This video series covers advanced techniques for image captioning and face recognition using machine learning models. We explore the BLIP model for generating image captions,…
This video covers advanced techniques for analyzing subtitle files using machine learning models. We explore a Streamlit application that uses the spaCy library to load…
This video series covers advanced techniques for image captioning and face recognition using machine learning models. We explore the BLIP model for generating image captions,…
In this video, the underlying user story for this code is straightforward: to establish a semantic match between all existing keywords in a CMS (such…
Continue reading → Using Semantic Similarity with a CMS #ia #nlp #cms #api #python
In this video, we delve into the challenge of evaluating the quality of AI-generated text, focusing on how to ensure it is accurate and avoids…
Continue reading → Streamlit & Ollama: Querying Mistral LLM Locally and Generating Titles & Keywords
An article exploring the process for testing the output of Large Language Models (LLMs) using a tool called “promptfoo.” This tool allows developers to evaluate…
Continue reading → Promptfoo: The Ultimate Tool for Ensuring LLM Quality and Reliability (Part 1)
This video is part of a post “Building a Vue.js SPA (Single Page Application) Frontend with FastAPI Backend for AI Integration” QUICK DESCRIPTION Transitioning from…
Continue reading → Video #6 Streamlit Mastery: View Whisper Transcripts & Prevent Page Reloads
This video is part of a post “Building a Vue.js SPA (Single Page Application) Frontend with FastAPI Backend for AI Integration” QUICK DESCRIPTION Transitioning from…
Continue reading → Video #4 Connect FastAPI Backend to Vue.js Frontend: Full Guide
This video is part of a post “Building a Vue.js SPA (Single Page Application) Frontend with FastAPI Backend for AI Integration” QUICK DESCRIPTION Transitioning from…
Continue reading → Video #2 Master Vuejs: Build Your First App and Project with Ease
This video is part of a post “Building a Vue.js SPA (Single Page Application) Frontend with FastAPI Backend for AI Integration” QUICK DESCRIPTION Transitioning from…
Continue reading → Video #1 Scaffold Your First Vitejs App: Comprehensive Beginner’s Guide
This video is part of a post “Empower Your Workflow: Harnessing the Power of LM Studio and Ollama for Seamless Local LLM Execution” POST: https://wp.me/p3Vuhl-3iX…
This video is part of a post “Empower Your Workflow: Harnessing the Power of LM Studio and Ollama for Seamless Local LLM Execution” POST: https://wp.me/p3Vuhl-3iX…
This video is part of a post “Crafting Fluent Translation API: A quick Journey into Text Translation with NLLB, HuggingFace, and FastAPI, Plus a small…
This video is part of a post “Crafting Fluent Translation API: A quick Journey into Text Translation with NLLB, HuggingFace, and FastAPI, Plus a small…
This video is part of a post “Crafting Fluent Translation API: A quick Journey into Text Translation with NLLB, HuggingFace, and FastAPI, Plus a small…
This video is part of a post “Unlocking Speech-to-Text: Harnessing the Power of the OpenAI Whisper API with FastAPI Integration.” This file “011_faster_whisper.py” enables quick…
Continue reading → Using #whisper & #fastapi: Unlocking Multilingual Transcription with #whisper
This video is part of a post “Unlocking Speech-to-Text: Harnessing the Power of the OpenAI Whisper API with FastAPI Integration.” This file “009_openai_whisper_fastapi.py” is POC…
Continue reading → Using #whisper & #fastapi : Creating a #multilingual Audio #api with #whisper
POC from “005_fastapi_tiangolo_tutorial_sql_databases”: The example given by the official documentation of FastAPI with SQLAlchemy. POC “007_sql_databases_peewee”: The example given by the official documentation of FastAPI…
POC from 008_fastapi_mysql_restapi: Using Docker to create a MYSQL database and a PhpMyAdmin instance connected to FastAPI POC. This video is part of the post…
I already mention this POC. The files are a mix between logical code written by ChatGPT (I have given the prompt in the main.py) and…