| boolean isError | |
| String fileName | |
| FileOperations(initialState, isError = false) { | |
| currentState = initialState | |
| this.isError = isError | |
| } | |
dineshkumarsarangapani
/ watermark.py
Last active
January 19, 2024 08:59
for adding watermark to PDFs
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| # compatible with Python versions 2.6, 2.7, | |
| # and 3.2 - 3.5. (pip3 install pypdf4) | |
| from pathlib import Path | |
| from typing import Union, Literal, List | |
| from PyPDF2 import PdfWriter, PdfReader | |
| import PyPDF2 |
dineshkumarsarangapani
/ torchhub_train_openai.jsonl
Created
August 22, 2023 05:26
— forked from ValorLin/torchhub_train_openai.jsonl
torchhub_train_openai.jsonl
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| {"prompt":"I need an API that can estimate the relative depth of objects from a single image. The output should be an inverse depth map.\n\n###\n\n","completion":" {'domain': 'Classification', 'api_call': \"model = torch.hub.load('intel-isl\/MiDaS', 'DPT_Hybrid', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': \"Load the pretrained MiDaS model for estimating relative depth from a single image, which outputs an inverse depth map.\", 'code': 'import torch\nmodel = torch.hub.load(\"intel-isl\/MiDaS\", \"DPT_Hybrid\", pretrained=True)'}###"} | |
| {"prompt":"I need an API to detect different types of vehicles, people, and lanes in an image captured from a camera mounted on a self-driving car.\n\n###\n\n","completion":" {'domain': 'Object Detection', 'api_call': \"model = torch.hub.load('hustvl\/yolop', 'yolop', pretrained=True)\", 'api_provider': 'PyTorch', 'explanation': 'Load the YOLOP model from PyTorch Hub with pre-trained weights optimized for object detection, drivable area segmentation, and lane de |
dineshkumarsarangapani
/ min-char-rnn.py
Created
December 27, 2018 12:29
— forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
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| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |