Update README.md

README.md

@@ -1,74 +1,79 @@
-# Video Transcript Chatbot
-
-A beginner-friendly Gradio app that turns any YouTube video into a conversational chatbot using LangChain and Hugging Face Inference API.
-
----
-
-## Features
-
-- **Dynamic Video Input**: Paste a full YouTube URL or raw video ID.  
-- **Embedding Model Selection**: Pick any HF embedding model (default: `sentence-transformers/all-MiniLM-L6-v2`).  
-- **LLM Model Selection**: Choose any HF text-generation model (default: `meta-llama/Llama-3.1-8B-Instruct`).  
-- **Secure Token Entry**: You must enter your own HF API token at runtime—no hard-coded defaults.  
-- **Conversational Memory**: Multi-turn chat history is preserved.  
-- **Retrieval-Augmented Generation**: Uses FAISS + transcript context to ground answers.
-
----
-
-## Prerequisites
-
-- **Python 3.8+**  
-- **Hugging Face API Token** with Inference access:  
-  https://huggingface.co/settings/tokens  
-- **Git** (for cloning the repo)
-
----
-
-## Installation
-
-1. **Clone the repo**  
-   ```bash
-   git clone https://github.com/<your-username>/yt-rag-chatbot.git
-   cd yt-rag-chatbot
-2. **(Optional) Create a virtual environment**
-   ```bash
-   python -m venv venv
-   source venv/bin/activate    # macOS/Linux
-   venv\Scripts\activate       # Windows
-3. **Install dependencies**
-   ```bash
-   python -m venv venv
-   source venv/bin/activate    # macOS/Linux
-   venv\Scripts\activate       # Windows
-
-**Usage**
-1. **Start the app:**
-   ```bash
-   python app.py
-2. **Open** your browser at the local URL (e.g. http://127.0.0.1:7860)
-3. **Use the UI:**
-
-- **YouTube Video URL or ID:** Paste your link/ID.
-
-- **Embedding Model:** Leave default or enter another HF embedding model.
-
-- **LLM Model:** Enter your desired HF LLM repo.
-
-- **Your HF API Token:** Paste your token (input hidden).
-
-- Click **Initialize Chat** to load and index the transcript.
-
-- Ask questions in the chat window to interact with the video content.
-
-  
-**Customization**
-
-- **Default Models:** Edit the default values for embedding_model_input and llm_model_input in app.py.
-
-- **Retrieval Size:** Change the k value in the retriever configuration:
-  ```python
-  retriever = vector_store.as_retriever(search_kwargs={'k': 4})
- 
-
-
-   
+---
+title: Video Transcript Chatbot
+emoji: 🎥
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+python_version: '3.10'
+---
+
+# Video Transcript Chatbot
+
+A beginner-friendly Gradio app that turns any YouTube video into a conversational chatbot using LangChain and Hugging Face Inference API.
+
+---
+
+## Features
+
+- **Dynamic Video Input**: Paste a full YouTube URL or raw video ID.  
+- **Embedding Model Selection**: Pick any HF embedding model (default: `sentence-transformers/all-MiniLM-L6-v2`).  
+- **LLM Model Selection**: Choose any HF text-generation model (default: `meta-llama/Llama-3.1-8B-Instruct`).  
+- **Secure Token Entry**: You must enter your own HF API token at runtime—no hard-coded defaults.  
+- **Conversational Memory**: Multi-turn chat history is preserved.  
+- **Retrieval-Augmented Generation**: Uses FAISS + transcript context to ground answers.
+
+---
+
+## Prerequisites
+
+- **Python 3.8+**  
+- **Hugging Face API Token** with Inference access:  
+  https://huggingface.co/settings/tokens  
+- **Git** (for cloning the repo)
+
+---
+
+## Installation
+
+1. **Clone the repo**  
+   ```bash
+   git clone https://github.com/<your-username>/yt-rag-chatbot.git
+   cd yt-rag-chatbot
+2. **(Optional) Create a virtual environment**
+   ```bash
+   python -m venv venv
+   source venv/bin/activate    # macOS/Linux
+   venv\Scripts\activate       # Windows
+3. **Install dependencies**
+   ```bash
+   python -m venv venv
+   source venv/bin/activate    # macOS/Linux
+   venv\Scripts\activate       # Windows
+
+**Usage**
+1. **Start the app:**
+   ```bash
+   python app.py
+2. **Open** your browser at the local URL (e.g. http://127.0.0.1:7860)
+3. **Use the UI:**
+
+- **YouTube Video URL or ID:** Paste your link/ID.
+
+- **Embedding Model:** Leave default or enter another HF embedding model.
+
+- **LLM Model:** Enter your desired HF LLM repo.
+
+- **Your HF API Token:** Paste your token (input hidden).
+
+- Click **Initialize Chat** to load and index the transcript.
+
+- Ask questions in the chat window to interact with the video content.
+
+  
+**Customization**
+
+- **Default Models:** Edit the default values for embedding_model_input and llm_model_input in app.py.
+
+- **Retrieval Size:** Change the k value in the retriever configuration:
+  ```python
+  retriever = vector_store.as_retriever(search_kwargs={'k': 4})