secemp9’s gists

secemp9 / openai_toolcall.py

Created July 23, 2025 00:17

openai toolcalling test

	import requests
	import json
	import time
	import copy
	from enum import Enum
	from typing import Dict, List, Any, Optional, Union, Iterator
	from dataclasses import dataclass, field

	class ModelErrorType(Enum):
	MAX_LENGTH = "1024"

secemp9 / openrouter_toolcall.py

Created July 22, 2025 02:29

openrouter tool calling verify

	import requests
	import json
	import os
	import subprocess
	import time
	from typing import Dict, List, Any, Optional
	import uuid
	import shutil

	class ToolCallingTester:

secemp9 / gist:837164556b0f0631297ea1a554755a86

Created June 10, 2025 07:49

<output_sequence function="x(i)=2(i)+2" i_start="0" i_end="10000" count="10001">

2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 4

secemp9 / vlm_token_vis_moondream.py

Created May 31, 2025 20:53

attempt at reproducing https://www.groundlight.ai/blog/how-vlm-works-tokens 's interactive vlm demo to visualize tokens on their patch/predicted region on an image

	import torch, torch.nn.functional as F
	from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
	from PIL import Image

	REVISION = "2025-04-14" # lock to a known good tag
	MODEL_ID = "vikhyatk/moondream2"
	device = "cuda" # or "cpu" / bitsandbytes / GGUF, etc.

	# 1️⃣ load model + text tokenizer
	model = AutoModelForCausalLM.from_pretrained(

secemp9 / vlm_token_vis_llava.py

Created May 31, 2025 20:52

attempt at reproducing https://www.groundlight.ai/blog/how-vlm-works-tokens 's interactive vlm demo to visualize tokens on their patch/predicted region on an image

	import torch, torch.nn.functional as F
	from transformers import AutoProcessor, LlavaForConditionalGeneration
	from PIL import Image

	MODEL_ID = "llava-hf/llava-1.5-7b-hf"
	device = "cuda"

	model = LlavaForConditionalGeneration.from_pretrained(
	MODEL_ID, torch_dtype=torch.float16, device_map="auto")
	processor = AutoProcessor.from_pretrained(MODEL_ID)

secemp9 / rotation_invariant_cnn.py

Created May 31, 2025 04:21

rotation invariant CNN implementation

	# -- coding: utf-8 --
	"""
	CyCNN implementation for rotation invariant image classification.
	Based on "CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers"
	"""

	# Core Libraries
	import os
	import random
	import time

secemp9 / testchoice.html

Created May 20, 2025 01:16



	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Chips Filter Demo</title>
	<style>
	body {

secemp9 / autocolor_ps.py

Created May 6, 2025 23:54

autocolor (photoshop) inspired algorithm

	def improved_auto_color(image: Image.Image) -> Image.Image:
	"""
	Improved version of Photoshop-like Auto Color.
	It works by:
	1. Converting to float and normalizing.
	2. Computing per-channel percentiles (e.g., 0.5% and 99.5%).
	3. Stretching values between those percentiles to [0, 255].
	4. This avoids over-amplifying noise or outliers.
	"""
	img_array = np.array(image).astype(np.float32)

secemp9 / gemini_cli.py

Created May 4, 2025 22:03

woa gemini

	#!/usr/bin/env python3
	import sys
	import os
	from google import genai
	from google.genai import types
	from PIL import Image
	import readline # For command history

	def print_usage():
	"""Print usage instructions."""

secemp9 / plot_claude.py

Created April 14, 2025 11:57

	import json
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	from collections import Counter
	from datetime import datetime, timedelta
	import seaborn as sns
	import matplotlib.dates as mdates
	from matplotlib.gridspec import GridSpec
	import re

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