updates to README

README.md

@@ -1,6 +1,6 @@
 ---
 title: Boston School Choice
-emoji: 🌖
+emoji: 🏫
 colorFrom: pink
 colorTo: purple
 sdk: streamlit
@@ -9,62 +9,64 @@ app_file: app.py
 pinned: false
 ---
 
-# Boston Public Schools Enrollment Assistant Chatbot
+# Boston School Choice Enrollment Assistant Chatbot
 
-This is a Streamlit-based conversational chatbot designed to assist families in navigating the Boston Public Schools (BPS) enrollment process. It uses a local language model (Gemma-2B via llama-cpp) combined with Retrieval-Augmented Generation (RAG) and external APIs to answer questions about school eligibility, required documents, and nearby schools based on user-provided information.
+Welcome to the Boston School Choice Enrollment Assistant Chatbot! This application is designed to help families navigate the Boston Public Schools (BPS) enrollment process with ease. Using a combination of local language models and external APIs, the chatbot provides accurate and helpful information about school eligibility, required documents, and nearby schools based on user input.
 
-## Features
+## Key Features
 
-* **Conversational Interface:** Ask questions naturally about BPS enrollment.
-* **Context Tracking:** Remembers information provided during the conversation (residence, grade, etc.) using a JSON structure.
-* **Document Retrieval (RAG):** Uses information from local text files (in the `docs/` directory) containing BPS rules and regulations to answer questions accurately.
-* **Nearby School Lookup:** Uses the Geoapify API to find schools near a provided Boston residence address.
-* **Local LLM:** Runs inference locally using `llama-cpp-python` with a quantized Gemma-2B GGUF model, suitable for CPU execution on platforms like Hugging Face Spaces free tier.
+- **Conversational Interface**: Interact naturally with the chatbot to get answers about BPS enrollment.
+- **Context Tracking**: The chatbot remembers user-provided information such as residence and grade level.
+- **Document Retrieval (RAG)**: Accesses local text files containing BPS rules and regulations to provide accurate answers.
+- **Nearby School Lookup**: Utilizes the Geoapify API to find schools near a given Boston address.
+- **Local LLM**: Runs inference locally using `llama-cpp-python` with a quantized Gemma-2B GGUF model.
 
 ## Technology Stack
 
-* **Frontend:** Streamlit
-* **LLM Inference:** `llama-cpp-python`
-* **LLM Model:** `bartowski/gemma-2-2b-it-GGUF` (Q8_0 quantization)
-* **Embeddings:** `sentence-transformers` (`all-MiniLM-L6-v2`)
-* **Vector Store:** FAISS (CPU)
-* **Geocoding/Places API:** Geoapify
-* **Core Language:** Python 3
+- **Frontend**: [Streamlit](https://streamlit.io/)
+- **LLM Inference**: [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
+- **LLM Model**: `bartowski/gemma-2-2b-it-GGUF` (Q8_0 quantization)
+- **Embeddings**: [sentence-transformers](https://www.sbert.net/) (`all-MiniLM-L6-v2`)
+- **Vector Store**: [FAISS](https://faiss.ai/)
+- **Geocoding/Places API**: [Geoapify](https://www.geoapify.com/)
+- **Core Language**: Python 3
 
-## Setup and Installation (for Hugging Face Spaces)
+## Setup and Installation
 
-1.  **Repository Files:** Ensure the following files are present in your Hugging Face Space repository:
-    * `app.py`: The main Streamlit application script.
-    * `requirements.txt`: Lists all Python dependencies.
-    * `prompts.py`: Contains the prompt strings for the LLM.
-    * `docs/`: A directory containing `.txt` files with BPS enrollment information used for RAG. **You must create this directory and add the relevant text files.**
-    * `.streamlit/secrets.toml`: (Optional but recommended) For storing API keys.
+1. **Repository Files**: Ensure the following files are present:
+   - `app.py`: Main Streamlit application script.
+   - `requirements.txt`: Lists all Python dependencies.
+   - `prompts.py`: Contains prompt strings for the LLM.
+   - `docs/`: Directory with `.txt` files for RAG.
+   - `.streamlit/secrets.toml`: For storing API keys.
 
-2.  **Dependencies:** The `requirements.txt` file ensures all necessary packages are installed automatically by Hugging Face Spaces.
+2. **Dependencies**: Install necessary packages via `requirements.txt`.
 
-3.  **`docs/` Directory:** Create a directory named `docs` in the root of your repository. Place all the `.txt` files containing the Boston Public Schools information you want the chatbot to reference inside this directory. The RAG system relies on these files.
+3. **`docs/` Directory**: Create a `docs` directory and add relevant `.txt` files.
 
-4.  **API Keys:**
-    * **Geoapify:** You need a Geoapify API key for the geocoding and nearby school lookup features.
-    * Create a file named `secrets.toml` inside a directory named `.streamlit` in your repository (i.e., `.streamlit/secrets.toml`).
-    * Add your Geoapify key to this file:
-        ```toml
-        # .streamlit/secrets.toml
-        GEOAPIFY_KEY = "YOUR_ACTUAL_GEOAPIFY_API_KEY_HERE"
-
-        # Optional: Add Hugging Face Token if needed for model downloads
-        # HF_TOKEN = "YOUR_HUGGINGFACE_TOKEN_HERE"
-        ```
-    * **Note:** Protect your API keys. `secrets.toml` is generally included in `.gitignore` for local development but is read by Streamlit Cloud and Hugging Face Spaces when deployed.
+4. **API Keys**:
+   - Obtain a Geoapify API key for geocoding and school lookup features.
+   - Store the key in `.streamlit/secrets.toml`:
+     ```toml
+     GEOAPIFY_KEY = "YOUR_ACTUAL_GEOAPIFY_API_KEY_HERE"
+     ```
 
 ## Running the Application
 
-This application is designed to be deployed on Hugging Face Spaces. Once the files (`app.py`, `requirements.txt`, `prompts.py`, `docs/` directory, `.streamlit/secrets.toml`) are correctly set up in the repository, Hugging Face Spaces should automatically build and run the Streamlit application.
+Deploy the application on Hugging Face Spaces. Ensure all files are correctly set up in the repository for automatic build and run.
 
 ## Usage
 
-Interact with the chatbot by typing your questions into the input box at the bottom of the application. Provide information like your address or your child's grade when asked, and the chatbot will use this context, along with retrieved documents and external lookups, to assist you.
+Type your questions into the input box at the bottom of the application. Provide information like your address or your child's grade when prompted. The chatbot will use this context, along with retrieved documents and external lookups, to assist you.
+
+## Contributing
+
+We welcome contributions! Please fork the repository and submit a pull request with your changes.
+
+## License
+
+This project is licensed under the MIT License.
 
 ## Disclaimer
 
-This chatbot aims to provide helpful information based on the documents provided in the `docs/` directory and external APIs. However, **Boston Public Schools enrollment rules and policies can change.** Always verify critical information directly with official BPS resources or a BPS Welcome Center before making decisions. The information provided by this chatbot is not guaranteed to be 100% accurate or up-to-date and should be used as a guide only.
+This chatbot provides information based on documents in the `docs/` directory and external APIs. Always verify critical information with official BPS resources or a BPS Welcome Center. The information provided is not guaranteed to be 100% accurate or up-to-date.