fix readme

README.md

@@ -5,7 +5,7 @@ colorFrom: "yellow"
 colorTo: "purple"
 sdk: "gradio"
 python_version: "3.10"
-start_command: "bash start.sh"
+start_command: "python app.py"
 short_description: "A Gradio RAG app for querying the poetry of Allama Iqbal."
 tags:
   - rag
@@ -17,15 +17,18 @@ tags:
 
 # Iqbal Poetry RAG System
 
-A Retrieval-Augmented Generation (RAG) system for exploring and querying the poetry of Allama Iqbal. This project leverages vector search and large language models (LLMs) to answer questions about Iqbal's poetry, providing relevant poem excerpts as context.
+A Retrieval-Augmented Generation (RAG) system for exploring and querying the poetry of Allama Iqbal. This project leverages vector search and large language models (LLMs) to answer questions about Iqbal's poetry, providing relevant poem excerpts as context. 
+
+Note: On first run your will need to set up the vector embeddings store so the set up and initialization can take a few hours dependings on the performance of your PC.
 
 ---
 
 ## 🚀 Hugging Face Spaces Ready
 
+### In Progress: 
 This project is ready to be deployed as a [Hugging Face Space](https://huggingface.co/spaces). The configuration block above (in YAML) tells Hugging Face how to launch the app:
 - **sdk**: Uses Gradio for the web interface.
-- **app_file**: Entry point for the app (`app/main.py`).
+- **app_file**: Entry point for the app (`app.py`).
 - **python_version**: Uses Python 3.10.
 - **short_description**: Shown in the Space's thumbnail.
 - **tags**: For discoverability.
@@ -36,7 +39,7 @@ To deploy, simply upload this repository to your Hugging Face account as a new S
 
 ## Features
 
-- **Semantic Search**: Retrieve the most relevant poems for a given question using vector embeddings.
+- **Semantic Search**: Retrieve the most relevant poems and their themes for a given question using vector embeddings.
 - **LLM-Powered Answers**: Generate answers using a language model, grounded in retrieved poem context.
 - **Gradio Interface**: User-friendly web interface powered by [Gradio](https://gradio.app/).
 - **Plug-and-Play Dataset**: The poetry dataset is already included in the repository, with all paths set up for immediate use.
@@ -52,6 +55,16 @@ To deploy, simply upload this repository to your Hugging Face account as a new S
 
 - Python 3.9+
 - [uv](https://github.com/astral-sh/uv) (a fast Python package installer, drop-in replacement for pip)
+- HuggingFace account (https://huggingface.co/) (to use pretrained models)
+- Ollama (https://ollama.com/) (to create vector embeddings)
+
+```bash
+# install Ollama
+curl -sSfL https://ollama.ai/install.sh | sh
+
+# pull a model
+ollama pull llama3
+```
 
 ### 1. Clone the repository
 
@@ -76,7 +89,7 @@ The poetry dataset is already included in the repository, and all file paths are
 To launch the Gradio app locally:
 
 ```bash
-python app/main.py
+python app.py
 ```
 
 This will start a Gradio web interface in your browser, where you can enter your questions about Iqbal's poetry and receive contextually grounded answers.
@@ -88,33 +101,35 @@ This will start a Gradio web interface in your browser, where you can enter your
 ```
 iqbal_poetry_rag/
 │
-├── app/
-│   ├── RAGSystem.py         # Main RAG system class
-│   ├── main.py              # Entry point for the Gradio app
-│   └── config.py            # Configuration (thresholds, file paths, etc.)
+├── interface/
+│   ├── RAGSystem.py          # Main RAG system class
+│   ├── gradio_interface.py   # Gradio app and its interface
+│   └── config.py             # Configuration (thresholds, file paths, etc.)
 │
 ├── rag/
-│   ├── vector_store.py      # Vector store initialization and building
-│   ├── retriever.py         # Retriever configuration
-│   ├── llm.py               # LLM initialization and prompt management
+│   ├── vector_store.py       # Vector store initialization and building
+│   ├── retriever.py          # Retriever configuration
+│   ├── llm.py                # LLM initialization and prompt management
+│   └── embeddings.py         # Embedding functionality for the RAG system uses Ollama
 │
 ├── utils/
-│   ├── error_handling.py    # Error handling decorators
-│   └── feedback_logger.py   # (Optional) Feedback logging
+│   ├── error_handling.py     # Error handling decorators
+│   └── feedback_logger.py    # (Optional) Feedback logging
 │
-├── dataset/
-│   └── poems.json           # Iqbal's poetry dataset (already included)
+├── data/                     # Iqbal's poetry dataset (already included)
 │
-├── requirements.txt         # Project dependencies
-└── README.md                # This file
+├── requirements.txt          # Project dependencies
+├── app.py                    # Entry point for the app
+└── README.md                 # This file
 ```
 
 ---
 
 ## Configuration
 
-Edit `app/config.py` to set:
-
+Edit `interface/config.py` to set:
+- `HUGGING_FACE_TOKEN`: Your personal huggingface token (this can be set up using dotenv. Create a .env file in the home folder and store it as 
+HUGGING_FACE_TOKEN = <YOUR_TOKEN>)
 - `SCORE_THRESHOLD`: Minimum similarity score for retrieved poems.
 - `JSON_FILE_PATH`: Path to your poems data file (already set to the included dataset).