Update README.md

README.md

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-title: Term-4-Project
-app_file: new_app.py
-sdk: gradio
-sdk_version: 4.41.0
----
-# PerplexityClone
-
-Demo: [https://huggingface.co/spaces/YashSax/PerplexityClone](https://huggingface.co/spaces/YashSax/PerplexityClone)
-
-Chat Version Demo (multi-turn + retrieval for each step + query rewrite): [Google Drive Link](https://drive.google.com/file/d/1LCS4RdyI46WhfJabGGkIrgFJFWZq7cDK/view)
-
-A clone of [perplexity.ai](perplexity.ai), an LLM and RAG-powered search engine using LangChain, Chroma Vector Store, and Cohere Text Generation and Embedding Models. Frontend designed using Gradio.
-
-![image](https://github.com/YashSax/PerplexityClone/assets/46911428/014076e5-fa74-4821-b0b0-2298b3a89205)
-
-### Overview
-
-1) The user's query is passed through the Google Search API to create a corpus of the 10 most recent and relevant webpages.
-2) The documents are split and stored into a Chroma vector store
-3) Using Cohere's text embedding model, relevant snippets are collected using embedding similarity
-4) The relevant snippets are compiled into context for the RAG application
-5) The prompt and context are fed into Cohere's Command-R Text Generation LLM, and the output and documents are presented to the user.
+---
+title: Term-4-Project
+app_file: new_app.py
+sdk: gradio
+sdk_version: 4.41.0
+---
+
+
+### Overview
+
+1) The user's query is passed through the Google Search API to create a corpus of the 10 most recent and relevant webpages.
+2) The documents are split and stored into a Chroma vector store
+3) Using Cohere's text embedding model, relevant snippets are collected using embedding similarity
+4) The relevant snippets are compiled into context for the RAG application
+5) The prompt and context are fed into Cohere's Command-R Text Generation LLM, and the output and documents are presented to the user.