Build Python Microservice with FastAPI, Ollama & Whisper | AI Interviewer Project Part 2 скачать в хорошем качестве

Build Python Microservice with FastAPI, Ollama & Whisper | AI Interviewer Project Part 2

Welcome to Part 2 of the AI Interviewer Project! In this episode, we move beyond basic CRUD apps and start building the real intelligence behind our system. We design a dedicated Python AI microservice using FastAPI that acts as the brain of the application. Instead of relying on expensive cloud APIs, everything runs locally and completely free. By integrating Ollama (Mistral) for reasoning and OpenAI Whisper for speech-to-text, your app will be able to: 🎙️ Hear voice answers 💻 Read code submissions 🧠 Evaluate & grade responses like a real senior developer This video is a must-watch if you want to understand how real GenAI systems are built in production. 📂 Source Code 👉 https://github.com/siddhantsaxena45/A... 📺 Full Playlist 👉    • AI INTERVIWER  - The Ultimate FULL STACK W...   🔥 What We Build in This Video FastAPI Setup – High-performance Python microservice Local LLM Integration – Ollama (Mistral) for question generation & evaluation Voice Processing – Local speech-to-text using OpenAI Whisper Prompt Engineering – Strict system prompts to avoid hallucinations Evaluation Engine – Scoring logic for oral & coding answers Data Validation – Pydantic schemas synced with Node.js backend 🛠️ Libraries & Tools Used Framework: FastAPI + Uvicorn LLM: Ollama (Mistral 7B) Speech-to-Text: OpenAI Whisper (base.en) Audio Processing: PyDub + FFmpeg Validation: Pydantic ⏱️ Important Timestamps 00:00 – Introduction & project recap 00:18 – Python AI microservice overview 00:30 – Installing & running Ollama 00:49 – Pulling Mistral model 01:10 – Python venv & service setup 01:34 – Testing Ollama locally 02:22 – Virtual environment & dependencies 04:19 – FastAPI server & architecture 06:02 – CORS setup (Node ↔ Python) 11:41 – Whisper model loading strategy 14:05 – Pydantic request/response schemas 20:41 – Health check endpoint 26:29 – AI question generation API 33:00 – Prompt engineering best practices 39:49 – Cleaning & validating AI output 52:14 – Audio transcription API 57:42 – Whisper transcription pipeline 01:06:24 – Evaluation system design 01:12:19 – Oral vs Coding grading logic 01:20:27 – Strict JSON-only AI responses 01:35:16 – Sample AI evaluation output 01:36:16 – What’s next (React frontend) 🔔 What’s Next? 👉 Part 3: Building the React Frontend Subscribe so you don’t miss it! #Python #FastAPI #AIInterviewer #Ollama #Whisper #GenAI #Microservices #FullStack #MERN #CodingTutorial