docs(readme): Create comprehensive project README

This commit introduces a complete, professional README.md to serve as the primary documentation for the FOT Intervention Recommender PoC.

The new README is designed to align with the final project state and the requirements of the performance task brief. It provides a clear and comprehensive overview for both technical and non-technical audiences.

Key additions include:
- A clear project goal and value proposition.
- A link to the live Gradio demo on Hugging Face Spaces.
- Professional instructions for requesting an access key via GitHub Issues.
- A detailed breakdown of the final RAG architecture and strategic decisions.
- Step-by-step local setup and execution instructions using `uv`.
- A clear guide for running development tools (pytest, black, ruff).
- A project structure diagram for easy navigation.

README.md

@@ -13,71 +13,151 @@ short_description: POC - Freshman On-Track RAG Intervention Recommender
 ---
 
 
-# Demo Application
+# Freshman On-Track (FOT) Intervention Recommender
 
-A Python project for coding exercises and development.
+[![Python Version](https://img.shields.io/badge/Python-3.12-blue.svg)](https://www.python.org/downloads/release/python-3120/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/chuckfinca/fot-recommender-api)
 
-## Setup for Development
+This repository contains the proof-of-concept for the Freshman On-Track (FOT) Intervention Recommender, an AI-powered tool designed to empower educators.
 
-This project has dependencies with specific hardware requirements (e.g., PyTorch). To ensure a smooth setup on any machine, follow this two-step process.
+## 🚀 Live Demo
+
+The full application is deployed as an interactive web API on Hugging Face Spaces.
+
+**[👉 Click Here to Launch the Live FOT Recommender API](https://huggingface.co/spaces/chuckfinca/fot-recommender-api)**
+
+**Note on Access:** The public demo is protected by an access key. If you would like to try the live application, please **[open a GitHub issue in this repository](https://github.com/chuckfinca/fot-intervention-recommender/issues/new)** to request access, and I will be happy to provide a key.
+
+## 1. Project Goal
+
+Freshman year performance is the strongest predictor of high school graduation. However, educators often lack systematic tools to match at-risk 9th graders with the specific, evidence-based interventions they need.
+
+This project addresses that gap by providing a **Retrieval-Augmented Generation (RAG)** system that transforms a simple narrative about a student's challenges into a set of clear, actionable, and evidence-based recommendations. It turns scattered educational research into targeted guidance, enabling educators to support their students more effectively.
+
+## 2. Features
+
+*   **Advanced RAG Architecture**: Utilizes a sophisticated pipeline to ensure recommendations are relevant and grounded in evidence.
+    *   **Retrieval**: Employs a `FAISS` vector database and the `all-MiniLM-L6-v2` sentence-transformer model to perform semantic search over the knowledge base.
+    *   **Generation**: Uses Google's `gemini-1.5-flash-latest` model to synthesize the retrieved evidence into a coherent, actionable plan.
+*   **Persona-Based Recommendations**: Delivers tailored advice for different audiences, fulfilling a key project bonus goal. The system can generate distinct outputs for a **teacher**, **parent**, or **principal**.
+*   **Evidence-Backed**: Every recommendation is based on a curated knowledge base of best-practice documents from reputable sources like the Network for College Success, the Institute of Education Sciences, and Attendance Works.
+*   **Interactive Web Application**: A user-friendly Gradio UI allows for easy interaction, example scenarios, and a secure access key system for the demo.
+*   **Full Transparency**: The "Evidence Base" section in the output shows the exact source documents, page numbers, and content snippets used to generate the recommendation, along with a relevance score for each.
+
+## 3. System Architecture
+
+The project follows a modern RAG architecture designed for quality and scalability.
+
+1.  **Knowledge Base Curation**: A strategic decision was made to manually curate a high-quality `knowledge_base_raw.json` file from the source documents. For this proof-of-concept, this approach ensured maximum quality for the RAG pipeline, bypassing the complexities of programmatic PDF extraction.
+2.  **Data Preprocessing**: A `build_knowledge_base.py` script processes the raw JSON. It uses a semantic chunking strategy to group related concepts, creating a final `knowledge_base_final_chunks.json` file.
+3.  **Vector Indexing**: During the build process, the pre-processed chunks are encoded into vector embeddings and stored in a `faiss_index.bin` file for efficient similarity search.
+4.  **RAG Pipeline (At Runtime)**:
+    *   The user enters a student narrative into the Gradio app.
+    *   The narrative is converted into a vector embedding.
+    *   FAISS performs a similarity search on the vector index to retrieve the most relevant intervention chunks.
+    *   The retrieved chunks and the original narrative are formatted into a detailed prompt, tailored to the selected persona (teacher, parent, or principal).
+    *   The prompt is sent to the Gemini API, which generates a synthesized recommendation.
+    *   The final recommendation and its evidence base are formatted and displayed to the user.
+
+## 4. How to Run Locally
+
+This project uses `uv` for fast and reliable dependency management.
+
+### Prerequisites
+
+1.  **Python >= 3.12**
+2.  **`uv` installed**:
+    ```bash
+    pip install uv
+    ```
+3.  **Environment Variables**: You must create a `.env` file in the project's root directory. The application loads secrets from this file.
+    ```
+    # .env
+    FOT_GOOGLE_API_KEY="your_google_api_key_here"
+    DEMO_PASSWORD="your_local_password" # Sets the password for your local instance of the Gradio app.
+    ```
+
+### Setup
+
+Follow this two-step process to ensure hardware-specific dependencies like PyTorch are installed correctly.
 
 1.  **Create the virtual environment:**
     ```bash
     uv venv
     ```
+    *Activate the environment:*
+    *   macOS/Linux: `source .venv/bin/activate`
+    *   Windows: `.venv\Scripts\activate`
 
 2.  **Install PyTorch Separately:**
-    This command lets PyTorch's installer find the correct version for your specific hardware (Intel Mac, Apple Silicon, Windows, Linux, etc.).
+    This command lets `uv` find the correct PyTorch version for your specific hardware (Intel Mac, Apple Silicon, Windows, Linux, etc.).
     ```bash
     uv pip install torch --index-url https://download.pytorch.org/whl/cpu
     ```
-    *Note: We are explicitly using the CPU-only version of PyTorch, which is perfect for this project and avoids complex CUDA dependencies.*
+    *Note: We explicitly use the CPU-only version of PyTorch, which is perfect for this project and avoids complex CUDA dependencies.*
 
 3.  **Install the Project:**
-    Now that the difficult dependency is handled, install our application and its other development tools.
+    Now that the difficult dependency is handled, install the application and its development tools.
     ```bash
     uv pip install -e ".[dev]"
     ```
 
-This command will now see that a compatible version of `torch` is already installed and will proceed without errors.
+### Running the Application
 
-
-## Running the Application
-
-After setup, run the application using its console script entry point. This is the standard way to run the app and avoids any warnings.
+After setup, run the Gradio web application using its console script entry point.
 
 ```bash
 uv run fot-recommender
 ```
 
-## Development Tools
+This will launch the interactive Gradio API, which you can access in your browser.
 
-After setting up for development, you can use the following tools.
+## 5. Development
 
-**Run Tests:**
-```bash
-uv run pytest
-```
+The project is configured with a suite of standard development tools for maintaining code quality.
 
-**Format Code:**
-```bash
-uv run black .
-```
-
-**Lint Code:**
-```bash
-uv run ruff check .
-```
-
-**Type Checking:**
-```bash
-uv run mypy src/
-```
+*   **Run Tests:**
+    ```bash
+    uv run pytest
+    ```
+*   **Format Code:**
+    ```bash
+    uv run black .
+    ```
+*   **Lint Code:**
+    ```bash
+    uv run ruff check .
+    ```
+*   **Type Checking:**
+    ```bash
+    uv run mypy src/
+    ```
 
-## Project Structure
+## 6. Project Structure
 
 ```
-src/demo_application/    # Main package
-tests/                   # Test files
-pyproject.toml          # Project configuration
-```
+.
+├── app.py                  # Gradio UI and web API entry point
+├── data/
+│   ├── processed/          # Processed data artifacts
+│   │   ├── citations.json
+│   │   ├── faiss_index.bin
+│   │   ├── knowledge_base_final_chunks.json
+│   │   └── knowledge_base_raw.json
+│   └── source_pdfs/        # Original source documents
+├── docs/                     # Project planning documents
+├── notebooks/                # Proof-of-concept notebook
+├── pyproject.toml          # Project configuration and dependencies
+├── README.md               # This file
+├── scripts/
+│   └── build_knowledge_base.py # Script to build data artifacts
+├── src/
+│   └── fot_recommender/    # Main Python package
+│       ├── __init__.py
+│       ├── config.py       # Configuration and environment variables
+│       ├── main.py         # Main application logic
+│       ├── prompts.py      # Prompts for the generative model
+│       ├── rag_pipeline.py # Core RAG logic
+│       └── semantic_chunker.py # Logic for chunking source data
+└── tests/                    # Unit and integration tests