Small mods to the Nim source

Signed-off-by: Jacob Torrey <jacob@thinkst.com>

README.md

@@ -8,7 +8,7 @@ its training data to calculate the probability of each word given the preceeding
 the more high-probability tokens are more likely to be AI-originated. Techniques and tools in this repo are looking for
 faster approximation to be embeddable and more scalable.
 
-## LZMA compression detector (`lzma_detect.py`)
+## LZMA compression detector (`lzma_detect.py` and `nlzmadetect`)
 
 This is the first attempt, using the LZMA compression ratios as a way to indirectly measure the perplexity of a text.
 Compression ratios have been used in the past to [detect anomalies in network data](http://owncloud.unsri.ac.id/journal/security/ontheuse_compression_Network_anomaly_detec.pdf)

nlzmadetect/README.md

@@ -0,0 +1,10 @@
+# Nim package to classify test as LLM-generated
+
+This is a nim version of the LZMA detector written in Python. 
+
+## Instructions
+Build with `nimble build` optionally passing `-d:release` for more optimized output.
+
+Run `./nlzmadetect` with a filename to check (or multiple)
+
+Test against the samples repository with `nimble test`

nlzmadetect/src/nlzmadetect.nim

@@ -5,10 +5,10 @@ import strutils
 when isMainModule:
   import std/[parseopt, os]
 
-const PRELUDE_FILE = "../ai-generated.txt"
+const PRELUDE_FILE = "../../ai-generated.txt"
 const COMPRESSION_PRESET = 2.int32
 const SHORT_SAMPLE_THRESHOLD = 350
-var PRELUDE_STR = readFile(PRELUDE_FILE).convert("us-ascii", "UTF-8").replace(re"[^\x00-\x7F]")
+const PRELUDE_STR = staticRead(PRELUDE_FILE)
 
 proc compress_str(s : string, preset = COMPRESSION_PRESET): float64