Update README.md

README.md

@@ -1,32 +1,43 @@
+---
+title: AI Research Companion
+emoji: 🔬
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+---
+
 # 🔬 AI Research Companion (Groq + LangChain + FAISS)
 
-An advanced **AI-powered research assistant** that helps you analyze academic papers, ask natural language questions, generate engaging summaries, and discover related research papers — all from a modern, tabbed **Gradio interface**.
+An advanced **AI-powered research assistant** that helps you analyze academic papers, ask natural language questions, generate engaging summaries, and discover related research — all from a modern, tabbed **Gradio interface**.
 
 ---
 
 ## 🚀 Features
 
-✅ **PDF Upload & Text Extraction** – Extracts full text from research papers  
-✅ **Chunking & Vector Embedding** – Uses LangChain + HuggingFace embeddings for semantic search  
-✅ **Groq LLM Q&A** – Powered by `llama-3.3-70b-versatile` for accurate, context-aware answers  
-✅ **Cited Source References** – Displays the exact chunks used for each answer  
-✅ **Research Paper Summarization** – Creates engaging, layperson-friendly summaries  
-✅ **Similar Paper Discovery** – Queries arXiv API to find related academic works  
-✅ **Beautiful Multi-Tab UI** – Fully custom styled with Gradio + CSS  
+- **PDF Upload & Text Extraction** – Extracts full text from research papers  
+- **Chunking & Vector Embedding** – Uses LangChain + HuggingFace embeddings for semantic search  
+- **Groq LLM Q&A** – Powered by `llama-3.3-70b-versatile` for accurate answers  
+- **Cited Source References** – Shows the exact text chunks used  
+- **Research Paper Summaries** – Creates clear, engaging summaries  
+- **Similar Paper Discovery** – Uses arXiv API to find related studies  
+- **Beautiful Multi-Tab UI** – Custom-styled Gradio interface  
 
 ---
 
 ## 🛠 Tech Stack
 
-- **Python 3.9+**
-- [Gradio](https://gradio.app/) – Interactive UI framework
-- [LangChain](https://www.langchain.com/) – Document processing & QA chain
-- [FAISS](https://github.com/facebookresearch/faiss) – Efficient similarity search
-- [HuggingFace Sentence Transformers](https://www.sbert.net/) – Embeddings (`all-mpnet-base-v2`)
-- [Groq API](https://groq.com/) – High-performance LLM inference
-- [PyPDF2](https://pypi.org/project/PyPDF2/) – PDF parsing
-- [Feedparser](https://pypi.org/project/feedparser/) – arXiv paper search
-- **Custom CSS** – Modern tabbed layout, shadows, gradients, and animations
+- Python 3.9+  
+- Gradio  
+- LangChain  
+- FAISS  
+- HuggingFace Sentence Transformers (`all-mpnet-base-v2`)  
+- Groq API  
+- PyPDF2  
+- Feedparser  
+- Custom CSS  
 
 ---
 
@@ -37,58 +48,3 @@ An advanced **AI-powered research assistant** that helps you analyze academic pa
 ```bash
 git clone https://github.com/bobbythomas985/Research_Assistant
 cd Research_Assistant
-```
-### 2️⃣ Install Dependencies
-```bash
-pip install -r requirements.txt
-```
-### 3️⃣ Set Up Your API Key
-Export your Groq API key as an environment variable:
-**Linux / macOS**
-```bash
-export GROQ_API_KEY="your_api_key_here"
-```
-**Windows**
-```powershell
-setx GROQ_API_KEY "your_api_key_here"
-```
-Alternatively, replace the placeholder in **app.py**:
-```python
-GROQ_API_KEY = os.getenv("GROQ_API_KEY", "your-api-key")
-```
-### 4️⃣ Run the App
-```bash
-python app.py
-```
-
----
-
-## 🖥️ How It Works
-
-1️⃣ **Upload a PDF**  
-📄 The system extracts all text from the research paper.
-
-2️⃣ **Process & Embed**  
-🔍 Splits the extracted text into overlapping chunks and creates a **FAISS vector index** using **HuggingFace embeddings** for efficient semantic search.
-
-3️⃣ **Ask Questions**  
-❓ User questions are converted into embeddings and matched with the most relevant chunks from the document.
-
-4️⃣ **LLM Answer Generation**  
-🤖 Groq’s `llama-3.3-70b-versatile` model is used to generate accurate, context-aware answers with a custom prompt.
-
-5️⃣ **Summarize & Discover Papers**  
-📝 Generates engaging, structured summaries of the document and retrieves similar papers from **arXiv** for further reading.
-
-
-## 🔮 Future Improvements
-
-- 📚 **Multi-document support** – Build a single knowledge base from multiple PDFs  
-- 📷 **LLM Reranking** – Use cross-encoder reranking for better context selection  
-- 📑 **Clickable Source References** – Jump directly to relevant sections inside the PDF  
-- 🚀 **Deploy on Hugging Face Spaces / Streamlit Cloud** – Make it public and shareable  
-- 🌍 **Multilingual Q&A** – Integrate translation for global research accessibility
-
----
-
-> *Empowering researchers to go from papers → insights → new discoveries.*