Posts

Python learnings #2 - apache mod-python

This tells you how to install mod_python so that you can code for the web in python, in other words, stay in one language for the backend and frontend.   $ sudo apt-get update $ sudo apt-get install libapache2-mod-python libapache2-mod-python-doc Configure mod_python You need to create directory to host your python scripts assuming that your root html directory is /var/www. Type the following command: $ sudo mkdir /var/www/py $ sudo chown yourname:www-data /var/www/py Now, open /etc/apache2/sites-available/default and add following code: AddHandler mod_python .py PythonHandler hello PythonDebug On Note that PythonHandler is hello which is hello.py. Save and close the file. Restart Apache2 server. /etc/init.d/apache2 restart $ vi /var/www/py/hello.py The following simple mod_python program serves as a good test: import sys sys.path.append('/var/www/html') from mod_python import apache def handler(req): req.log_error('handler') re...
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Building my own LLM

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Yesterday I started building my own LLM because I want to avoid licensing issues for basic chatbot usages. It's super slow training these things. I won't post code here because I plan to commercialise it, but here's the progress screenshot: I've set it to use the following parameters. Sadly my GPU is rather small so it is taking ages. # Training hyperparameters TEMPERATURE=0.7 TOP_K=40 TOP_P=0.95 REPEAT_PENALTY=1.1 MAX_NEW_TOKENS=200 BLOCK_SIZE=32 BATCH_SIZE=8 NUM_EPOCHS=5 LEARNING_RATE=0.001 CHECKPOINT_INTERVAL=1 USE_GPU=true # File and directory settings MODEL_DIR=models/ CORPUS_DIR=repo/ LOG_DIR=logs/ BACKUP_DIR=bak/ TOKENIZER_PATH= # Internal settings EVAL_INTERVAL=100 EVAL_ITERS=100 SEED=1337 INPUT_FILE=input.txt
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Update on what ChatGPT is bad at doing.

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Intro I've experimented now for about a year with giving ChatGPT tasks. The below criticisms apply to ChatGPT 4o . I recently got access to ChatGPT 4.5 which is better at coding and following instructions (it at least takes the instruction to give steps one at a time, seriously, whereas ChatGPT 4o almost always ignores that). However, 4.5 is more grumpy in tone. I found o1 and o3 not great either, so I keep going back to 4o. Anyway, so far my primary concern is that I can see that within a year or two, this thing will be able to do most of what I can do. It certainly makes my work more efficient. The bad Anyway, this is what 4o is bad at: Making, interpreting, processing, editing spreadsheets. You have to convert the spreadsheet to CSV first (you lose all formulas and charts). It is ok at interpreting small data sets, e.g. 20-30 bits of simple information e.g. amount of money spent per month, with each month listed. It's no good at, for example, creating a Nett Present Value...
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using coqui.ai and python to convert a conversation into (spoken) speech using your voice as one conversant

Install coqui (see other post here:  https://johnsaiblog.blogspot.com/2025/03/coqui-going-down-how-to-install-before.html ) Now if you save your discussion into a text file, called full_conversation . txt , in this order: speaker 1 blah blah blah speaker 2 blah blah etc etc (ie one speaker per line, no multi-line sentences), you can split the file up into sections of speech to convert to speech. Make sure each sentence is approximately 250 letters or less. Shorter is better. This script creates a folder called conversation_parts / which contains the conversation split up by speaker and by sentence.  import os import re # Input and output folder input_file = "full_conversation.txt"   # The full transcript output_folder = "conversation_parts" max_length = 250   # Maximum characters per chunk # Ensure output folder exists os.makedirs(output_folder, exist_ok=True) # Function to split text into manageable chunks def split_text(text, max_length=250):     sentence...
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coqui going down - how to install before it does

 Sadly, coqui.ai is going down. It's a Text to Speech tool using python and other things. It breaks and downloads tons of dependencies if you try just installing it with pip, it's better to use conda. There is a docker option but that wants to download like 7gb of garbage.   Check that dependencies like espeak-ng and ffmpeg are installed. Installer script 1, probably won't work #!/bin/bash # Exit on error set -e echo "   Starting Coqui TTS installation..." # Ensure Miniconda is in PATH export PATH="$HOME/miniconda3/bin:$PATH" # Create and activate the Conda environment echo "   Creating Conda environment 'coqui' with Python 3.10..." conda create -n coqui python=3.10 -y source activate coqui || conda activate coqui # Install Coqui-TTS and dependencies echo "   Installing Coqui-TTS..." pip install --upgrade pip pip install coqui-tts # Install espeak-ng for phoneme-based synthesis (needed for some models) echo "   Installi...
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