Update README.md

README.md

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+---
+title: SmartManuals-AI
+emoji: 🧠
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 4.29.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+tags:
+  - RAG
+  - LLM
+  - Chroma
+  - Gradio
+  - OCR
+  - HuggingFace
+  - PDF
+  - Word
+  - SemanticSearch
+  - SmartManualsAI
+---
+
 # ✅ SmartManuals-AI for Hugging Face Spaces
 
-SmartManuals-AI is a local-first document QA system that uses RAG (retrieval-augmented generation), OCR, and embedding search to answer technical questions from PDFs **and Word documents**.
+SmartManuals-AI is a local-first document QA system that uses **retrieval-augmented generation (RAG)**, **OCR**, and **semantic embeddings** to answer technical questions from equipment manuals, service guides, and parts catalogs.
+
+This app is optimized for Hugging Face Spaces and **requires no user upload** — just preload your manuals in a `Manuals/` folder.
 
 ---
 
 ## 🔧 Features
 
-- 🔍 **Ask natural-language questions** to your manuals
-- 📄 Handles both **PDFs** and **Word `.docx`** files
-- 🧠 Uses **semantic search** with `sentence-transformers`
-- 🗃️ ChromaDB for fast local vector indexing
-- 💬 Answers generated by **Meta LLaMA 3.1 8B Instruct** (default)
-- 📊 Gradio dashboard for interaction
+- 🧠 Ask **natural-language questions** against your own manuals
+- 📄 Supports both **PDF** and **Word (.docx)** documents
+- 🔍 Uses `sentence-transformers` for semantic search
+- 🗃️ Indexes chunks in **ChromaDB** (stored locally)
+- 💬 Generates answers via Hugging Face models (default: **Meta LLaMA 3.1 8B Instruct**)
+- 🖥️ Clean **Gradio interface** for querying
 
 ---
 
 ## 📁 Folder Structure
+
 ```
 SmartManuals-AI/
-├── app.py                 # Hugging Face Spaces main app
-├── Manuals/               # 📂 Upload your PDF and Word manuals here
+├── app.py                 # Main Hugging Face app
+├── Manuals/               # Place your PDF and DOCX manuals here
 │   ├── OM_Treadmill.pdf
 │   └── Parts_Bike.docx
-├── chroma_store/         # ⛓️ ChromaDB vector DB (auto-generated)
-├── requirements.txt      # 📦 Dependencies
-└── README.md             # 📖 This file
+├── chroma_store/         # Vector database (auto-generated)
+├── requirements.txt      # Dependencies
+└── README.md             # This file
 ```
 
 ---
 
-## 🚀 Usage in Hugging Face Spaces
+## 🚀 Usage on Hugging Face Spaces
+
+### 🔐 Environment Variable
+
+Add this secret in your Space settings:
+
+| Name      | Value                |
+|-----------|----------------------|
+| `HF_TOKEN` | Your Hugging Face token |
+
+> **Note**: You must accept model licenses on [Hugging Face Hub](https://huggingface.co/meta-llama) before using gated models like `Llama-3.1-8B-Instruct`.
+
+---
+
+### 📤 Uploading Manuals
 
-### 🔐 Environment Variables
-Add your Hugging Face token as a secret:
+- Upload your **PDF and Word documents** directly to the `Manuals/` folder in your Space repository.
+- No need for file uploads via the interface.
 
-- `HF_TOKEN`: Your Hugging Face access token (required for gated models)
+---
 
-### 📤 Upload Your Files
-Put all your manuals (PDF and Word `.docx`) into the `Manuals/` folder.
+### 🧠 How It Works
 
-### 🧠 App Behavior
-- On startup:
-  - Extracts text (with OCR fallback) from PDFs
-  - Extracts clean text from Word documents
-  - Chunks and embeds content into ChromaDB
-- During inference:
-  - Retrieves semantically relevant chunks
-  - Sends them to LLaMA 3.1 Instruct for answer generation
+- On app startup:
+  - Text is extracted from **PDFs (with OCR fallback)** and `.docx` Word files
+  - Sentences are cleaned, chunked, and embedded with `all-MiniLM-L6-v2`
+  - Chunks are stored in a local **ChromaDB vector database**
 
-### ❌ No User Upload
-This app is **designed to work without file uploads**. All processing is done on preloaded files in the `Manuals/` directory.
+- At query time:
+  - Your question is embedded and semantically compared against chunks
+  - The most relevant chunks are passed to the LLM
+  - The **LLM (LLaMA 3.1)** generates a focused answer from context only
 
 ---
 
-## 🧠 Default Model
-- Uses **`meta-llama/Llama-3.1-8B-Instruct`**
-- All question answering is **fully automatic**
-- User is **not required to pick a model, doc type, or filter** — the system decides based on question and content.
+## 🤖 Default Model
+
+- This app uses: **`meta-llama/Llama-3.1-8B-Instruct`**
+- More models are supported behind-the-scenes (e.g. Mistral, Gemma)
+- **No need to manually pick** models, doc types, or categories
 
 ---
 
 ## 🧩 Supported File Types
-- `.pdf` (with OCR for scanned pages)
-- `.docx` (via `python-docx`)
+
+- ✅ PDF (`.pdf`) with OCR fallback using Tesseract
+- ✅ Word Documents (`.docx`)
 
 ---
 
 ## 🧪 Local Development
-Install dependencies:
+
+Clone and run locally:
+
 ```bash
+git clone https://github.com/damoojeje/SmartManuals-AI.git
+cd SmartManuals-AI
 pip install -r requirements.txt
-```
-Run locally:
-```bash
 python app.py
 ```
 
+> 📁 Place your manuals inside the `Manuals/` directory before running.
+
 ---
 
-## 👨🏽‍💻 Project by: [Damilare Eniolabi](mailto:damilareeniolabi@gmail.com)
-GitHub: [@damoojeje](https://github.com/damoojeje)
+## 👨🏽‍💻 Created By
+
+**Damilare Eniolabi**  
+📧 [damilareeniolabi@gmail.com](mailto:damilareeniolabi@gmail.com)  
+🔗 GitHub: [@damoojeje](https://github.com/damoojeje)
 
 ---
 
-## 📌 Tags
-`RAG` `LLM` `Chroma` `OCR` `PDF` `Word` `Gradio` `HuggingFace` `SmartManualsAI`
+## 🔖 Tags
+
+`RAG` · `LLM` · `Gradio` · `ChromaDB` · `OCR` · `SemanticSearch` · `PDF` · `Word` · `SmartManualsAI` · `EquipmentQA`