Imama Kainat@Nataki

AutoRecruit is the AI-driven application aiming to revolutionize how corporate entities hire their new team members, drastically reducing time and cost. In this fast-moving business world, a reliable and efficient team is the prime factor for any startup or company to achieve growth and success. The traditional ways of hiring may consume 3 to 4 months of time and huge finances in HR activities. AutoRecruit resolves such challenges with a solution that assures credible hires within 4 to 5 days. Our platform embeds multiple layers of testing and evaluation, making use of the advanced capabilities that the Llama 3 model provides in conducting market-standard coding rounds. This confirms that the candidates are technically sound and fit for their roles. Company registration is followed by resume uploads, technical and skill-based evaluation, and finally, the selection of the most suitable candidates. It leverages a powerful tech stack that includes React.js at the frontend, Node.js at the backend, powered by Express.js, MongoDB as the database, and VS Code, GitHub, Git, Humaniz AI for tools. Enabling companies to quickly and efficiently construct strong and effective teams—those that will drive project success and organizational growth—AutoRecruit automates and optimizes the recruitment process.

ChatCinema is a multifaceted Streamlit application that combines a sophisticated movie information chatbot with advanced data processing and generation capabilities. The project integrates various cutting-edge technologies to create a versatile platform for movie enthusiasts, data scientists, and AI researchers. At its core, ChatCinema features a highly interactive movie chatbot. This chatbot utilizes a CSV file ('Hydra-Movie-Scrape.csv') as its primary data source, containing a wealth of information about various movies. To enable efficient and relevant movie retrieval, the application employs the 'all-MiniLM-L6-v2' sentence transformer model to generate embeddings for movie summaries. These embeddings are then used in conjunction with cosine similarity calculations to find the most relevant movie based on user queries. The chatbot's natural language processing capabilities are powered by the Groq API, specifically using the 'llama3-8b-8192' model. This integration allows for dynamic and context-aware responses to user inquiries. When a user inputs a movie-related query, the system retrieves the most similar movie from its database and uses this information as context for generating a response. The output includes comprehensive movie details such as title, year, summary, genres, IMDB ID, YouTube trailer link, rating, movie poster URL, director, writers, and cast information. Additionally, the chatbot generates relevant dialogues or additional information about the movie using the AI model. A key feature of ChatCinema is its ability to maintain and manage chat history. The application stores conversation logs in Streamlit's session state, allowing for a continuous and contextual chat experience. Users have the option to download their chat history, which is provided as an encrypted CSV file for enhanced privacy and security.

NetDeploy AI is an AI-powered tool designed to assist telecom engineers, ISPs, and businesses in efficiently planning network deployments. It utilizes Hugging Face's Falcon-7B-Instruct model to generate detailed network deployment strategies based on user inputs such as location, terrain, budget, estimated users, coverage area, and technology preferences. Key Features: AI-Powered Network Planning – Generates recommendations on network topology, infrastructure, cost estimation, scalability, and challenges. Interactive Mapping – Uses Folium to visualize deployment locations with coverage areas. Comparison Feature – Allows users to compare AI-generated plans with their own strategies for better decision-making. User-Friendly Interface – Built with Gradio, featuring a clean UI with a sidebar for easy navigation. Custom Styling – The app includes custom CSS styling for a professional look and feel. Technical Components: Requests: API calls to Hugging Face for AI-generated responses. Folium & MarkerCluster: For interactive mapping and coverage visualization. JSON Processing: Formatting AI responses for better readability. Error Handling & Retry Logic: Ensures reliable AI query execution. Gradio UI: Provides an intuitive web interface. Team Behind NetDeploy AI: Imama Kainat Abdul Hanan Muhammad Hamza