Muhammad Muavia Ateeq@Muavia

CV Parser is a web-based AI application designed to automate the extraction and analysis of resume data. Users can upload resumes in PDF, JPG, or PNG format. The app performs the following: Text Extraction: Uses PyMuPDF for digital PDFs and Tesseract OCR for scanned documents and images. AI-Powered Analysis: Sends extracted resume text to the Groq-hosted LLaMA 3 model to: Parse structured JSON fields (name, email, phone, etc.) Recommend 2–3 relevant career fields based on the resume content Data Storage: Automatically logs parsed results to a connected Google Sheet for easy access, reporting, or recruitment pipelines. This tool helps recruiters, HR platforms, and job portals automate resume handling and job matching processes efficiently using cutting-edge LLMs and OCR technologies. Technology & Category Tags AI OCR LLaMA3 Groq API Resume Parsing Google Sheets API Gradio Python Document Analysis Job Matching PDF Processing Tesseract PyMuPDF

Text2Speech AI is a powerful generative application that transforms user-input text into lifelike spoken audio using advanced natural language processing and speech synthesis models. Designed to provide high-quality, natural-sounding voice output, the app utilizes a combination of large language models (LLMs) and text-to-speech (TTS) engines to generate context-aware, expressive audio. The application is built using tool such as Gradio for a user-friendly frontend, while Google Colab supports the backend environment for fast prototyping and scalability. It leverages Hugging Face models for TTS tasks, integrates RAG (Retrieval-Augmented Generation) for improved text interpretation, and uses PyTorch, Torchaudio, and SoundFile for audio processing and output. By combining these cutting-edge technologies, Text2Speech AI enables real-time, natural voice generation suitable for educational tools, accessibility applications, audiobooks, and voice assistants.

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