Update README.md

README.md

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-# Smart Search Tool for Analytics Vidhya Courses
-# Goal
-To create a smart search tool that enables users to find relevant free courses on Analytics Vidhya’s platform quickly.
-
-# Project Approach
-# Data Collection
-I began by scraping the free courses' titles and relevant metadata, such as course links and images, from Analytics Vidhya’s platform using BeautifulSoup.
-
-# Model Selection
-Originally, I used the Groq API for generating embeddings and conducting searches. However, I found the results less suitable, leading me to switch to a more refined solution using BERT (Bidirectional Encoder Representations from Transformers). I leveraged a pre-trained BERT model (bert-base-uncased from Hugging Face) for generating embeddings.
-
-# Relevance Matching
-To match user queries with relevant courses, I calculated cosine similarity between the user’s query embedding and the course title embeddings. This similarity score enables ranking courses based on relevance, ensuring the most suitable courses are shown first.
-
-# Interface
-The application uses both Streamlit and Shiny for flexible, user-friendly interfaces. These interfaces display course details dynamically, including title, image, link, and relevance score.Finally I can able to conclude that Shiny is more faster in retrieving the results and display those in more interactive way than StreamLit.
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-# Deployment on Hugging Face Spaces
-I deployed the tool on Hugging Face Spaces, providing an accessible, visually appealing interface for public use, enhanced with custom CSS for style and responsiveness.
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-BERT model : google-bert/bert-base-uncased
+title: "Smart Search Tool for Analytics Vidhya Courses"
+emoji: "🔍"
+colorFrom: "blue"
+colorTo: "green"
+sdk: "streamlit"
+sdk_version: "1.26.0"
+app_file: "app.py"
+pinned: false