Ctrl+K
- 1.52 kB initial commit
- 208 Bytes To create a local dentist appointment app named Raznet DentaAI using a "DeepSite"-style approach with a locally installed Ollama LLM (specifically tiny-llama) on your Clear Linux OS under the ~/Videos folder, we’ll design a web-based solution. Since "DeepSite" refers to a concept of generating web pages via text prompts with LLMs (inspired by tools like those from HuggingFace or LocalSite), we’ll leverage Ollama's tiny-llama model to dynamically generate the app's structure, handle appointment scheduling, and provide a user interface. The app will run locally, utilizing a Flask backend to integrate with Ollama and serve a responsive HTML frontend. Approach LLM Prompt: A powerful prompt will guide tiny-llama to generate the app's logic, including appointment management, scheduling, and a user-friendly interface. Technology Stack: Flask for the backend, HTML/CSS/JavaScript (with Tailwind CSS) for the frontend, and Ollama for LLM-driven features. Local Setup: The app will be self-contained under ~/Videos/RaznetDentaAI, using Ollama's API to process prompts and manage data. Features: Appointment booking, schedule viewing, patient records (simulated), and a chatbot for assistance, all powered by tiny-llama. Prerequisites Ensure Ollama is installed and tiny-llama is pulled: bash ollama pull tiny-llama Install Python and Flask on Clear Linux: bash sudo swupd bundle-add python3-basic pip3 install flask requests Powerful Prompt for Ollama The prompt will instruct tiny-llama to generate a complete web app for Raznet DentaAI, including backend logic and frontend design. Implementation The setup script below creates the app structure, generates the necessary files using Ollama's API, and provides a runnable solution. - Initial Deployment
- 54.1 kB To create a local dentist appointment app named Raznet DentaAI using a "DeepSite"-style approach with a locally installed Ollama LLM (specifically tiny-llama) on your Clear Linux OS under the ~/Videos folder, we’ll design a web-based solution. Since "DeepSite" refers to a concept of generating web pages via text prompts with LLMs (inspired by tools like those from HuggingFace or LocalSite), we’ll leverage Ollama's tiny-llama model to dynamically generate the app's structure, handle appointment scheduling, and provide a user interface. The app will run locally, utilizing a Flask backend to integrate with Ollama and serve a responsive HTML frontend. Approach LLM Prompt: A powerful prompt will guide tiny-llama to generate the app's logic, including appointment management, scheduling, and a user-friendly interface. Technology Stack: Flask for the backend, HTML/CSS/JavaScript (with Tailwind CSS) for the frontend, and Ollama for LLM-driven features. Local Setup: The app will be self-contained under ~/Videos/RaznetDentaAI, using Ollama's API to process prompts and manage data. Features: Appointment booking, schedule viewing, patient records (simulated), and a chatbot for assistance, all powered by tiny-llama. Prerequisites Ensure Ollama is installed and tiny-llama is pulled: bash ollama pull tiny-llama Install Python and Flask on Clear Linux: bash sudo swupd bundle-add python3-basic pip3 install flask requests Powerful Prompt for Ollama The prompt will instruct tiny-llama to generate a complete web app for Raznet DentaAI, including backend logic and frontend design. Implementation The setup script below creates the app structure, generates the necessary files using Ollama's API, and provides a runnable solution. - Initial Deployment
- 388 Bytes initial commit