Introduction:
The objective of this project was to develop a web-based application that optimizes the job application process through Natural Language Processing (NLP) and machine learning. The Real-Time Resume and Job Description Analyzer allows users to upload their resumes in PDF format and job descriptions in TXT format, analyzing the content to provide insights on how well the resume aligns with the job description.

Phase 1: Text Extraction
Initial Approach:
I began by implementing PDF parsing to extract text from resumes, ensuring that I could retrieve the necessary information for analysis.

Challenges Faced:
During this phase, I encountered difficulties in accurately extracting text from various resume formats, which required exploring different libraries to achieve consistent results.

Phase 2: Named Entity Recognition (NER)
Library Selection:
To identify key components in resumes, such as skills, experience, and education, I utilized the spaCy library for Named Entity Recognition (NER).

Accuracy Improvement:
After several iterations, I refined the NER model to enhance its accuracy in extracting relevant information, which is crucial for comparing resumes against job descriptions.

Phase 3: Similarity Analysis
Model Implementation:
I implemented the Sentence Transformers library to compute semantic similarity between the resume and job description. This involved generating embeddings for both documents to evaluate how closely they aligned.

Results:
The similarity analysis provided a percentage score, enabling users to see how well their resumes matched the specific requirements of the job description.

Phase 4: User Interface Development
Gradio Interface:
To make the application user-friendly, I built an intuitive interface using Gradio, allowing users to easily upload their resumes and job descriptions and receive instant feedback.

User Testing:
I conducted user testing sessions to gather feedback, which helped me refine the interface for a seamless experience.

Phase 5: Deployment
Hugging Face Spaces:
The application was deployed on Hugging Face Spaces, making it easily accessible for users. This platform allowed me to share my work and receive valuable insights from the community.

API Security:
I ensured that sensitive information, such as API keys, was securely managed using Hugging Face Secrets, protecting them from unauthorized access.

User Manual for Execution:
https://huggingface.co/spaces/Anushree1/Resumeanalyser
Upload your resume in PDF format and the job description in TXT format.
Click the "submit" button to receive feedback on the alignment between your resume and the job description.

Conclusion:
This project was a significant learning experience, enhancing my skills in NLP, machine learning, and web development. I faced various challenges, particularly in text extraction and similarity analysis, but the effort proved worthwhile as I created a functional and beneficial tool for job seekers and recruiters alike.

Project Demonstration: https://www.linkedin.com/posts/anushree-3a826a254_recruiters-hiringmanagers-ai-activity-7247184862787330048-QiOM?utm_source=share&utm_medium=member_desktop

Real time Resume and JD analyser