Update README.md

README.md

@@ -1,109 +1,120 @@
-# 🌀 Odisha Disaster Management RAG Chatbot
-
-## 📌 Overview
-Odisha faces recurring disasters every year such as **floods, cyclones, and droughts**.  
-While the state has a strong disaster management authority (OSDMA), information is often scattered across reports, research papers, and government documents.  
-
-This project builds a **Retrieval-Augmented Generation (RAG) based chatbot** that provides citizens, researchers, and policymakers with **clear, reliable, and contextual answers** related to Odisha’s disaster management practices.
-
----
-
-## ✨ Features
--  Handles **132 PDFs** and **12 text files** (OSDMA, IMD, NDMA, research papers).  
--  **Preprocessing pipeline**: PDF/text extraction, cleaning, normalization, chunking.  
-- **Embeddings** with `sentence-transformers/all-MiniLM-L6-v2`.  
--  **FAISS Vector Database** for fast and efficient retrieval.  
--  **RAG pipeline**:  
-  1. User query → query structuring (handles poor English, spelling issues).  
-  2. Retrieve relevant chunks from FAISS.  
-  3. If no relevant results → no LLM call (saves cost).  
-  4. If relevant → LLM generates structured, contextual answers.  
--  **Prompt engineering** for better accuracy and reduced hallucinations.  
--  Backend: **FastAPI**.  
--  Frontend: **HTML, CSS, JS chatbot interface**.  
-
----
-
-## 🏗️ Architecture
-
- **User Query → Query Structuring → FAISS Retriever → Relevant Chunks → LLM → Answer**
-
-# 🛠️ Tech Stack
-
--  **Python** (data handling & backend)  
--  **PyPDF, TextLoader** → PDF/Text extraction  
--  **FAISS** → Vector database  
--  **HuggingFace Sentence Transformers** → Embeddings  
--  **FastAPI** → Backend API  
--  **HTML, CSS, JavaScript** → Frontend chatbot UI  
--  **LLM (OpenAI / HuggingFace)** → Answer generation  
-
----
-
-## ⚙️ Installation
-
-### 1. Clone the repository
-```bash
-git clone https://github.com/subhakanta156/odisha-disaster-knowledge-assistant.git
-```
-### 2. Create virtual environment & install dependencies
-```bash
-python -m venv venv
-source venv/bin/activate   # Linux/Mac
-venv\Scripts\activate      # Windows
-
-pip install -r requirements.txt
-```
-### 3. Prepare the data
-- Place all PDFs/text files inside the data/ folder.
-- Run preprocessing & embedding script:
-```bash 
-python scripts/build_vector_store.py
-```
-### 4. Run the FastAPI backend
-```bash
-uvicorn app.main:app --reload
-```
-### 5. Open the frontend
-- Open `frontend/index.html` in your browser.
-  
-## 🚀 Usage  
-
-Ask questions like:  
-
-- “How does Odisha’s disaster proneness compare with other Indian states?”  
-- “Provide details of relief funds sanctioned for Odisha during the 1999 Super Cyclone.” 
-- “Which Odisha agency is primarily responsible for issuing cyclone alerts?” 
-- “Explain the key steps taken by the Odisha government if lives are lost in a disaster?”
-
-
-The system retrieves relevant chunks from reports and generates reliable, structured answers.  
-
----
-
-## 📊 Optimizations  
-
--  Added query filtering → No LLM call if retrieval fails (reduces cost).  
--  Handled poor English queries via query restructuring.  
--  Improved prompt engineering to minimize hallucinations.  
-
----
-
-## 📌 Future Improvements  
-
--  Add multilingual support (Odia/Hindi queries).  
--  Deploy on cloud (AWS/GCP/Azure) with Docker.  
--  Use advanced embeddings (e.g., `all-mpnet-base-v2`) for higher accuracy.  
--  Add real-time updates (e.g., cyclone alerts).  
-
----
-
-## 👨‍💻 Author  
-
-**Subhakanta Rath**  
-
-MSc AI & ML @ IIIT Lucknow  
-
-Passionate about AI/ML, Data Engineering  
-
-
+
+---
+title: "Odisha Disaster RAG Chatbot"
+emoji: "🌊"
+colorFrom: green
+colorTo: yellow
+sdk: docker
+app_file: app.py
+pinned: false
+license: mit
+---
+# 🌀 Odisha Disaster Management RAG Chatbot
+
+## 📌 Overview
+Odisha faces recurring disasters every year such as **floods, cyclones, and droughts**.  
+While the state has a strong disaster management authority (OSDMA), information is often scattered across reports, research papers, and government documents.  
+
+This project builds a **Retrieval-Augmented Generation (RAG) based chatbot** that provides citizens, researchers, and policymakers with **clear, reliable, and contextual answers** related to Odisha’s disaster management practices.
+
+---
+
+## ✨ Features
+-  Handles **132 PDFs** and **12 text files** (OSDMA, IMD, NDMA, research papers).  
+-  **Preprocessing pipeline**: PDF/text extraction, cleaning, normalization, chunking.  
+- **Embeddings** with `sentence-transformers/all-MiniLM-L6-v2`.  
+-  **FAISS Vector Database** for fast and efficient retrieval.  
+-  **RAG pipeline**:  
+  1. User query → query structuring (handles poor English, spelling issues).  
+  2. Retrieve relevant chunks from FAISS.  
+  3. If no relevant results → no LLM call (saves cost).  
+  4. If relevant → LLM generates structured, contextual answers.  
+-  **Prompt engineering** for better accuracy and reduced hallucinations.  
+-  Backend: **FastAPI**.  
+-  Frontend: **HTML, CSS, JS chatbot interface**.  
+
+---
+
+## 🏗️ Architecture
+
+ **User Query → Query Structuring → FAISS Retriever → Relevant Chunks → LLM → Answer**
+
+# 🛠️ Tech Stack
+
+-  **Python** (data handling & backend)  
+-  **PyPDF, TextLoader** → PDF/Text extraction  
+-  **FAISS** → Vector database  
+-  **HuggingFace Sentence Transformers** → Embeddings  
+-  **FastAPI** → Backend API  
+-  **HTML, CSS, JavaScript** → Frontend chatbot UI  
+-  **LLM (OpenAI / HuggingFace)** → Answer generation  
+
+---
+
+## ⚙️ Installation
+
+### 1. Clone the repository
+```bash
+git clone https://github.com/subhakanta156/odisha-disaster-knowledge-assistant.git
+```
+### 2. Create virtual environment & install dependencies
+```bash
+python -m venv venv
+source venv/bin/activate   # Linux/Mac
+venv\Scripts\activate      # Windows
+
+pip install -r requirements.txt
+```
+### 3. Prepare the data
+- Place all PDFs/text files inside the data/ folder.
+- Run preprocessing & embedding script:
+```bash 
+python scripts/build_vector_store.py
+```
+### 4. Run the FastAPI backend
+```bash
+uvicorn app.main:app --reload
+```
+### 5. Open the frontend
+- Open `frontend/index.html` in your browser.
+  
+## 🚀 Usage  
+
+Ask questions like:  
+
+- “How does Odisha’s disaster proneness compare with other Indian states?”  
+- “Provide details of relief funds sanctioned for Odisha during the 1999 Super Cyclone.” 
+- “Which Odisha agency is primarily responsible for issuing cyclone alerts?” 
+- “Explain the key steps taken by the Odisha government if lives are lost in a disaster?”
+
+
+The system retrieves relevant chunks from reports and generates reliable, structured answers.  
+
+---
+
+## 📊 Optimizations  
+
+-  Added query filtering → No LLM call if retrieval fails (reduces cost).  
+-  Handled poor English queries via query restructuring.  
+-  Improved prompt engineering to minimize hallucinations.  
+
+---
+
+## 📌 Future Improvements  
+
+-  Add multilingual support (Odia/Hindi queries).  
+-  Deploy on cloud (AWS/GCP/Azure) with Docker.  
+-  Use advanced embeddings (e.g., `all-mpnet-base-v2`) for higher accuracy.  
+-  Add real-time updates (e.g., cyclone alerts).  
+
+---
+
+## 👨‍💻 Author  
+
+**Subhakanta Rath**  
+
+MSc AI & ML @ IIIT Lucknow  
+
+Passionate about AI/ML, Data Engineering  
+
+