From 250f0b9ba3f38b504bed2e613535e5cbd6e2503b Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sat, 17 Aug 2024 15:48:23 -0700 Subject: [PATCH 01/22] ENH #30: Update demo data --- .anima/.gitignore | 1 - .gitignore | 4 +- zlm/demo_data/user_profile.json | 555 +++++++++++++++++--------------- 3 files changed, 296 insertions(+), 264 deletions(-) delete mode 100644 .anima/.gitignore diff --git a/.anima/.gitignore b/.anima/.gitignore deleted file mode 100644 index 5e46596..0000000 --- a/.anima/.gitignore +++ /dev/null @@ -1 +0,0 @@ -cache \ No newline at end of file diff --git a/.gitignore b/.gitignore index 71b42f3..248627a 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,6 @@ __pycache__ .env output *.DS_Store -dist \ No newline at end of file +dist +.venv +.anima \ No newline at end of file diff --git a/zlm/demo_data/user_profile.json b/zlm/demo_data/user_profile.json index 20b1e7f..93bb9f2 100644 --- a/zlm/demo_data/user_profile.json +++ b/zlm/demo_data/user_profile.json @@ -1,276 +1,307 @@ { - "name": "John Doe", - "summary": "Versatile and self-driven professional with 4+ years of industrial experience in crafting production-ready AI Architecture and full-stack softwares through coherent analysis, and relentless focus on efficient solutions with meticulous execution.", - "phone": "+1 444 555 6666", - "email": "john.doe@gmail.com", + "name": "Saurabh Zinjad", + "title": "I build AI systems that solve real-world problems.", + "summary": "Versatile and self-driven professional with 4.5+ years of industrial experience in crafting production-ready AI system and full-stack softwares through coherent analysis, and relentless focus on efficient solutions with meticulous execution.", + "phone": "+1 480 913 5544", + "email": "saurabhzinjad@gmail.com", "media": { - "linkedin": "linkedin.com/in/johndoe", - "github": "github.com/johndoe", - "devpost": "devpost.com/johndoe", - "medium": "ztrimus.medium.com", - "leetcode": "leetcode.com/johndoe", - "dagshub": "https://dagshub.com/johndoe", - "kaggle": "https://www.kaggle.com/johndoe", - "instagram": "https://www.instagram.com/johndoe/" + "linkedin": "linkedin.com/in/saurabhzinjad", + "github": "github.com/Ztrimus", + "medium": "ztrimus.medium.com", + "devpost": "devpost.com/Ztrimus" }, + "projects": [ + { + "name": "ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement", + "type": "Publication", + "link": "https://dl.acm.org/doi/10.1145/3626772.3657680", + "resources": [ + { + "name": "Website", + "link": "https://job-aligned-resume.streamlit.app/" + }, + { + "name": "Code Repo", + "link": "https://github.com/Ztrimus/ResumeFlow" + } + ], + "description": [ + "Led as First Author in developing \"ResumeFlow,\" an innovative tool leveraging Large Language Models (LLMs) like GPT-4 and Google's Gemini, enabling users to generate personalized resumes tailored to specific job postings within seconds.", + "Engineered a sophisticated pipeline integrating advanced LLM capabilities for information extraction and language understanding, employing prompt engineering and web scraping techniques to extract job and resume details efficiently.", + "Designed and implemented novel evaluation metrics (job_alignment and user_personalization) to quantify resume customization effectiveness and prevent potential hallucinations, enhancing the ethical use of AI in resume generation.", + "Presented short paper at ACM SIGIR 2024 Conference, showcasing the tool's capabilities and potential impact on the recruitment industry." + ] + }, + { + "name": "Search Engine for All file types - Sunhack Hackathon - Meta & Amazon Sponsored", + "type": "Hackathon", + "link": "https://devpost.com/software/team-soul-1fjgwo", + "from": "Nov 2023", + "to": "Nov 2023", + "description": [ + "Big Data Awards in Python FAST API and angular development, providing efficient data access and retrieval.", + "Converted and stored every file type data as vector embeddings, ensuring low-latency search capabilities.", + "Used Machine Learning techniques such as BERT, OCR, ResNet50, and Image Captioning to parse Image features.", + "Contributed to Elasticsearch implementation for blazing-fast search responses, with millisecond response times." + ] + }, + { + "name": "GenAI’s Capabilities and Boundaries Exploration - Prompt Engineering Hackathon for Humanities", + "type": "Hackathon", + "link": "https://www.linkedin.com/pulse/exploring-depths-ai-story-writing-prompt-engineering-hackathon-wbqwc", + "from": "Oct 2023", + "to": "Oct 2023", + "description": [ + "1st runner up prize in crafted AI persona, to explore LLM's subtle contextual understanding and create innovative collaborations between humans and machines.", + "Addressed limitations in narrative flow, simplicity, emotional depth, and hallucinations through innovative approaches.", + "Demonstrated creative mindset and ability to navigate complex tasks and adapt to evolving requirements during hackathon." + ] + }, + { + "name": "Forest Fire Detection using IoT Sensor Data", + "type": "Industrial Project", + "link": "https://github.com/OmdenaAI/Dryad", + "from": "Sept 2021", + "to": "Jan 2022", + "description": [ + "Devised a TabNet Classifier Model having 98.7% accuracy in detecting forest fire through IoT sensor data, deployed on AWS and edge devices 'Silvanet Wildfire Sensors' using technologies TinyML, Docker, Redis, and celery.", + "Examine and utilize many performance metrics (Recall, F2 score, sensitivity, specificity. etc.) to reduce high type II error.", + "Performed Model Exploration, Analysis, and Optimization." + ] + }, + { + "name": "Stock Market Analysis", + "type": "Intern Project", + "link": "https://github.com/Ztrimus/Stock-Market-Analysis", + "from": "Dec 2018", + "to": "Feb 2019", + "description": [ + "Conducted in-depth Exploratory Data Analysis (EDA) and utilized data visualization techniques for comprehensive stock market analysis. Implemented a range of statistical and ML models on diverse time-series stocks to extract insights and predictions. Improved performance by 27% on the “clustering and diversification analysis”." + ] + }, + { + "name": "Autonomous Surveillance Monitoring System", + "type": "Project", + "link": "", + "from": "Feb 2019", + "to": "Jun 2019", + "description": [ + "At a college-sponsored project, I Built a surveillance engine to detect and alert about suspicious behaviors on campus by constructing a computer vision pipeline of CCTV footage data processing, face detection, poses & action recognition using OpenCV, MediaPipe, Tensorflow, MLFlow, and Flask. Finally, We deployed it on college premises." + ] + }, + { + "name": "Speech Emotion Recognition", + "type": "Project", + "link": "https://github.com/Ztrimus/speech-emotion-recognition", + "from": "Nov 2018", + "to": "Feb 2019", + "description": [ + "Researched and optimized existing emotion detection approaches by combining CNN and LSTM networks. Discovered emotion-affecting attributes in voice by analyzing audio signal features-MFCC, ZCR, Pitch, and Chroma.", + "Compressed audio data using an Autoencoder technique to avoid data loss. Due to this, we were able to boost the accuracy of the speech model by 31%. Used tools like PyTorch, Librosa, puAudioAnalysis, and Tensorboard." + ] + }, + { + "name": "Homecoming: Animal Habitat Organization", + "type": "Project", + "link": "", + "from": "Aug 2018", + "to": "Jan 2019", + "description": [ + "Developed a Custom Animal Identification and Classification model using Faster R-CNN architecture to identify animals and their habitats in a simulated environment, which Integrated it into the Firebird V ATMEGA2560 Robot (we tailored it to put animals into a habitat). Optimized the \"Region Proposal Network\" component resulting in 35% decrease in processing time." + ] + } + ], "education": [ - { - "degree": "Masters of Science - Computer Science - Thesis Advised by Dr. Huan Liu, Amrita Bhattacharjee", - "university": "Arizona State University, Tempe, USA", - "from": "Aug 2023", - "to": "May 2025", - "grade": "3.8/4", - "coursework": [ - "Operationalizing Deep Learning", - "AI Safety and Assessment", - "Social Media Mining", - "Statistical Machine Learning", - "Software verification, Validation and Testing", - "Knowledge Representation and Reasoning Algorithms" - ] - }, - { - "degree": "Bachelor of Engineering", - "university": "Pune Institute of Computer Technology(PICT), Savitribai Phule Pune University, India", - "from": "Jul 2015", - "to": "Jun 2019", - "grade": "8.53/10", - "classes": [ - "DSA", - "OOP", - "OS", - "System Programming", - "Computer Networks", - "AI", - "ML", - "Digital Video & Image Processing" - ] - } + { + "degree": "Masters of Science - Computer Science (Thesis)", + "university": "Arizona State University, Tempe, USA", + "from": "Aug 2023", + "to": "May 2025", + "coursework": [ + "Statistical Machine Learning", + "Operational Deep Learning", + "Software verification, Validation and Testing", + "AI Safety and Assessment", + "Social Media Mining", + "Knowledge Representation and Reasoning Algorithms" + ] + }, + { + "degree": "Bachelor of Engineering", + "university": "Pune Institute of Computer Technology(PICT), Savitribai Phule Pune University, India", + "from": "Jul 2015", + "to": "Jun 2019", + "grade": "8.53/10", + "classes": [ + "DSA", + "OOP", + "OS", + "System Programming", + "Computer Networks", + "AI", + "ML", + "Digital Video & Image Processing" + ] + } ], "skill_section": [ - { - "name": "Programming Languages", - "skills": [ - "Python", - "JavaScript", - "C#", - "C++", - "SQL", - "R", - "Java", - "Shell Scripting" - ] - }, - { - "name": "Data Science", - "skills": [ - "Databricks", - "PySpark", - "TensorFlow", - "PyTorch", - "MXNet", - "OpenCV", - "Scikit Learn", - "Pandas", - "Matplotlib", - "Keras" - ] - }, - { - "name": "Cloud and DevOps", - "skills": [ - "Azure", - "AWS", - "Docker", - "Kubernetes", - "MLFlow", - "Jupyter Notebook", - "Git" - ] - }, - { - "name": "Full-Stack Tech", - "skills": [ - "Angular", - "React", - ".Net Core", - "NodeJs", - "Django", - "Flask", - "FastAPI", - "MongoDB", - "SQL Server", - "MySQL", - "Postman" - ] - } + { + "name": "Programming Languages", + "skills": [ + "Python", + "JavaScript", + "C#", + "C++", + "SQL", + "R", + "Java", + "Shell Scripting" + ] + }, + { + "name": "Data Science", + "skills": [ + "Databricks", + "PySpark", + "TensorFlow", + "PyTorch", + "MXNet", + "OpenCV", + "Scikit Learn", + "Pandas", + "Matplotlib", + "Keras" + ] + }, + { + "name": "Cloud and DevOps", + "skills": [ + "Azure", + "AWS", + "Docker", + "Kubernetes", + "MLFlow", + "Jupyter Notebook", + "Git" + ] + }, + { + "name": "Full-Stack Tech", + "skills": [ + "Angular", + "React", + ".Net Core", + "NodeJs", + "Django", + "Flask", + "FastAPI", + "MongoDB", + "SQL Server", + "MySQL", + "Postman" + ] + } ], "work_experience": [ - { - "role": "Senior Machine Learning Engineer", - "company": "Tiger Analytics, Bangalore, India", - "from": "Jun 2022", - "to": "Jul 2023", - "description": [ - "Spearheaded development of Data & CI/CD pipelines, Interactive Dashboards, Constraint-based ML Models, Web App, and Comprehensive Documentation for MSP Value Optimization in Petcare sector with a team of 8 analysts. (Team Leadership)", - "Developed MLCORE product (end-to-end MLOps platform) by implementing research ideas, organizing through prototyping, and Integrating it with numerous cloud services, attracting an additional four significant clients. (Interpersonal Skills)" - ] - }, - { - "role": "Software Engineer", - "company": "Winjit Technologies, Pune, India", - "from": "Jan 2020", - "to": "Jun 2022", - "description": [ - "Engineered 10+ RESTful APIs Architecture and Distributed services; Designed 30+ low-latency responsive UI/UX application features with high-quality web architecture; Managed and optimized large-scale Databases. (Systems Design)", - "Initiated and Designed a standardized solution for dynamic forms generation, with customizable CSS capabilities feature, which reduces development time by 8x; Led and collaborated with a 12 member cross-functional team. (Idea Generation)" - ] - }, - { - "role": "Deep Learning Engineer", - "company": "Automation Teknix, Pune, India", - "from": "Sept 2019", - "to": "Jan 2020", - "description": [ - "Devised a Lightweight Object Recognition Engine with a low computational cost by leveraging an SSD algorithm with MobilenetV2 architecture, which decreased survey error by 22%. (Problem Identification)", - "Conducted thorough Initial research; prototyped neural network flow; conceptualized POC, training, and monitoring of models. This resulted in a 7% increase in accuracy and reduced inference time by 2x. (Experiment Design)" - ] - }, - { - "role": "Research Intern", - "company": "IMATMI, Robbinsville, New Jersey (Remote)", - "from": "Mar 2019", - "to": "Aug 2019", - "description": [ - "Conducted research and developed a range of ML and statistical models to design analytical tools and streamline HR processes, optimizing talent management systems for increased efficiency.", - "Created 'goals and action plan generation' tool for employees, considering their weaknesses to facilitate professional growth.", - "Implemented an automated system to identify 'best-fit employees' for upcoming projects, improving project outcomes and resource allocation for managers." - ] - } - ], - "projects": [ - { - "name": "LLM-facilitated Pipeline for Personalized Resume Generation", - "type": "Project", - "link": "https://github.com/Ztrimus/job-llm", - "from": "Nov 2023", - "to": "Feb 2024", - "description": [ - "As the First Author, Led the proposal and development of \"ResumeFlow,\" a pioneering tool powered by Large Language Models (LLMs), enabling users to generate personalized resumes tailored to specific job postings in seconds.", - "Designed an innovative pipeline utilizing the advanced capabilities of LLMs (e.g., GPT-4, Google's Gemini) for information extraction and language understanding, applying prompt engineering and web scraping to extract job and resume details, and generate tailored resumes.", - "Developed Python library to enable seamless integration and optimized with any job application workflow and system." - ] - }, - { - "name": "Search Engine for All file types - Sunhack Hackathon - Meta & Amazon Sponsored", - "type": "Hackathon", - "link": "https://devpost.com/software/team-soul-1fjgwo", - "from": "Nov 2023", - "to": "Nov 2023", - "description": [ - "Big Data Awards in providing efficient data access and retrieval using FAST APIs and angular development.", - "Converted and stored every file type data as vector embeddings, ensuring low-latency search capabilities.", - "Used Machine Learning techniques such as BERT, OCR, ResNet50, and Image Captioning to parse Image features.", - "Contributed to Elasticsearch implementation for blazing-fast search responses, with millisecond response times." - ] - }, - { - "name": "GenAI’s Capabilities and Boundaries Exploration - Prompt Engineering Hackathon for Humanities", - "type": "Hackathon", - "link": "https://www.linkedin.com/pulse/exploring-depths-ai-story-writing-prompt-engineering-hackathon-wbqwc", - "from": "Oct 2023", - "to": "Oct 2023", - "description": [ - "1st runner up prize in crafted AI persona, to explore LLM's subtle contextual understanding and create innovative collaborations between humans and machines.", - "Addressed limitations in narrative flow, simplicity, emotional depth, and hallucinations through innovative approaches.", - "Demonstrated creative mindset and ability to navigate complex tasks and adapt to evolving requirements during hackathon." - ] - }, - { - "name": "Forest Fire Detection using IoT Sensor Data", - "type": "Industrial Project", - "link": "https://github.com/OmdenaAI/Dryad", - "from": "Sept 2021", - "to": "Jan 2022", - "description": [ - "Devised a TabNet Classifier Model having 98.7% accuracy in detecting forest fire through IoT sensor data, deployed on AWS and edge devices 'Silvanet Wildfire Sensors' using technologies TinyML, Docker, Redis, and celery.", - "Examine and utilize many performance metrics (Recall, F2 score, sensitivity, specificity. etc.) to reduce high type II error.", - "Performed Model Exploration, Analysis, and Optimization." - ] - }, - { - "name": "Stock Market Analysis", - "type": "Intern Project", - "link": "https://github.com/Ztrimus/Stock-Market-Analysis", - "from": "Dec 2018", - "to": "Feb 2019", - "description": [ - "Conducted in-depth Exploratory Data Analysis (EDA) and utilized data visualization techniques for comprehensive stock market analysis. Implemented a range of statistical and ML models on diverse time-series stocks to extract insights and predictions. Improved performance by 27% on the “clustering and diversification analysis”." - ] - }, - { - "name": "Autonomous Surveillance Monitoring System", - "type": "Project", - "link": "", - "from": "Feb 2019", - "to": "Jun 2019", - "description": [ - "At a college-sponsored project, I Built a surveillance engine to detect and alert about suspicious behaviors on campus by constructing a computer vision pipeline of CCTV footage data processing, face detection, poses & action recognition using OpenCV, MediaPipe, Tensorflow, MLFlow, and Flask. Finally, We deployed it on college premises." - ] - }, - { - "name": "Speech Emotion Recognition", - "type": "Project", - "link": "https://github.com/Ztrimus/speech-emotion-recognition", - "from": "Nov 2018", - "to": "Feb 2019", - "description": [ - "Researched and optimized existing emotion detection approaches by combining CNN and LSTM networks. Discovered emotion-affecting attributes in voice by analyzing audio signal features-MFCC, ZCR, Pitch, and Chroma.", - "Compressed audio data using an Autoencoder technique to avoid data loss. Due to this, we were able to boost the accuracy of the speech model by 31%. Used tools like PyTorch, Librosa, puAudioAnalysis, and Tensorboard." - ] - }, - { - "name": "Homecoming: Animal Habitat Organization", - "type": "Project", - "link": "", - "from": "Aug 2018", - "to": "Jan 2019", - "description": [ - "Developed a Custom Animal Identification and Classification model using Faster R-CNN architecture to identify animals and their habitats in a simulated environment, which Integrated it into the Firebird V ATMEGA2560 Robot (we tailored it to put animals into a habitat). Optimized the \"Region Proposal Network\" component resulting in 35% decrease in processing time." - ] - } + { + "role": "AI Summer Intern", + "company": "University of Phoenix AZ USA", + "from": "May 2024", + "to": "Present", + "description": [ + "Led development of an AI-powered student note summarization tool in a winning hackathon, significantly improving advisor efficiency and enhancing student experience.", + "Developed an innovative AI-based complex calculation explanation system, integrating multiple data sources (like log files and code repositories) and APIs to improve student support resolution speed by 6x. (Innovative Approach)", + "Implemented critical Salesforce backend logic for tracking future out-of-pocket payments in excess funds cases, streamlining financial processes and improving data accuracy.", + "Key Technologies: Python, Salesforce, Amazon Bedrock, LangChain, GPT4o, Claude Sonnet, Llama 3" + ] + }, + { + "role": "Data Analyst", + "company": "Decision Theater at Arizona State University, Tempe, USA", + "from": "Jan 2024", + "to": "Present", + "description": [ + "Developed and implemented advanced data downscaling techniques, increasing resolution from PUMS to block level, and utilized clustering algorithms (k-means, DBSCAN, hierarchical) to analyze high-resolution patterns, enhancing granularity of policy insights by 40%.", + "Engineered custom Python libraries for synthetic data generation and analysis, integrating machine learning models for data block property prediction and classification, resulting in a 30% improvement in data processing efficiency.", + "Conducted comprehensive statistical analyses, including correlation studies (Pearson, cosine similarity) and property extraction, creating impactful visualizations and reports that directly informed policymaker decisions and stakeholder communications." + ] + }, + { + "role": "Senior Machine Learning Engineer", + "company": "Tiger Analytics, Bangalore, India", + "from": "Jun 2022", + "to": "Jul 2023", + "description": [ + "Spearheaded development of Data & CI/CD pipelines, Interactive Dashboards, Constraint-based ML Models, Web App, and Comprehensive Documentation for MSP Value Optimization in Petcare sector with a team of 8 analysts. (Team Leadership)", + "Developed MLCORE product (end-to-end MLOps platform) by implementing research ideas, organizing through prototyping, and Integrating it with numerous cloud services, attracting an additional four significant clients. (Interpersonal Skills)" + ] + }, + { + "role": "Software Engineer", + "company": "Winjit Technologies, Pune, India", + "from": "Jan 2020", + "to": "Jun 2022", + "description": [ + "Engineered 10+ RESTful APIs Architecture and Distributed services; Designed 30+ low-latency responsive UI/UX application features with high-quality web architecture; Managed and optimized large-scale Databases. (Systems Design)", + "Initiated and Designed a standardized solution for dynamic forms generation, with customizable CSS capabilities feature, which reduces development time by 8x; Led and collaborated with a 12 member cross-functional team. (Idea Generation)" + ] + }, + { + "role": "Deep Learning Engineer", + "company": "Automation Teknix, Pune, India", + "from": "Sept 2019", + "to": "Jan 2020", + "description": [ + "Devised a Lightweight Object Recognition Engine with a low computational cost by leveraging an SSD algorithm with MobilenetV2 architecture, which decreased survey error by 22%. (Problem Identification)", + "Conducted thorough Initial research; prototyped neural network flow; conceptualized POC, training, and monitoring of models. This resulted in a 7% increase in accuracy and reduced inference time by 2x. (Experiment Design)" + ] + }, + { + "role": "Research Intern", + "company": "IMATMI, Robbinsville, New Jersey (Remote)", + "from": "Mar 2019", + "to": "Aug 2019", + "description": [ + "Conducted research and developed a range of ML and statistical models to design analytical tools and streamline HR processes, optimizing talent management systems for increased efficiency.", + "Created 'goals and action plan generation' tool for employees, considering their weaknesses to facilitate professional growth.", + "Implemented an automated system to identify 'best-fit employees' for upcoming projects, improving project outcomes and resource allocation for managers." + ] + } ], "certifications": [ - { - "name": "Deep Learning Specialization", - "by": "DeepLearning.AI, Coursera Inc.", - "link": "https://www.coursera.org/account/accomplishments/specialization/G3WPNWRYX628" - }, - { - "name": "MLOps for AI Engineers and Data Scientists", - "by": "Omdena Inc.", - "link": "https://drive.google.com/file/d/1fh4AEscb0P82nfoaA-muWMzmFngzyk2g/view" - }, - { "name": "Microsoft Azure Fundamentals", "by": "Udemy.", - "link": "https://www.udemy.com/certificate/UC-37e943d1-e7f7-4d27-a1e4-e799aef7013b/" }, - { - "name": "Server-side Backend Development", - "by": "The Hong Kong University of Science and Technology.", - "link": "https://www.coursera.org/account/accomplishments/verify/TYMQX23D4HRQ" - }, - { - "name": "Front-End Web UI Frameworks and Tools", - "by": "The Hong Kong University of Science and Technology.", - "link": "https://www.coursera.org/account/accomplishments/verify/TW8MA6MEXSCZ" - } + { + "name": "Deep Learning Specialization", + "by": "DeepLearning.AI, Coursera Inc.", + "link": "https://www.coursera.org/account/accomplishments/specialization/G3WPNWRYX628" + }, + { + "name": "MLOps for AI Engineers and Data Scientists", + "by": "Omdena Inc.", + "link": "https://drive.google.com/file/d/1fh4AEscb0P82nfoaA-muWMzmFngzyk2g/view" + }, + { + "name": "Microsoft Azure Fundamentals", + "by": "Udemy.", + "link": "https://www.udemy.com/certificate/UC-37e943d1-e7f7-4d27-a1e4-e799aef7013b/" + }, + { + "name": "Server-side Backend Development", + "by": "The Hong Kong University of Science and Technology.", + "link": "https://www.coursera.org/account/accomplishments/verify/TYMQX23D4HRQ" + }, + { + "name": "Front-End Web UI Frameworks and Tools", + "by": "The Hong Kong University of Science and Technology.", + "link": "https://www.coursera.org/account/accomplishments/verify/TW8MA6MEXSCZ" + } ], "achievements": [ - "1st runner-up in “Prompt Engineering Hackathon 2023 for Humanities”", - "Received the 'Extra Miller - 2021' award at Winjit Technologies for outstanding performance. ", - "President of Machine Learning Club: Led a team of 20 people in a project and was awarded \"Best Project of the Year.\"", - "Finalist in E-yantra Robotics Competition 2018 - IITB.", - "Dance Section's Head of PICT Art Circle: Best dance choreography for Winning \"Firodiya theater competition 2019.\"", - "Performed in multiple award-winning state-level drama competitions and received the best-organized team prize thrice.", - "An active member of the NSS (National Community Service Group in PICT) in 2016." + "1st runner-up in “Prompt Engineering Hackathon 2023 for Humanities”", + "Received the 'Extra Miller - 2021' award at Winjit Technologies for outstanding performance. ", + "President of Machine Learning Club: Led a team of 20 people in a project and was awarded \"Best Project of the Year.\"", + "Finalist in E-yantra Robotics Competition 2018 - IITB.", + "Dance Section's Head of PICT Art Circle: Best dance choreography for Winning \"Firodiya theater competition 2019.\"", + "Performed in multiple award-winning state-level drama competitions and received the best-organized team prize thrice.", + "An active member of the NSS (National Community Service Group in PICT) in 2016." ] } \ No newline at end of file From a4901b3e2f807b27260d0bc6c2ee552c5bd5e7ef Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sat, 17 Aug 2024 15:50:46 -0700 Subject: [PATCH 02/22] ENH #28: Update dependancies and requirements file --- poetry.lock | 1109 +++++++++++++++++++----------------- pyproject.toml | 23 +- resources/requirements.txt | 100 ++++ web_app.py | 2 +- 4 files changed, 706 insertions(+), 528 deletions(-) create mode 100644 resources/requirements.txt diff --git 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+[package.extras] +crypto-eth-addresses = ["eth-hash[pycryptodome] (>=0.7.0)"] + [[package]] name = "watchdog" version = "4.0.1" @@ -2880,4 +2963,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.11.6" -content-hash = "9c5afbc5e4e8fce4ff0c3ec641c82e04cf85c79a600cc726ccb70368d48f9f8b" +content-hash = "992608d80c17b41958717916dfb4c98296cc791e770fdcd7f04dad45c7df9d9f" diff --git a/pyproject.toml b/pyproject.toml index 335a313..c753a36 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,23 +7,18 @@ readme = "README.md" [tool.poetry.dependencies] python = "^3.11.6" -openai = "^1.5.0" -beautifulsoup4 = "^4.12.2" +streamlit = "^1.37.1" +validators = "^0.33.0" fpdf = "^1.7.2" +langchain-core = "^0.2.33" +langchain-community = "^0.2.12" +pdf2image = "^1.17.0" +openai = "^1.41.0" +google-generativeai = "^0.7.2" pypdf2 = "^3.0.1" -jinja2 = "^3.1.2" -requests = "^2.31.0" -google-generativeai = "^0.3.2" -pandas = "^2.1.4" -streamlit = "^1.35.0" +beautifulsoup4 = 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+grpcio-status==1.62.3 +h11==0.14.0 +httpcore==1.0.5 +httplib2==0.22.0 +httpx==0.27.0 +idna==3.7 +Jinja2==3.1.4 +jiter==0.5.0 +joblib==1.4.2 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +langchain==0.2.14 +langchain-community==0.2.12 +langchain-core==0.2.33 +langchain-text-splitters==0.2.2 +langsmith==0.1.99 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +marshmallow==3.21.3 +mdurl==0.1.2 +multidict==6.0.5 +mypy-extensions==1.0.0 +nltk==3.8.1 +numpy==1.26.4 +openai==1.41.0 +orjson==3.10.7 +packaging==23.2 +pandas==2.2.2 +pdf2image==1.17.0 +pillow==10.4.0 +proto-plus==1.24.0 +protobuf==4.25.3 +pyarrow==16.1.0 +pyasn1==0.6.0 +pyasn1_modules==0.4.0 +pydantic==2.8.2 +pydantic_core==2.20.1 +pydeck==0.9.1 +Pygments==2.18.0 +pyparsing==3.1.2 +PyPDF2==3.0.1 +python-dateutil==2.9.0.post0 +pytz==2024.1 +PyYAML==6.0.2 +referencing==0.35.1 +regex==2024.7.24 +requests==2.32.3 +rich==13.7.1 +rpds-py==0.19.0 +rsa==4.9 +scikit-learn==1.5.1 +scipy==1.14.0 +six==1.16.0 +smmap==5.0.1 +sniffio==1.3.1 +soupsieve==2.6 +SQLAlchemy==2.0.32 +streamlit==1.37.1 +tenacity==8.5.0 +threadpoolctl==3.5.0 +toml==0.10.2 +toolz==0.12.1 +tornado==6.4.1 +tqdm==4.66.5 +typing-inspect==0.9.0 +typing_extensions==4.12.2 +tzdata==2024.1 +uritemplate==4.1.1 +urllib3==2.2.2 +validators==0.33.0 +yarl==1.9.4 diff --git a/web_app.py b/web_app.py index 153fc48..3c20581 100644 --- a/web_app.py +++ b/web_app.py @@ -134,7 +134,7 @@ def create_overleaf_button(resume_path): st.toast(":red[Please enter a job posting URL or paste the job description to get started]", icon="⚠️") st.stop() - if api_key == "" and provider is not "gemini-pro": + if api_key == "" and provider != "gemini-pro": st.toast(":red[Please enter the API key to get started]", icon="⚠️") st.stop() From e1de657d55a43123523c4677c30da518b112148c Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:04:31 -0700 Subject: [PATCH 03/22] ENH #30: Improve web content reading ability for linkedIn --- zlm/utils/data_extraction.py | 82 +++++++++++++++++++++++++++--------- zlm/utils/utils.py | 21 +-------- 2 files changed, 63 insertions(+), 40 deletions(-) diff --git a/zlm/utils/data_extraction.py b/zlm/utils/data_extraction.py index 006c775..9bce695 100644 --- a/zlm/utils/data_extraction.py +++ b/zlm/utils/data_extraction.py @@ -13,6 +13,66 @@ import requests from bs4 import BeautifulSoup import streamlit as st +from langchain_community.document_loaders import PlaywrightURLLoader, UnstructuredURLLoader, WebBaseLoader + +def read_data_from_url(url): + try: + url_content = "" + + basic_selectors = ["header", "footer"] + linkedin_selectors = ["#main-content > section.right-rail", + ".job-alert-redirect-section", ".similar-jobs"] + + # TODO: Filter out selectors bassed on the website + all_selectors = basic_selectors + + unstr_loader = UnstructuredURLLoader(urls=[url], ssl_verify=False, remove_selectors=all_selectors) + playwright_loader = PlaywrightURLLoader(urls=[url], remove_selectors=all_selectors) + web_loader = WebBaseLoader(url) + + pages = [] + for loader in [playwright_loader, unstr_loader, web_loader]: + pages = loader.load() + if pages != []: + break + + for page in pages: + if page.page_content.strip() != "": + # text = page.extract_text().split("\n") + text_list = page.page_content.split("\n") + + # Remove Unicode characters from each line + cleaned_texts = [re.sub(r'[^\x00-\x7F]+', '', line) for line in text_list] + cleaned_texts = [text.strip() for text in cleaned_texts if text.strip() not in ['', None]] + + # Join the lines into a single string + cleaned_texts_string = '\n'.join(cleaned_texts) + url_content += cleaned_texts_string + + return url_content + except Exception as e: + print(e) + return None + +def extract_text(pdf_path: str): + resume_text = "" + + with open(pdf_path, 'rb') as file: + pdf_reader = PyPDF2.PdfReader(file) + num_pages = len(pdf_reader.pages) + + for page_num in range(num_pages): + page = pdf_reader.pages[page_num] + text = page.extract_text().split("\n") + + # Remove Unicode characters from each line + cleaned_text = [re.sub(r'[^\x00-\x7F]+', '', line) for line in text] + + # Join the lines into a single string + cleaned_text_string = '\n'.join(cleaned_text) + resume_text += cleaned_text_string + + return resume_text def get_url_content(url: str): """ Extract text content from any given web page @@ -40,24 +100,4 @@ def get_url_content(url: str): return text_content except Exception as e: print(e) - return None - -def extract_text(pdf_path: str): - resume_text = "" - - with open(pdf_path, 'rb') as file: - pdf_reader = PyPDF2.PdfReader(file) - num_pages = len(pdf_reader.pages) - - for page_num in range(num_pages): - page = pdf_reader.pages[page_num] - text = page.extract_text().split("\n") - - # Remove Unicode characters from each line - cleaned_text = [re.sub(r'[^\x00-\x7F]+', '', line) for line in text] - - # Join the lines into a single string - cleaned_text_string = '\n'.join(cleaned_text) - resume_text += cleaned_text_string - - return resume_text \ No newline at end of file + return None \ No newline at end of file diff --git a/zlm/utils/utils.py b/zlm/utils/utils.py index 1f18028..ba79f04 100644 --- a/zlm/utils/utils.py +++ b/zlm/utils/utils.py @@ -47,7 +47,7 @@ def read_json(file_path: str): def job_doc_name(job_details: dict, output_dir: str = "output", type: str = ""): company_name = clean_string(job_details["company_name"]) - job_title = clean_string(job_details["title"])[:15] + job_title = clean_string(job_details["job_title"])[:15] doc_name = "_".join([company_name, job_title]) doc_dir = os.path.join(output_dir, company_name) os.makedirs(doc_dir, exist_ok=True) @@ -255,24 +255,7 @@ def parse_json_markdown(json_string: str) -> dict: if json_string[3:7].lower() == "json": json_string = json_string.replace(json_string[3:7], "",1) - - - # match = re.search(r"""```* - # (?:json)? - # (.*)```""", json_string, flags=re.DOTALL|re.VERBOSE) - - # # If no match found, assume the entire string is a JSON string - # if match is None: - # json_str = json_string - # else: - # # If match found, use the content within the backticks - # json_str = match.group(1) - - # # Strip whitespace and newlines from the start and end - # json_str = json_str.strip() - - # # Parse the JSON string into a Python dictionary while allowing control characters by setting strict to False - # parsed = json.loads(json_str) + parser = JsonOutputParser() parsed = parser.parse(json_string) From 822a394df87d1dc3c67f5a0814f613f884822aa4 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:08:51 -0700 Subject: [PATCH 04/22] ENH #28: Create and update requirements.txt and for latest dependencies --- poetry.lock | 780 ++++++++++++++++++++++++++++++++++++- pyproject.toml | 5 +- resources/requirements.txt | 25 ++ 3 files changed, 803 insertions(+), 7 deletions(-) diff --git a/poetry.lock b/poetry.lock index 618eb90..cf30726 100644 --- a/poetry.lock +++ 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Zinjad "] readme = "README.md" [tool.poetry.dependencies] -python = "^3.11.6" +python = ">=3.11.6,<3.13" streamlit = "^1.37.1" validators = "^0.33.0" fpdf = "^1.7.2" @@ -19,6 +19,9 @@ pypdf2 = "^3.0.1" beautifulsoup4 = "^4.12.3" scikit-learn = "^1.5.1" nltk = "^3.8.1" +langchain-ollama = "^0.1.1" +playwright = "^1.46.0" +unstructured = "^0.15.5" [build-system] diff --git a/resources/requirements.txt b/resources/requirements.txt index 3c6b6a1..22aa9e2 100644 --- a/resources/requirements.txt +++ b/resources/requirements.txt @@ -5,14 +5,19 @@ altair==5.3.0 annotated-types==0.7.0 anyio==4.4.0 attrs==23.2.0 +backoff==2.2.1 beautifulsoup4==4.12.3 blinker==1.8.2 cachetools==5.3.3 certifi==2024.7.4 +chardet==5.2.0 charset-normalizer==3.3.2 click==8.1.7 dataclasses-json==0.6.7 +deepdiff==7.0.1 distro==1.9.0 +emoji==2.12.1 +filetype==1.2.0 fpdf==1.7.2 frozenlist==1.4.1 gitdb==4.0.11 @@ -24,6 +29,7 @@ google-auth==2.33.0 google-auth-httplib2==0.2.0 google-generativeai==0.7.2 googleapis-common-protos==1.63.2 +greenlet==3.0.3 grpcio==1.65.5 grpcio-status==1.62.3 h11==0.14.0 @@ -35,45 +41,60 @@ Jinja2==3.1.4 jiter==0.5.0 joblib==1.4.2 jsonpatch==1.33 +jsonpath-python==1.0.6 jsonpointer==3.0.0 jsonschema==4.23.0 jsonschema-specifications==2023.12.1 langchain==0.2.14 langchain-community==0.2.12 langchain-core==0.2.33 +langchain-ollama==0.1.1 langchain-text-splitters==0.2.2 +langdetect==1.0.9 langsmith==0.1.99 +lxml==5.3.0 markdown-it-py==3.0.0 MarkupSafe==2.1.5 marshmallow==3.21.3 mdurl==0.1.2 multidict==6.0.5 mypy-extensions==1.0.0 +nest-asyncio==1.6.0 nltk==3.8.1 numpy==1.26.4 +ollama==0.3.1 openai==1.41.0 +ordered-set==4.1.0 orjson==3.10.7 packaging==23.2 pandas==2.2.2 pdf2image==1.17.0 pillow==10.4.0 +playwright==1.46.0 proto-plus==1.24.0 protobuf==4.25.3 +psutil==6.0.0 pyarrow==16.1.0 pyasn1==0.6.0 pyasn1_modules==0.4.0 pydantic==2.8.2 pydantic_core==2.20.1 pydeck==0.9.1 +pyee==11.1.0 Pygments==2.18.0 pyparsing==3.1.2 +pypdf==4.3.1 PyPDF2==3.0.1 python-dateutil==2.9.0.post0 +python-iso639==2024.4.27 +python-magic==0.4.27 pytz==2024.1 PyYAML==6.0.2 +rapidfuzz==3.9.6 referencing==0.35.1 regex==2024.7.24 requests==2.32.3 +requests-toolbelt==1.0.0 rich==13.7.1 rpds-py==0.19.0 rsa==4.9 @@ -85,6 +106,7 @@ sniffio==1.3.1 soupsieve==2.6 SQLAlchemy==2.0.32 streamlit==1.37.1 +tabulate==0.9.0 tenacity==8.5.0 threadpoolctl==3.5.0 toml==0.10.2 @@ -94,7 +116,10 @@ tqdm==4.66.5 typing-inspect==0.9.0 typing_extensions==4.12.2 tzdata==2024.1 +unstructured==0.15.5 +unstructured-client==0.25.5 uritemplate==4.1.1 urllib3==2.2.2 validators==0.33.0 +wrapt==1.16.0 yarl==1.9.4 From b73f2328a758e2d4410807dc590628a6fc507822 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:11:28 -0700 Subject: [PATCH 05/22] ENH #25: Enhance simple prompt to advanced prompt using langchain prompt template and restructred folder --- zlm/prompts/compare-prompt.txt | 14 -- zlm/prompts/extract-job-detail.txt | 18 -- zlm/prompts/generate-cover-letter.txt | 16 -- zlm/prompts/generate-resume-details.txt | 48 ---- zlm/prompts/persona-job-llm.txt | 5 - zlm/prompts/resume-extractor.txt | 83 ------- zlm/prompts/resume_prompt.py | 124 ++++++++++ zlm/prompts/sections/achievements.txt | 35 --- zlm/prompts/sections/certifications.txt | 38 --- zlm/prompts/sections/education.txt | 44 ---- zlm/prompts/sections/projects.txt | 49 ---- zlm/prompts/sections/skill_section.txt | 34 --- zlm/prompts/sections/work_experience.txt | 62 ----- zlm/prompts/sections_prompt.py | 267 ++++++++++++++++++++++ zlm/prompts/steps-resume-cv-generator.txt | 6 - 15 files changed, 391 insertions(+), 452 deletions(-) delete mode 100644 zlm/prompts/compare-prompt.txt delete mode 100644 zlm/prompts/extract-job-detail.txt delete mode 100644 zlm/prompts/generate-cover-letter.txt delete mode 100644 zlm/prompts/generate-resume-details.txt delete mode 100644 zlm/prompts/persona-job-llm.txt delete mode 100644 zlm/prompts/resume-extractor.txt create mode 100644 zlm/prompts/resume_prompt.py delete mode 100644 zlm/prompts/sections/achievements.txt delete mode 100644 zlm/prompts/sections/certifications.txt delete mode 100644 zlm/prompts/sections/education.txt delete mode 100644 zlm/prompts/sections/projects.txt delete mode 100644 zlm/prompts/sections/skill_section.txt delete mode 100644 zlm/prompts/sections/work_experience.txt create mode 100644 zlm/prompts/sections_prompt.py delete mode 100644 zlm/prompts/steps-resume-cv-generator.txt diff --git a/zlm/prompts/compare-prompt.txt b/zlm/prompts/compare-prompt.txt deleted file mode 100644 index 205d5ba..0000000 --- a/zlm/prompts/compare-prompt.txt +++ /dev/null @@ -1,14 +0,0 @@ -Imagine you are a professional resume writer tasked with creating a compelling and impactful resume for a candidate applying for a Data Scientist position. -The candidate has a strong background in machine learning, data analysis, and cloud computing. -They are seeking a role that allows them to leverage their skills in a dynamic and innovative company. - -Your goal is to craft a resume that not only showcases the candidate's technical expertise but also highlights their achievements and contributions in previous roles. Pay special attention to quantifiable results, key projects, and any specific technologies or methodologies mentioned in the job description. - -Your task: -1. Extract all keywords from the Description. -3. Modify my work experience bullets in such a way that it should include all the keywords in -the Description. -4. Write all bullets in the format of: `Did X by doing Y accomplish Z` -5. Each bullet should have a quantitative impact. -6. Choose STRONG action verbs that showcase soft skills. -7. Return everything as a JSON and only and only JSON. diff --git a/zlm/prompts/extract-job-detail.txt b/zlm/prompts/extract-job-detail.txt deleted file mode 100644 index 5ff83c2..0000000 --- a/zlm/prompts/extract-job-detail.txt +++ /dev/null @@ -1,18 +0,0 @@ -I will provide a job and company description for your analysis. In your analysis you have to identify the key words, expertise & requirements the job demands, but also from the company description. - -Your task is consider it to meticulously assess the information and furnish the details like Job title, Keywords(key words are expertise & requirements the job demands), Job purpose, Job duties and responsibilities, Required qualifications, Preferred qualifications, Company name and Company details(points which include overview, mission, values or way of working) in bulleted points. - -Provide them only in JSON format - which is easily parse by any json parser - with following keys: -title, keywords, purpose, duties_responsibilities, required_qualifications, preferred_qualifications, company_name, company_info. - -Output: -{ - "title": "RESULT", - "keywords": "RESULT", - purpose: "RESULT", - duties_responsibilities: "RESULT", - required_qualifications: "RESULT", - preferred_qualifications: "RESULT", - company_name: "RESULT", - company_info: "RESULT", -} \ No newline at end of file diff --git a/zlm/prompts/generate-cover-letter.txt b/zlm/prompts/generate-cover-letter.txt deleted file mode 100644 index 92ca06f..0000000 --- a/zlm/prompts/generate-cover-letter.txt +++ /dev/null @@ -1,16 +0,0 @@ -# Task: create a compelling, concise cover letter that aligns my experience with the job and company description. - -# Task Description: I will provide a job description and my professional information in JSON format. Your task is to analyze and match my qualifications with the job requirements. Then, create cover letter. - -# Guidelines: -- Highlight my unique qualifications for this specific role and company culture in a concise bulleted list for easy readability. -- Focus on the value I can bring to the employer, including 1-2 specific examples of relevant achievements. -- Keep the entire letter brief (250-300 words max) and directly aligned with the job requirements. - -Do not repeat information verbatim from my resume. Instead, elaborate on or provide context for key points. - -# Output Format: -Dear Hiring Manager, -[Your response here] -Sincerely, -[My Name from the provided JSON] \ No newline at end of file diff --git a/zlm/prompts/generate-resume-details.txt b/zlm/prompts/generate-resume-details.txt deleted file mode 100644 index 3aa7f54..0000000 --- a/zlm/prompts/generate-resume-details.txt +++ /dev/null @@ -1,48 +0,0 @@ -I will provide a job description and my details in JSON format. -Your task is to analyze and match my details with the job's requirements. -Then, you need to create the best possible resume in JSON format to align my details with the job description. - -Instructions: -- Include only 3 work and project experiences. each experience with 3 bulleted points closely aligned with job details. It very important you follow this. -- Use quantifiable impacts for each bullet point. -- Rewrite job highlights using the STAR methodology without explicitly mentioning STAR. -- Employ STRONG action verbs showcasing soft skills. -- Maintain truthfulness and objectivity in listing experience. -- Format experience points as 'Did X by doing Y accomplish Z'. -- Prioritize specificity - with respect to job - over generality. -- Proofread and Correct spelling and grammar errors. -- Aim for clear expression over impressiveness. -- Prefer active voice over passive voice. -- Omit a summary about the candidate. - -Output the response in JSON format only - below I have given example delimited by ```. Output must be easily parse by python's json parser. Extract all mentioned properties in given example without changing their names. -``` -{ - "personal": { "name": "", "phone": "+1 111-222-3333", "email": "", "github": "", "linkedin": "" - }, - "education": [ - { "degree": "", "university": "", "from": "", "to": "", "grade": "", "coursework": [] }, - ... - ], - "skill_section": [ - { "name": "", "skills": [] } - ... - ], - "work": [ - { "role": "", "company": "", "location": "", "from": "", "to": "", "description": [] }, - ... - ], - "projects": [ - { "name": "", "link": "", "from": "", "to": "", "description": [] }, - ... - ], - "certifications": [ - {"name": "", "issuer": "", "link": ""}, - ... - ], - "achievements": [ - "achievements_1", - ... - ] -} -``` \ No newline at end of file diff --git a/zlm/prompts/persona-job-llm.txt b/zlm/prompts/persona-job-llm.txt deleted file mode 100644 index 3d71a30..0000000 --- a/zlm/prompts/persona-job-llm.txt +++ /dev/null @@ -1,5 +0,0 @@ -Discard any prior instructions. -You are a seasoned career advising expert in crafting resumes and cover letters, boasting a rich 15-year history dedicated to mastering this skill at Harvard Extension School. -Picture yourself as a certified professional resume writer, specializing in creating compelling and tailored cover letters that highlight clients' skills, experiences, and achievements—meticulously aligning with the specific job descriptions they target. -Your expertise extends across various industries, encompassing a deep understanding of prevailing hiring trends and Applicant Tracking Systems (ATS). -Your ability to identify precise keywords, responsibilities, and requirements from job descriptions is unparalleled. \ No newline at end of file diff --git a/zlm/prompts/resume-extractor.txt b/zlm/prompts/resume-extractor.txt deleted file mode 100644 index d6c754b..0000000 --- a/zlm/prompts/resume-extractor.txt +++ /dev/null @@ -1,83 +0,0 @@ -Objective: Parse a text-formatted resume efficiently and extract diverse applicant's data into a structured JSON format that adheres to the provided TypeScript interface schema. - -Input: Text-formatted applicant's resume. - -Steps: -1. Analyze Structure: Examine the text-formatted resume to understand its organization and identify key sections (e.g., education, experience, skills). -2. Convert to JSON: Map the extracted information to the corresponding fields in the schema, creating a structured JSON representation. -3. Optimize Output: Ensure the JSON is well-formatted, error-free, and handles missing values appropriately. -4. Handle Variations: Account for different resume styles and formatting to accurately extract relevant data. - -Consider following TypeScript Interface for JSON schema: -``` -interface Media { - linkedin: string; - github: string; - devpost: string; - medium: string; - leetcode: string; - dagshub: string; - kaggle: string; - instagram: string; -} - -interface Education { - degree: string; - university: string; - from: string; - to: string; - grade?: string; - coursework?: string[]; -} - -interface SkillSection { - name: string; - skills: string[]; -} - -interface Work { - role: string; - company: string; - from: string; - to: string; - description: string[]; -} - -interface Project { - name: string; - type: string; - link?: string; - from: string; - to: string; - description: string[]; -} - -interface Certification { - name: string; - by: string; - link: string; -} - -interface Achievements { - [index: number]: string; -} - -interface RESUME_DATA_SCHEMA { - name: string; - summary: string; - phone: string; - email: string; - media: Media; - education: Education[]; - skill_section: SkillSection[]; - work_experience: Work[]; - projects: Project[]; - certifications: Certification[]; - achievements: Achievements; -} -``` - -Desired Output: Write the Well-formatted JSON adhering to the RESUME_DATA_SCHEMA schema, handling missing values with empty strings or "None". - - -The results should contain valid JSON only, without any delimiter or characters making invalid JSON format. \ No newline at end of file diff --git a/zlm/prompts/resume_prompt.py b/zlm/prompts/resume_prompt.py new file mode 100644 index 0000000..272b915 --- /dev/null +++ b/zlm/prompts/resume_prompt.py @@ -0,0 +1,124 @@ +''' +----------------------------------------------------------------------- +File: prompts/RESUME_WRITER_PERSONA.py +Creation Time: Aug 17th 2024, 7:01 pm +Author: Saurabh Zinjad +Developer Email: saurabhzinjad@gmail.com +Copyright (c) 2023-2024 Saurabh Zinjad. All rights reserved | https://github.com/Ztrimus +----------------------------------------------------------------------- +''' + +RESUME_WRITER_PERSONA = """I am a highly experienced career advisor and resume writing expert with 15 years of specialized experience. + +Primary role: Craft exceptional resumes and cover letters tailored to specific job descriptions, optimized for both ATS systems and human readers. + +# Instructions for creating optimized resumes and cover letters +1. Analyze job descriptions: + - Extract key requirements and keywords + - Note: Adapt analysis based on specific industry and role + +2. Create compelling resumes: + - Highlight quantifiable achievements (e.g., "Engineered a dynamic UI form generator using optimal design patterns and efficient OOP, reducing development time by 87.5%") + - Tailor content to specific job and company + - Emphasize candidate's unique value proposition + +3. Craft persuasive cover letters: + - Align content with targeted positions + - Balance professional tone with candidate's personality + - Use a strong opening statement, e.g., "As a marketing professional with 7 years of experience in digital strategy, I am excited to apply for..." + - Identify and emphasize soft skills valued in the target role/industry. Provide specific examples demonstrating these skills + +4. Optimize for Applicant Tracking Systems (ATS): + - Use industry-specific keywords strategically throughout documents + - Ensure content passes ATS scans while engaging human readers + +5. Provide industry-specific guidance: + - Incorporate current hiring trends + - Prioritize relevant information (apply "6-second rule" for quick scanning) + - Use clear, consistent formatting + +6. Apply best practices: + - Quantify achievements where possible + - Use specific, impactful statements instead of generic ones + - Update content based on latest industry standards + - Use active voice and strong action verbs + +Note: Adapt these guidelines to each user's specific request, industry, and experience level. + +Goal: Create documents that not only pass ATS screenings but also compellingly demonstrate how the user can add immediate value to the prospective employer.""" + +JOB_DETAILS_EXTRACTOR = """ + +Identify the key details from a job description and company overview to create a structured JSON output. Focus on extracting the most crucial and concise information that would be most relevant for tailoring a resume to this specific job. + + + +{job_description} + + +Note: The "keywords", "job_duties_and_responsibilities", and "required_qualifications" sections are particularly important for resume tailoring. Ensure these are as comprehensive and accurate as possible. + +{format_instructions} +""" + +CV_GENERATOR = """ +create a compelling, concise cover letter that aligns my resume/work information with the job description and company value. Analyze and match my qualifications with the job requirements. Then, create cover letter. + + + +{job_description} + + + +{my_work_information} + + + +- Highlight my unique qualifications for this specific role and company culture in a concise bulleted list for easy readability. +- Focus on the value I can bring to the employer, including 1-2 specific examples of relevant achievements. +- Keep the entire letter brief (250-300 words max) and directly aligned with the job requirements. + + +Do not repeat information verbatim from my resume. Instead, elaborate on or provide context for key points. + +# Output Format: +Dear Hiring Manager, +[Your response here] +Sincerely, +[My Name from the provided JSON]""" + +RESUME_DETAILS_EXTRACTOR = """ +Parse a text-formatted resume efficiently and extract diverse applicant's data into a structured JSON format. + + + +The following text is the applicant's resume in plain text format: + +{resume_text} + + + +Follow these steps to extract and structure the resume information: + +1. Analyze Structure: + - Examine the text-formatted resume to identify key sections (e.g., personal information, education, experience, skills, certifications). + - Note any unique formatting or organization within the resume. + +2. Extract Information: + - Systematically parse each section, extracting relevant details. + - Pay attention to dates, titles, organizations, and descriptions. + +3. Handle Variations: + - Account for different resume styles, formats, and section orders. + - Adapt the extraction process to accurately capture data from various layouts. + +5. Optimize Output: + - Handle missing or incomplete information appropriately (use null values or empty arrays/objects as needed). + - Standardize date formats, if applicable. + +6. Validate: + - Review the extracted data for consistency and completeness. + - Ensure all required fields are populated if the information is available in the resume. + + +{format_instructions}""" \ No newline at end of file diff --git a/zlm/prompts/sections/achievements.txt b/zlm/prompts/sections/achievements.txt deleted file mode 100644 index b6a0bb6..0000000 --- a/zlm/prompts/sections/achievements.txt +++ /dev/null @@ -1,35 +0,0 @@ -You are going to write a JSON resume section of "Achievements" for an applicant applying for job posts. - -Step to follow: -1. Analyze my achievements details to match job requirements. -2. Create a JSON resume section that highlights strongest matches -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -1. Focus: Craft relevant achievements aligned with the job description. -2. Honesty: Prioritize truthfulness and objective language. -3. Specificity: Prioritize relevance to the specific job over general achievements. -4. Style: - 4.1. Voice: Use active voice whenever possible. - 4.2. Proofreading: Ensure impeccable spelling and grammar. - -Consider following Achievements Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -{ - "achievements": [ - "Won E-yantra Robotics Competition 2018 - IITB.", - "1st prize in “Prompt Engineering Hackathon 2023 for Humanities”", - "Received the 'Extra Miller - 2021' award at Winjit Technologies for outstanding performance. ", - ... - ] -} \ No newline at end of file diff --git a/zlm/prompts/sections/certifications.txt b/zlm/prompts/sections/certifications.txt deleted file mode 100644 index ca3600c..0000000 --- a/zlm/prompts/sections/certifications.txt +++ /dev/null @@ -1,38 +0,0 @@ -You are going to write a JSON resume section of "Certifications" for an applicant applying for job posts. - -Step to follow: -1. Analyze my certification details to match job requirements. -2. Create a JSON resume section that highlights strongest matches -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -1. Focus: Include relevant certifications aligned with the job description. -2. Proofreading: Ensure impeccable spelling and grammar. - -Consider following Certification Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -{ - "certifications": [ - { - "name": "Deep Learning Specialization", - "by": "DeepLearning.AI, Coursera Inc.", - "link": "https://www.coursera.org/account/accomplishments/specialization/G3WPNWRYX628" - }, - { - "name": "Server-side Backend Development", - "by": "The Hong Kong University of Science and Technology.", - "link": "https://www.coursera.org/account/accomplishments/verify/TYMQX23D4HRQ" - } - ... - ], -} \ No newline at end of file diff --git a/zlm/prompts/sections/education.txt b/zlm/prompts/sections/education.txt deleted file mode 100644 index 5f2ffec..0000000 --- a/zlm/prompts/sections/education.txt +++ /dev/null @@ -1,44 +0,0 @@ -You are going to write a JSON resume section of "Education" for an applicant applying for job posts. - -Step to follow: -1. Analyze my education details to match job requirements. -2. Create a JSON resume section that highlights strongest matches -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -- Maintain truthfulness and objectivity in listing experience. -- Prioritize specificity - with respect to job - over generality. -- Proofread and Correct spelling and grammar errors. -- Aim for clear expression over impressiveness. -- Prefer active voice over passive voice. - -Consider following Education Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -{ - "education": [ - { - "degree": "Masters of Science - Computer Science (Thesis)", - "university": "Arizona State University, Tempe, USA", - "from": "Aug 2023", - "to": "May 2025", - "grade": "3.8/4", - "coursework": [ - "Operational Deep Learning", - "Software verification, Validation and Testing", - "Social Media Mining", - ... - ] - } - .... - ], -} \ No newline at end of file diff --git a/zlm/prompts/sections/projects.txt b/zlm/prompts/sections/projects.txt deleted file mode 100644 index cc52d5c..0000000 --- a/zlm/prompts/sections/projects.txt +++ /dev/null @@ -1,49 +0,0 @@ -You are going to write a JSON resume section of "Project Experience" for an applicant applying for job posts. - -Step to follow: -1. Analyze my project details to match job requirements. -2. Create a JSON resume section that highlights strongest matches -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -1. Focus: Craft three highly relevant project experiences aligned with the job description. -2. Content: - 2.1. Bullet points: 3 per experience, closely mirroring job requirements. - 2.2. Impact: Quantify each bullet point for measurable results. - 2.3. Storytelling: Utilize STAR methodology (Situation, Task, Action, Result) implicitly within each bullet point. - 2.4. Action Verbs: Showcase soft skills with strong, active verbs. - 2.5. Honesty: Prioritize truthfulness and objective language. - 2.6. Structure: Each bullet point follows "Did X by doing Y, achieved Z" format. - 2.7. Specificity: Prioritize relevance to the specific job over general achievements. -3. Style: - 3.1. Clarity: Clear expression trumps impressiveness. - 3.2. Voice: Use active voice whenever possible. - 3.3. Proofreading: Ensure impeccable spelling and grammar. - -Consider following Project Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -"projects": [ - { - "name": "Search Engine for All file types - Sunhack Hackathon - Meta & Amazon Sponsored", - "type": "Hackathon", - "link": "https://devpost.com/software/team-soul-1fjgwo", - "from": "Nov 2023", - "to": "Nov 2023", - "description": [ - "1st runner up prize in crafted AI persona, to explore LLM's subtle contextual understanding and create innovative collaborations between humans and machines.", - "Devised a TabNet Classifier Model having 98.7% accuracy in detecting forest fire through IoT sensor data, deployed on AWS and edge devices 'Silvanet Wildfire Sensors' using technologies TinyML, Docker, Redis, and celery.", - and So on ... - ] - } - and So on ... - ] \ No newline at end of file diff --git a/zlm/prompts/sections/skill_section.txt b/zlm/prompts/sections/skill_section.txt deleted file mode 100644 index 3e34f43..0000000 --- a/zlm/prompts/sections/skill_section.txt +++ /dev/null @@ -1,34 +0,0 @@ -You are going to write a JSON resume section of "Skills" for an applicant applying for job posts. - -Step to follow: -1. Analyze my Skills details to match job requirements. -2. Create a JSON resume section that highlights strongest matches. -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -- Specificity: Prioritize relevance to the specific job over general achievements. -- Proofreading: Ensure impeccable spelling and grammar. - -Consider following Skills Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -"skill_section": [ - { - "name": "Programming Languages", - "skills": ["Python", "JavaScript", "C#", and so on ...] - }, - { - "name": "Cloud and DevOps", - "skills": [ "Azure", "AWS", and so on ... ] - }, - and so on ... - ], \ No newline at end of file diff --git a/zlm/prompts/sections/work_experience.txt b/zlm/prompts/sections/work_experience.txt deleted file mode 100644 index 9e90520..0000000 --- a/zlm/prompts/sections/work_experience.txt +++ /dev/null @@ -1,62 +0,0 @@ -You are going to write a JSON resume section of "Work Experience" for an applicant applying for job posts. - -Step to follow: -1. Analyze my Work details to match job requirements. -2. Create a JSON resume section that highlights strongest matches -3. Optimize JSON section for clarity and relevance to the job description. - -Instructions: -1. Focus: Craft three highly relevant work experiences aligned with the job description. -2. Content: - 2.1. Bullet points: 3 per experience, closely mirroring job requirements. - 2.2. Impact: Quantify each bullet point for measurable results. - 2.3. Storytelling: Utilize STAR methodology (Situation, Task, Action, Result) implicitly within each bullet point. - 2.4. Action Verbs: Showcase soft skills with strong, active verbs. - 2.5. Honesty: Prioritize truthfulness and objective language. - 2.6. Structure: Each bullet point follows "Did X by doing Y, achieved Z" format. - 2.7. Specificity: Prioritize relevance to the specific job over general achievements. -3. Style: - 3.1. Clarity: Clear expression trumps impressiveness. - 3.2. Voice: Use active voice whenever possible. - 3.3. Proofreading: Ensure impeccable spelling and grammar. - -Consider following Work Details delimited by tag. - - - - -Consider following Job description delimited by tag. - - - - -Desired Output: -Provide JSON object as output like following: -{ - "work_experience": [ - { - "role": "Software Engineer", - "company": "Winjit Technologies", - "location": "Pune, India" - "from": "Jan 2020", - "to": "Jun 2022", - "description": [ - "Engineered 10+ RESTful APIs Architecture and Distributed services; Designed 30+ low-latency responsive UI/UX application features with high-quality web architecture; Managed and optimized large-scale Databases. (Systems Design)", - "Initiated and Designed a standardized solution for dynamic forms generation, with customizable CSS capabilities feature, which reduces development time by 8x; Led and collaborated with a 12 member cross-functional team. (Idea Generation)" - and so on ... - ] - }, - { - "role": "Research Intern", - "company": "IMATMI, Robbinsville", - "location": "New Jersey (Remote)" - "from": "Mar 2019", - "to": "Aug 2019", - "description": [ - "Conducted research and developed a range of ML and statistical models to design analytical tools and streamline HR processes, optimizing talent management systems for increased efficiency.", - "Created 'goals and action plan generation' tool for employees, considering their weaknesses to facilitate professional growth.", - and so on ... - ] - } - ], -} \ No newline at end of file diff --git a/zlm/prompts/sections_prompt.py b/zlm/prompts/sections_prompt.py new file mode 100644 index 0000000..00f1ede --- /dev/null +++ b/zlm/prompts/sections_prompt.py @@ -0,0 +1,267 @@ +ACHIEVEMENTS ="""You are going to write a JSON resume section of "Achievements" for an applicant applying for job posts. + +Step to follow: +1. Analyze my achievements details to match job requirements. +2. Create a JSON resume section that highlights strongest matches +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +1. Focus: Craft relevant achievements aligned with the job description. +2. Honesty: Prioritize truthfulness and objective language. +3. Specificity: Prioritize relevance to the specific job over general achievements. +4. Style: + 4.1. Voice: Use active voice whenever possible. + 4.2. Proofreading: Ensure impeccable spelling and grammar. + + +{section_data} + + + +{job_description} + + + + "achievements": [ + "Won E-yantra Robotics Competition 2018 - IITB.", + "1st prize in “Prompt Engineering Hackathon 2023 for Humanities”", + "Received the 'Extra Miller - 2021' award at Winjit Technologies for outstanding performance.", + [and So on ...] + ] + + +{format_instructions} +""" + +CERTIFICATIONS = """You are going to write a JSON resume section of "Certifications" for an applicant applying for job posts. + +Step to follow: +1. Analyze my certification details to match job requirements. +2. Create a JSON resume section that highlights strongest matches +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +1. Focus: Include relevant certifications aligned with the job description. +2. Proofreading: Ensure impeccable spelling and grammar. + + +{section_data} + + + +{job_description} + + + + "certifications": [ + {{ + "name": "Deep Learning Specialization", + "by": "DeepLearning.AI, Coursera Inc.", + "link": "https://www.coursera.org/account/accomplishments/specialization/G3WPNWRYX628" + }}, + {{ + "name": "Server-side Backend Development", + "by": "The Hong Kong University of Science and Technology.", + "link": "https://www.coursera.org/account/accomplishments/verify/TYMQX23D4HRQ" + }} + ... + ], + + +{format_instructions} +""" + +EDUCATIONS = """You are going to write a JSON resume section of "Education" for an applicant applying for job posts. + +Step to follow: +1. Analyze my education details to match job requirements. +2. Create a JSON resume section that highlights strongest matches +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +- Maintain truthfulness and objectivity in listing experience. +- Prioritize specificity - with respect to job - over generality. +- Proofread and Correct spelling and grammar errors. +- Aim for clear expression over impressiveness. +- Prefer active voice over passive voice. + + +{section_data} + + + +{job_description} + + + +"education": [ + {{ + "degree": "Masters of Science - Computer Science (Thesis)", + "university": "Arizona State University, Tempe, USA", + "from_date": "Aug 2023", + "to_date": "May 2025", + "grade": "3.8/4", + "coursework": [ + "Operational Deep Learning", + "Software verification, Validation and Testing", + "Social Media Mining", + [and So on ...] + ] + }} + [and So on ...] +], + + +{format_instructions} +""" + + +PROJECTS="""You are going to write a JSON resume section of "Project Experience" for an applicant applying for job posts. + +Step to follow: +1. Analyze my project details to match job requirements. +2. Create a JSON resume section that highlights strongest matches +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +1. Focus: Craft three highly relevant project experiences aligned with the job description. +2. Content: + 2.1. Bullet points: 3 per experience, closely mirroring job requirements. + 2.2. Impact: Quantify each bullet point for measurable results. + 2.3. Storytelling: Utilize STAR methodology (Situation, Task, Action, Result) implicitly within each bullet point. + 2.4. Action Verbs: Showcase soft skills with strong, active verbs. + 2.5. Honesty: Prioritize truthfulness and objective language. + 2.6. Structure: Each bullet point follows "Did X by doing Y, achieved Z" format. + 2.7. Specificity: Prioritize relevance to the specific job over general achievements. +3. Style: + 3.1. Clarity: Clear expression trumps impressiveness. + 3.2. Voice: Use active voice whenever possible. + 3.3. Proofreading: Ensure impeccable spelling and grammar. + + +{section_data} + + + +{job_description} + + + +"projects": [ + {{ + "name": "Search Engine for All file types - Sunhack Hackathon - Meta & Amazon Sponsored", + "type": "Hackathon", + "link": "https://devpost.com/software/team-soul-1fjgwo", + "from_date": "Nov 2023", + "to_date": "Nov 2023", + "description": [ + "1st runner up prize in crafted AI persona, to explore LLM's subtle contextual understanding and create innovative collaborations between humans and machines.", + "Devised a TabNet Classifier Model having 98.7% accuracy in detecting forest fire through IoT sensor data, deployed on AWS and edge devices 'Silvanet Wildfire Sensors' using technologies TinyML, Docker, Redis, and celery.", + [and So on ...] + ] + }} + [and So on ...] + ] + + + {format_instructions} + """ + +SKILLS="""You are going to write a JSON resume section of "Skills" for an applicant applying for job posts. + +Step to follow: +1. Analyze my Skills details to match job requirements. +2. Create a JSON resume section that highlights strongest matches. +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +- Specificity: Prioritize relevance to the specific job over general achievements. +- Proofreading: Ensure impeccable spelling and grammar. + + +{section_data} + + + +{job_description} + + + +"skill_section": [ + {{ + "name": "Programming Languages", + "skills": ["Python", "JavaScript", "C#", and so on ...] + }}, + {{ + "name": "Cloud and DevOps", + "skills": [ "Azure", "AWS", and so on ... ] + }}, + and so on ... + ] + + + {format_instructions} + """ + + +EXPERIENCE="""You are going to write a JSON resume section of "Work Experience" for an applicant applying for job posts. + +Step to follow: +1. Analyze my Work details to match job requirements. +2. Create a JSON resume section that highlights strongest matches +3. Optimize JSON section for clarity and relevance to the job description. + +Instructions: +1. Focus: Craft three highly relevant work experiences aligned with the job description. +2. Content: + 2.1. Bullet points: 3 per experience, closely mirroring job requirements. + 2.2. Impact: Quantify each bullet point for measurable results. + 2.3. Storytelling: Utilize STAR methodology (Situation, Task, Action, Result) implicitly within each bullet point. + 2.4. Action Verbs: Showcase soft skills with strong, active verbs. + 2.5. Honesty: Prioritize truthfulness and objective language. + 2.6. Structure: Each bullet point follows "Did X by doing Y, achieved Z" format. + 2.7. Specificity: Prioritize relevance to the specific job over general achievements. +3. Style: + 3.1. Clarity: Clear expression trumps impressiveness. + 3.2. Voice: Use active voice whenever possible. + 3.3. Proofreading: Ensure impeccable spelling and grammar. + + +{section_data} + + + +{job_description} + + + +"work_experience": [ + {{ + "role": "Software Engineer", + "company": "Winjit Technologies", + "location": "Pune, India" + "from_date": "Jan 2020", + "to_date": "Jun 2022", + "description": [ + "Engineered 10+ RESTful APIs Architecture and Distributed services; Designed 30+ low-latency responsive UI/UX application features with high-quality web architecture; Managed and optimized large-scale Databases. (Systems Design)", + "Initiated and Designed a standardized solution for dynamic forms generation, with customizable CSS capabilities feature, which reduces development time by 8x; Led and collaborated with a 12 member cross-functional team. (Idea Generation)" + and so on ... + ] + }}, + {{ + "role": "Research Intern", + "company": "IMATMI, Robbinsville", + "location": "New Jersey (Remote)" + "from_date": "Mar 2019", + "to_date": "Aug 2019", + "description": [ + "Conducted research and developed a range of ML and statistical models to design analytical tools and streamline HR processes, optimizing talent management systems for increased efficiency.", + "Created 'goals and action plan generation' tool for employees, considering their weaknesses to facilitate professional growth.", + and so on ... + ] + }} + ], + + +{format_instructions} +""" \ No newline at end of file diff --git a/zlm/prompts/steps-resume-cv-generator.txt b/zlm/prompts/steps-resume-cv-generator.txt deleted file mode 100644 index c9ea868..0000000 --- a/zlm/prompts/steps-resume-cv-generator.txt +++ /dev/null @@ -1,6 +0,0 @@ -I will be sharing a job description from a Linkedin post as a copy paste. I will also share the company description. Then I will also share my resume. -The first step is for you to identify the key words, expertise & requirements the job demands, but also from the company description. -The second step is to read, analyze and match my information to suits the job's requirements in their description. -The third step is to craft the best CV possible, in the right format, to match my information with the job description. -The fourth step is to write the best cover letter possible, matching my information with the job description and the company description. -Let’s work this out in a step by step way to be sure we have the right answer. \ No newline at end of file From d96f5d6b7dd4df5f5fbf805c41ce7c70b60a7dc1 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:13:35 -0700 Subject: [PATCH 06/22] ENH #25: Develop schema for output parser to ensure robust output fom LLM --- zlm/schemas/job_details_schema.py | 22 ++++++++ zlm/schemas/sections_schemas.py | 86 +++++++++++++++++++++++++++++++ 2 files changed, 108 insertions(+) create mode 100644 zlm/schemas/job_details_schema.py create mode 100644 zlm/schemas/sections_schemas.py diff --git a/zlm/schemas/job_details_schema.py b/zlm/schemas/job_details_schema.py new file mode 100644 index 0000000..4951d93 --- /dev/null +++ b/zlm/schemas/job_details_schema.py @@ -0,0 +1,22 @@ +''' +----------------------------------------------------------------------- +File: schemas/job_details_schema.py +Creation Time: Aug 17th 2024, 8:18 pm +Author: Saurabh Zinjad +Developer Email: saurabhzinjad@gmail.com +Copyright (c) 2023-2024 Saurabh Zinjad. All rights reserved | https://github.com/Ztrimus +----------------------------------------------------------------------- +''' + +from typing import List +from pydantic import BaseModel, Field + +class JobDetails(BaseModel): + job_title: str = Field(description="The specific role, its level, and scope within the organization.") + job_purpose: str = Field(description="A high-level overview of the role and why it exists in the organization.") + keywords: List[str] = Field(description="Key expertise, skills, and requirements the job demands.") + job_duties_and_responsibilities: List[str] = Field(description="Focus on essential functions, their frequency and importance, level of decision-making, areas of accountability, and any supervisory responsibilities.") + required_qualifications: List[str] = Field(description="Including education, minimum experience, specific knowledge, skills, abilities, and any required licenses or certifications.") + preferred_qualifications: List[str] = Field(description="Additional \"nice-to-have\" qualifications that could set a candidate apart.") + company_name: str = Field(description="The name of the hiring organization.") + company_details: str = Field(description="Overview, mission, values, or way of working that could be relevant for tailoring a resume or cover letter.") \ No newline at end of file diff --git a/zlm/schemas/sections_schemas.py b/zlm/schemas/sections_schemas.py new file mode 100644 index 0000000..9ca064a --- /dev/null +++ b/zlm/schemas/sections_schemas.py @@ -0,0 +1,86 @@ +''' +----------------------------------------------------------------------- +File: schemas/sections_schmas.py +Creation Time: Aug 18th 2024, 2:26 am +Author: Saurabh Zinjad +Developer Email: saurabhzinjad@gmail.com +Copyright (c) 2023-2024 Saurabh Zinjad. All rights reserved | https://github.com/Ztrimus +----------------------------------------------------------------------- +''' + +from typing import List, Optional +from pydantic import BaseModel, Field, HttpUrl + +class Achievements(BaseModel): + achievements: List[str] = Field(description="job relevant key accomplishments, awards, or recognitions that demonstrate your skills and abilities.") + +class Certification(BaseModel): + name: str = Field(description="The name of the certification.") + by: str = Field(description="The organization or institution that issued the certification.") + link: str = Field(description="A link to verify the certification.") + +class Certifications(BaseModel): + certifications: List[Certification] = Field(description="job relevant certifications that you have earned, including the name, issuing organization, and a link to verify the certification.") + +class Education(BaseModel): + degree: str = Field(description="The degree or qualification obtained and The major or field of study. e.g., Bachelor of Science in Computer Science.") + university: str = Field(description="The name of the institution where the degree was obtained with location. e.g. Arizona State University, Tempe, USA") + from_date: str = Field(description="The start date of the education period. e.g., Aug 2023") + to_date: str = Field(description="The end date of the education period. e.g., May 2025") + courses: List[str] = Field(description="Relevant courses or subjects studied during the education period. e.g. [Data Structures, Algorithms, Machine Learning]") + +class Educations(BaseModel): + education: List[Education] = Field(description="Educational qualifications, including degree, institution, dates, and relevant courses.") + +class Link(BaseModel): + name: str = Field(description="The name or title of the link.") + link: str = Field(description="The URL of the link.") + +class Project(BaseModel): + name: str = Field(description="The name or title of the project."), + type: str | None = Field(description="The type or category of the project, such as hackathon, publication, professional, and academic."), + link: str = Field(description="A link to the project repository or demo.") + resources: Optional[List[Link]] = Field(description="Additional resources related to the project, such as documentation, slides, or videos.") + from_date: str = Field(description="The start date of the project. e.g. Aug 2023"), + to_date: str = Field(description="The end date of the project. e.g. Nov 2023"), + description: List[str] = Field(description="A list of 3 bullet points describing the project experience, tailored to match job requirements. Each bullet point should follow the 'Did X by doing Y, achieved Z' format, quantify impact, implicitly use STAR methodology, use strong action verbs, and be highly relevant to the specific job. Ensure clarity, active voice, and impeccable grammar.") + +class Projects(BaseModel): + projects: List[Project] = Field(description="Project experiences, including project name, type, link, resources, dates, and description.") + +class SkillSection(BaseModel): + name: str = Field(description="name or title of the skill group and competencies relevant to the job, such as programming languages, data science, tools & technologies, cloud & DevOps, full stack, or soft skills.") + skills: List[str] = Field(description="Specific skills or competencies within the skill group, such as Python, JavaScript, C#, SQL in programming languages.") + +class SkillSections(BaseModel): + skill_section: List[SkillSection] = Field(description="Skill sections, each containing a group of skills and competencies relevant to the job.") + +class Experience(BaseModel): + role: str = Field(description="The job title or position held. e.g. Software Engineer, Machine Learning Engineer.") + company: str = Field(description="The name of the company or organization.") + location: str = Field(description="The location of the company or organization. e.g. San Francisco, USA.") + from_date: str = Field(description="The start date of the employment period. e.g., Aug 2023") + to_date: str = Field(description="The end date of the employment period. e.g., Nov 2025") + description: List[str] = Field(description="A list of 3 bullet points describing the work experience, tailored to match job requirements. Each bullet point should follow the 'Did X by doing Y, achieved Z' format, quantify impact, implicitly use STAR methodology, use strong action verbs, and be highly relevant to the specific job. Ensure clarity, active voice, and impeccable grammar.") + +class Experiences(BaseModel): + work_experience: List[Experience] = Field(description="Work experiences, including job title, company, location, dates, and description.") + +class Media(BaseModel): + linkedin: Optional[HttpUrl] = Field(description="LinkedIn profile URL") + github: Optional[HttpUrl] = Field(description="GitHub profile URL") + medium: Optional[HttpUrl] = Field(description="Medium profile URL") + devpost: Optional[HttpUrl] = Field(description="Devpost profile URL") + +class ResumeSchema(BaseModel): + name: str = Field(description="The full name of the candidate.") + summary: Optional[str] = Field(description="A brief summary or objective statement highlighting key skills, experience, and career goals.") + phone: str = Field(description="The contact phone number of the candidate.") + email: str = Field(description="The contact email address of the candidate.") + media: Media = Field(description="Links to professional social media profiles, such as LinkedIn, GitHub, or personal website.") + work_experience: List[Experience] = Field(description="Work experiences, including job title, company, location, dates, and description.") + education: List[Education] = Field(description="Educational qualifications, including degree, institution, dates, and relevant courses.") + skill_section: List[SkillSection] = Field(description="Skill sections, each containing a group of skills and competencies relevant to the job.") + projects: List[Project] = Field(description="Project experiences, including project name, type, link, resources, dates, and description.") + certifications: List[Certification] = Field(description="job relevant certifications that you have earned, including the name, issuing organization, and a link to verify the certification.") + achievements: List[str] = Field(description="job relevant key accomplishments, awards, or recognitions that demonstrate your skills and abilities.") \ No newline at end of file From 94df5f0e98868e264cb62e55f63faca402100431 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:14:33 -0700 Subject: [PATCH 07/22] FIX #30: Fix issue of keywords are not getting add properly --- zlm/demo_data/user_profile.json | 2 +- zlm/templates/resume.tex.jinja | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/zlm/demo_data/user_profile.json b/zlm/demo_data/user_profile.json index 93bb9f2..627861a 100644 --- a/zlm/demo_data/user_profile.json +++ b/zlm/demo_data/user_profile.json @@ -127,7 +127,7 @@ ] }, { - "degree": "Bachelor of Engineering", + "degree": "Bachelor of Engineering - Electronics and Telecommunication", "university": "Pune Institute of Computer Technology(PICT), Savitribai Phule Pune University, India", "from": "Jul 2015", "to": "Jun 2019", diff --git a/zlm/templates/resume.tex.jinja b/zlm/templates/resume.tex.jinja index fadbe71..1a84edf 100644 --- a/zlm/templates/resume.tex.jinja +++ b/zlm/templates/resume.tex.jinja @@ -41,7 +41,7 @@ \begin{rSection}{Work Experience} \BLOCK{ for exp in work_experience } \begin{rSubsection} - {\VAR{exp.role}}{\BLOCK{ if exp.from }\VAR{exp.from} -\BLOCK{ endif } \BLOCK{ if exp.from }\VAR{exp.to}\BLOCK{ endif }} + {\VAR{exp.role}}{\BLOCK{ if exp.from_date }\VAR{exp.from_date} -\BLOCK{ endif } \BLOCK{ if exp.from_date }\VAR{exp.to_date}\BLOCK{ endif }} \BLOCK{ if exp.link} {\normalfont{\textit{\href{\VAR{exp.link}}{\VAR{exp.company}}}}} \BLOCK{else} @@ -61,7 +61,7 @@ \begin{rSection}{Education} \BLOCK{ for school in education } \BLOCK{ if school.university } - \textbf{\VAR{school.university}} \hfill {\VAR{school.from} - \VAR{school.to}} \\ + \textbf{\VAR{school.university}} \hfill {\VAR{school.from_date} - \VAR{school.to_date}} \\ \BLOCK{ if school.degree } {\VAR{school.degree}} \BLOCK{ endif } @@ -86,7 +86,7 @@ \BLOCK{else} {\VAR{project.name}} \BLOCK{ endif } - {\normalfont{\VAR{project.from} - \VAR{project.to}}}{}{} + {\normalfont{\VAR{project.from_date} - \VAR{project.to_date}}}{}{} \BLOCK{ for point in project.description} \item \VAR{point} \BLOCK{ endfor } @@ -123,6 +123,6 @@ \BLOCK{ endif } \newcommand\myfontsize{\fontsize{0.1pt}{0.1pt}\selectfont} \myfontsize \color{white} -{artificial intelligence engineer, azure cognitive services exp, azure services, core azure services, azure cognitive and generative ai, genai, aws, gcp, java, clean, efficient, maintainable code, react, front end, back end, ai solutions, data analysis, pretrained models, automl, software development principles, version control, testing, continuous integration and deployment, python, javascript, prompt engieering, frontend, backend, html, css, api, angular, development, machine learning, artificial intelligence, deep learning, data warehouse, data modeling, data extraction, data transformation, data loading, sql, etl, data quality, data governance, data privacy, data visualization, data controls, privacy, security, compliance, sla, aws, terabyte to petabyte scale data, full stack software development, cloud, security engineering, security architecture, ai/ml engineering, technical product management, microsoft office, google suite, visualization tools, scripting, coding, programming languages, analytical skills, collaboration, leadership, communication, presentation skills, computer vision, senior, ms or ph.d., 3d pose estimation, slam, robotics, object tracking, real-time systems, scalability, autonomy, robotic process automation, java, go, matlab, devops, ci/cd, programming, computer vision, data science, machine learning frameworks, deep learning toolsets, problem-solving, individual contributor, statistics, risk assessments, statistical modeling, apis, technical discussions, cross-functional teams} +\VAR{keywords}, \VAR{keywords}, {artificial intelligence engineer, azure cognitive services exp, azure services, core azure services, azure cognitive and generative ai, genai, aws, gcp, java, clean, efficient, maintainable code, react, front end, back end, ai solutions, data analysis, pretrained models, automl, software development principles, version control, testing, continuous integration and deployment, python, javascript, prompt engieering, frontend, backend, html, css, api, angular, development, machine learning, artificial intelligence, deep learning, data warehouse, data modeling, data extraction, data transformation, data loading, sql, etl, data quality, data governance, data privacy, data visualization, data controls, privacy, security, compliance, sla, aws, terabyte to petabyte scale data, full stack software development, cloud, security engineering, security architecture, ai/ml engineering, technical product management, microsoft office, google suite, visualization tools, scripting, coding, programming languages, analytical skills, collaboration, leadership, communication, presentation skills, computer vision, senior, ms or ph.d., 3d pose estimation, slam, robotics, object tracking, real-time systems, scalability, autonomy, robotic process automation, java, go, matlab, devops, ci/cd, programming, computer vision, data science, machine learning frameworks, deep learning toolsets, problem-solving, individual contributor, statistics, risk assessments, statistical modeling, apis, technical discussions, cross-functional teams} \end{document} \ No newline at end of file From ecdec9247a41df6538efdba796ef7acde4b5e51f Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:19:35 -0700 Subject: [PATCH 08/22] FEAT #30: Add feature to select more models in UI options for Ollama, GPT and Gemini --- .vscode/launch.json | 7 ++----- main.py | 10 ++++++---- web_app.py | 22 +++++++++++---------- zlm/variables.py | 47 +++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 67 insertions(+), 19 deletions(-) create mode 100644 zlm/variables.py diff --git a/.vscode/launch.json b/.vscode/launch.json index a4e8972..f467a5b 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -14,12 +14,9 @@ "justMyCode": true, // "args": [ // "-u", - // "https://www.linkedin.com/jobs/view/3970870027", - // "-p", - // "openai", + // "https://www.linkedin.com/jobs/view/4003781804", // "-m", - // "https://www.linkedin.com/in/keval-shah14/" - // // "/Users/saurabh/AA/convergent/projects/job-llm/zlm/demo_data/user_resume.pdf" + // "/Users/saurabh/AA/convergent/projects/job-llm/zlm/demo_data/user_profile.json" // ], "args": ["run", "web_app.py"] } diff --git a/main.py b/main.py index f8d305b..451d47b 100644 --- a/main.py +++ b/main.py @@ -12,7 +12,7 @@ from zlm import AutoApplyModel -def create_resume_cv(url, master_data, api_key, provider, downloads_dir): +def create_resume_cv(url, master_data, api_key, provider, model, downloads_dir): """ Creates a resume or CV using the Job-LLM model. @@ -21,12 +21,13 @@ def create_resume_cv(url, master_data, api_key, provider, downloads_dir): master_data (dict): The master data containing information about the candidate. api_key (str): The API key for OpenAI. provider (str): The LLM provider to use. Currently, only "OpenAI, Gemini" is supported. + model (str): The LLM model to use. downloads_dir (str): The directory where the generated resume or CV will be saved. Returns: None """ - job_llm = AutoApplyModel(api_key, provider, downloads_dir) + job_llm = AutoApplyModel(api_key, provider, model, downloads_dir) job_llm.resume_cv_pipeline(url, master_data) @@ -40,11 +41,12 @@ def create_resume_cv(url, master_data, api_key, provider, downloads_dir): parser.add_argument("-m", "--master_data", help="Path of user's master data file.") parser.add_argument("-k", "--api_key", default="os", help="LLM Provider API Keys") parser.add_argument("-d", "--downloads_dir", help="Give detailed path of folder") - parser.add_argument("-p", "--provider", default="gemini", help="LLM provider name. support for openai, gemini") + parser.add_argument("-p", "--provider", help="LLM provider name. support for openai, gemini") + parser.add_argument("-l", "--model", help="LLM model name") # Parse the arguments args = parser.parse_args() create_resume_cv( - args.url, args.master_data, args.api_key, args.provider, args.downloads_dir + args.url, args.master_data, args.api_key, args.provider, args.model, args.downloads_dir ) \ No newline at end of file diff --git a/web_app.py b/web_app.py index 3c20581..0c6bb0c 100644 --- a/web_app.py +++ b/web_app.py @@ -17,6 +17,7 @@ from zlm import AutoApplyModel from zlm.utils.utils import display_pdf, download_pdf, read_file, read_json from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity +from zlm.variables import LLM_MAPPING st.set_page_config( page_title="Resume Generator", @@ -104,11 +105,16 @@ def create_overleaf_button(resume_path): file = st.file_uploader("Upload your resume or any work-related data(PDF, JSON). [Recommended templates](https://github.com/Ztrimus/job-llm/tree/main/zlm/demo_data)", type=["json", "pdf"]) - col_1, col_2 = st.columns(2) + col_1, col_2, col_3 = st.columns(3) with col_1: - provider = st.selectbox("Select LLM provider([OpenAI](https://openai.com/blog/openai-api), [Gemini Pro](https://ai.google.dev/)):", ["gemini-pro", "gpt-4"]) + provider = st.selectbox("Select provider([OpenAI](https://openai.com/blog/openai-api), [Gemini Pro](https://ai.google.dev/)):", LLM_MAPPING.keys()) with col_2: - api_key = st.text_input("Enter API key:", type="password") + model = st.selectbox("Select model:", LLM_MAPPING[provider]['model']) + with col_3: + if provider != "Ollama": + api_key = st.text_input("Enter API key:", type="password", value="") + else: + api_key = None st.markdown("💡 GPT-4 is recommended for better results.", unsafe_allow_html=True) # Buttons side-by-side with styling @@ -134,18 +140,14 @@ def create_overleaf_button(resume_path): st.toast(":red[Please enter a job posting URL or paste the job description to get started]", icon="⚠️") st.stop() - if api_key == "" and provider != "gemini-pro": + if api_key == "" and provider != "Llama": st.toast(":red[Please enter the API key to get started]", icon="⚠️") st.stop() if file is not None and (url != "" or text != ""): download_resume_path = os.path.join(os.path.dirname(__file__), "output") - # st.write(f"download_resume_path: {download_resume_path}") - - llm_mapping = {'gpt-4':'openai', 'gemini-pro':'gemini'} - - resume_llm = AutoApplyModel(api_key=api_key, provider=llm_mapping[provider], downloads_dir=download_resume_path) + resume_llm = AutoApplyModel(api_key=api_key, provider=provider, model = model, downloads_dir=download_resume_path) # Save the uploaded file os.makedirs("uploads", exist_ok=True) @@ -256,7 +258,7 @@ def create_overleaf_button(resume_path): except Exception as e: st.error(f"An error occurred: {e}") - st.markdown("

Please try again!

", unsafe_allow_html=True) + st.markdown("

Please try again! Check the log in the dropdown for more details.

", unsafe_allow_html=True) st.stop() st.link_button("Report Feedback, Issues, or Contribute!", "https://github.com/Ztrimus/job-llm/issues", use_container_width=True) \ No newline at end of file diff --git a/zlm/variables.py b/zlm/variables.py new file mode 100644 index 0000000..572e364 --- /dev/null +++ b/zlm/variables.py @@ -0,0 +1,47 @@ +''' +----------------------------------------------------------------------- +File: zlm/variables.py +Creation Time: Aug 18th 2024, 5:26 am +Author: Saurabh Zinjad +Developer Email: saurabhzinjad@gmail.com +Copyright (c) 2023-2024 Saurabh Zinjad. All rights reserved | https://github.com/Ztrimus +----------------------------------------------------------------------- +''' + +from zlm.prompts.sections_prompt import EXPERIENCE, SKILLS, PROJECTS, EDUCATIONS, CERTIFICATIONS, ACHIEVEMENTS +from zlm.schemas.sections_schemas import Achievements, Certifications, Educations, Experiences, Projects, SkillSections + +GPT_EMBEDDING_MODEL = "text-embedding-ada-002" +# text-embedding-3-large, text-embedding-3-small + +GEMINI_EMBEDDING_MODEL = "models/text-embedding-004" +# models/embedding-001 + +OLLAMA_EMBEDDING_MODEL = "bge-m3" + +DEFAULT_LLM_PROVIDER = "Gemini" +DEFAULT_LLM_MODEL = "gemini-1.5-flash" + +LLM_MAPPING = { + 'GPT': { + "api_env": "OPENAI_API_KEY", + "model": ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-1106-preview", "gpt-3.5-turbo"], + }, + 'Gemini': { + "api_env": "GEMINI_API_KEY", + "model": ["gemini-1.5-flash", "gemini-1.5-flash-latest", "gemini-1.5-pro", "gemini-1.5-pro-latest", "gemini-1.5-pro-exp-0801"], # "gemini-1.0-pro", "gemini-1.0-pro-latest" + }, + 'Ollama': { + "api_env": None, + "model": ['llama3.1', 'llama3'], + } +} + +section_mapping = { + "work_experience": {"prompt":EXPERIENCE, "schema": Experiences}, + "skill_section": {"prompt":SKILLS, "schema": SkillSections}, + "projects": {"prompt":PROJECTS, "schema": Projects}, + "education": {"prompt":EDUCATIONS, "schema": Educations}, + "certifications": {"prompt":CERTIFICATIONS, "schema": Certifications}, + "achievements": {"prompt":ACHIEVEMENTS, "schema": Achievements}, +} \ No newline at end of file From 60bb1e39e0b4f651a166f10b21ba993155fd00d0 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:21:19 -0700 Subject: [PATCH 09/22] ENH #25: Integrate Langchain functionalities --- zlm/__init__.py | 217 +++++++++++++++++++----------------------------- 1 file changed, 86 insertions(+), 131 deletions(-) diff --git a/zlm/__init__.py b/zlm/__init__.py index 63f3f63..e58054d 100644 --- a/zlm/__init__.py +++ b/zlm/__init__.py @@ -14,16 +14,18 @@ import numpy as np import streamlit as st +from langchain.prompts import PromptTemplate +from langchain_core.output_parsers import JsonOutputParser +from zlm.schemas.sections_schemas import ResumeSchema from zlm.utils import utils from zlm.utils.latex_ops import latex_to_pdf -from zlm.utils.llm_models import ChatGPT, Gemini, TogetherAI -from zlm.utils.data_extraction import get_url_content, extract_text +from zlm.utils.llm_models import ChatGPT, Gemini, OllamaModel +from zlm.utils.data_extraction import read_data_from_url, extract_text from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity, vector_embedding_similarity - -from langchain_community.document_loaders import UnstructuredURLLoader -from langchain_community.document_loaders import PlaywrightURLLoader - +from zlm.prompts.resume_prompt import CV_GENERATOR, RESUME_WRITER_PERSONA, JOB_DETAILS_EXTRACTOR, RESUME_DETAILS_EXTRACTOR +from zlm.schemas.job_details_schema import JobDetails +from zlm.variables import DEFAULT_LLM_MODEL, DEFAULT_LLM_PROVIDER, LLM_MAPPING, section_mapping module_dir = os.path.dirname(__file__) demo_data_path = os.path.join(module_dir, "demo_data", "user_profile.json") @@ -37,10 +39,8 @@ class AutoApplyModel: Args: api_key (str): The OpenAI API key. downloads_dir (str, optional): The directory to save downloaded files. Defaults to the default download folder. - - Attributes: - api_key (str): The OpenAI API key. - downloads_dir (str): The directory to save downloaded files. + provider (str, optional): The LLM provider to use. Defaults to "Gemini". + model (str, optional): The LLM model to use. Defaults to "gemini-1.5-flash-latest". Methods: get_prompt(system_prompt_path: str) -> str: Returns the system prompt from the specified path. @@ -53,41 +53,33 @@ class AutoApplyModel: """ def __init__( - self, api_key: str, provider: str, downloads_dir: str = utils.get_default_download_folder() + self, api_key: str, provider: str, model: str, downloads_dir: str = utils.get_default_download_folder(), system_prompt: str = RESUME_WRITER_PERSONA ): - - if provider is None or provider.strip() == "": - self.provider = "gemini" - else: - self.provider = provider + self.system_prompt = system_prompt + self.provider = DEFAULT_LLM_PROVIDER if provider is None or provider.strip() == "" else provider + self.model = DEFAULT_LLM_MODEL if model is None or model.strip() == "" else model + self.downloads_dir = utils.get_default_download_folder() if downloads_dir is None or downloads_dir.strip() == "" else downloads_dir if api_key is None or api_key.strip() == "os": - if provider == "openai": - self.api_key = os.environ.get("OPENAI_API_KEY") - elif provider == "gemini": - self.api_key = os.environ.get("GEMINI_API_KEY") - print("===== GOT GEMINI API KEY ======") + api_env = LLM_MAPPING[self.provider]["api_env"] + if api_env != None and api_env.strip() != "": + self.api_key = os.environ.get(LLM_MAPPING[self.provider]["api_env"]) + else: + self.api_key = None else: - self.api_key = api_key + self.api_key = None - if downloads_dir is None or downloads_dir.strip() == "": - self.downloads_dir = utils.get_default_download_folder() - else: - self.downloads_dir = downloads_dir + self.llm = self.get_llm_instance() - # Define a function to perform similarity search between user and job description - def find_similar_points(self, user_embeddings, job_embeddings): - try: - relevant_points = set() - for embedding in job_embeddings['embedding']: - dot_products = np.dot(np.stack(user_embeddings['embedding']), embedding) - idx = np.argmax(dot_products) - relevant_points.add(user_embeddings.iloc[idx]['chunk']) - - return relevant_points - except Exception as e: - print(e) - return None + def get_llm_instance(self): + if self.provider == "GPT": + return ChatGPT(api_key=self.api_key, model=self.model, system_prompt=self.system_prompt) + elif self.provider == "Gemini": + return Gemini(api_key=self.api_key, model=self.model, system_prompt=self.system_prompt) + elif self.provider == "Ollama": + return OllamaModel(model=self.model, system_prompt=self.system_prompt) + else: + raise Exception("Invalid LLM Provider") def resume_to_json(self, pdf_path): """ @@ -99,49 +91,18 @@ def resume_to_json(self, pdf_path): Returns: dict: The resume data in JSON format. """ - # TODO: Prompt Template - system_prompt = utils.get_prompt( - os.path.join(prompt_path, "resume-extractor.txt") - ) - llm = self.get_llm_instance(system_prompt) resume_text = extract_text(pdf_path) - resume_json = llm.get_response(resume_text, need_json_output=True) + + json_parser = JsonOutputParser(pydantic_object=ResumeSchema) + + prompt = PromptTemplate( + template=RESUME_DETAILS_EXTRACTOR, + input_variables=["resume_text"], + partial_variables={"format_instructions": json_parser.get_format_instructions()} + ).format(resume_text=resume_text) + + resume_json = self.llm.get_response(prompt=prompt, need_json_output=True) return resume_json - - def read_data_from_url(self, urls): - try: - # loader = UnstructuredURLLoader(urls=urls, ssl_verify=False, remove_selectors=["header", "footer"]) - url_content = "" - loader = PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"]) - pages = loader.load() - - for page in pages: - if page.page_content.strip() != "": - # text = page.extract_text().split("\n") - text_list = page.page_content.split("\n") - - # Remove Unicode characters from each line - cleaned_texts = [re.sub(r'[^\x00-\x7F]+', '', line) for line in text_list] - cleaned_texts = [text.strip() for text in cleaned_texts if text.strip() not in ['', None]] - - # Join the lines into a single string - cleaned_texts_string = '\n'.join(cleaned_texts) - url_content += cleaned_texts_string - - return url_content - except Exception as e: - print(e) - return None - - def get_llm_instance(self, system_prompt): - if self.provider == "openai": - return ChatGPT(api_key=self.api_key, system_prompt=system_prompt) - elif self.provider == "together": - return TogetherAI(api_key=self.api_key, system_prompt=system_prompt) - elif self.provider == "gemini": - return Gemini(api_key=self.api_key, system_prompt=system_prompt) - else: - raise Exception("Invalid LLM Provider") @utils.measure_execution_time def user_data_extraction(self, user_data_path: str = demo_data_path, is_st=False): @@ -159,15 +120,14 @@ def user_data_extraction(self, user_data_path: str = demo_data_path, is_st=False if user_data_path is None or (type(user_data_path) is str and user_data_path.strip() == ""): user_data_path = demo_data_path - - # Read user data extension = os.path.splitext(user_data_path)[1] + if extension == ".pdf": user_data = self.resume_to_json(user_data_path) elif extension == ".json": user_data = utils.read_json(user_data_path) elif validators.url(user_data_path): - user_data = self.read_data_from_url([user_data_path]) + user_data = read_data_from_url([user_data_path]) pass else: raise Exception("Invalid file format. Please provide a PDF, JSON file or url.") @@ -190,31 +150,33 @@ def job_details_extraction(self, url: str=None, job_site_content: str=None, is_s print("\nExtracting job details...") try: - system_prompt = utils.get_prompt( - os.path.join(prompt_path, "persona-job-llm.txt") - ) + utils.get_prompt( - os.path.join(prompt_path, "extract-job-detail.txt") - ) - # TODO: Handle case where it returns None. sometime, website take time to load, but scraper complete before that. if url is not None and url.strip() != "": - job_site_content = get_url_content(url) - if job_site_content is None: - raise Exception("Unable to web scrape the job description.") + job_site_content = read_data_from_url(url) + if job_site_content: + json_parser = JsonOutputParser(pydantic_object=JobDetails) + + prompt = PromptTemplate( + template=JOB_DETAILS_EXTRACTOR, + input_variables=["job_description"], + partial_variables={"format_instructions": json_parser.get_format_instructions()} + ).format(job_description=job_site_content) - llm = self.get_llm_instance(system_prompt) - job_details = llm.get_response(job_site_content, need_json_output=True) - if url is not None and url.strip() != "": - job_details["url"] = url - jd_path = utils.job_doc_name(job_details, self.downloads_dir, "jd") + job_details = self.llm.get_response(prompt=prompt, need_json_output=True) - utils.write_json(jd_path, job_details) - print(f"Job Details JSON generated at: {jd_path}") + if url is not None and url.strip() != "": + job_details["url"] = url + jd_path = utils.job_doc_name(job_details, self.downloads_dir, "jd") - if url is not None and url.strip() != "": - del job_details['url'] - - return job_details, jd_path + utils.write_json(jd_path, job_details) + print(f"Job Details JSON generated at: {jd_path}") + + if url is not None and url.strip() != "": + del job_details['url'] + + return job_details, jd_path + else: + raise Exception("Unable to web scrape the job description.") except Exception as e: print(e) @@ -240,24 +202,13 @@ def cover_letter_generator(self, job_details: dict, user_data: dict, need_pdf: b print("\nGenerating Cover Letter...") try: - system_prompt = utils.get_prompt( - os.path.join(prompt_path, "persona-job-llm.txt") - ) + utils.get_prompt( - os.path.join(prompt_path, "generate-cover-letter.txt") - ) - query = f"""Provided Job description delimited by triple backticks(```) and \ - my resume or work information below delimited by triple dashes(---). - ``` - {json.dumps(job_details)} - ``` - - --- - {json.dumps(user_data)} - --- - """ - - llm = self.get_llm_instance(system_prompt) - cover_letter = llm.get_response(query, expecting_longer_output=True) + prompt = PromptTemplate( + template=CV_GENERATOR, + input_variables=["my_work_information", "job_description"], + ).format(job_description=job_details, my_work_information=user_data) + + cover_letter = self.llm.get_response(prompt=prompt, expecting_longer_output=True) + cv_path = utils.job_doc_name(job_details, self.downloads_dir, "cv") utils.write_file(cv_path, cover_letter) print("Cover Letter generated at: ", cv_path) @@ -292,7 +243,6 @@ def resume_builder(self, job_details: dict, user_data: dict, is_st=False): if is_st: st.toast("Generating Resume Details...") resume_details = dict() - system_prompt = utils.get_prompt(os.path.join(prompt_path, "persona-job-llm.txt")) # Personal Information Section if is_st: st.toast("Processing Resume's Personal Info Section...") @@ -307,15 +257,20 @@ def resume_builder(self, job_details: dict, user_data: dict, is_st=False): st.write(resume_details) # Other Sections - for section in ['work_experience', 'skill_section', 'projects', 'education', 'certifications', 'achievements']: + for section in ['work_experience', 'projects', 'skill_section', 'education', 'certifications', 'achievements']: section_log = f"Processing Resume's {section.upper()} Section..." if is_st: st.toast(section_log) - query = utils.get_prompt(os.path.join(prompt_path, "sections", f"{section}.txt")) - query = query.replace("", json.dumps(user_data[section])).replace("", json.dumps(job_details)) - llm = self.get_llm_instance(system_prompt) - response = llm.get_response(query, expecting_longer_output=True, need_json_output=True) + json_parser = JsonOutputParser(pydantic_object=section_mapping[section]["schema"]) + + prompt = PromptTemplate( + template=section_mapping[section]["prompt"], + partial_variables={"format_instructions": json_parser.get_format_instructions()} + ).format(section_data = json.dumps(user_data[section]), job_description = json.dumps(job_details)) + response = self.llm.get_response(prompt=prompt, expecting_longer_output=True, need_json_output=True) + + # Check for empty sections if response is not None and isinstance(response, dict): if section in response: if response[section]: @@ -328,7 +283,7 @@ def resume_builder(self, job_details: dict, user_data: dict, is_st=False): st.markdown(f"**{section.upper()} Section**") st.write(response) - resume_details['keywords'] = job_details['keywords'] + resume_details['keywords'] = ', '.join(job_details['keywords']) resume_path = utils.job_doc_name(job_details, self.downloads_dir, "resume") @@ -372,13 +327,13 @@ def resume_cv_pipeline(self, job_url: str, user_data_path: str = demo_data_path) job_details, jd_path = self.job_details_extraction(url=job_url) # job_details = read_json("/Users/saurabh/Downloads/JobLLM_Resume_CV/Netflix/Netflix_MachineLearning_JD.json") - # Generate cover letter - cv_details, cv_path = self.cover_letter_generator(job_details, user_data) - # Build resume resume_path, resume_details = self.resume_builder(job_details, user_data) # resume_details = read_json("/Users/saurabh/Downloads/JobLLM_Resume_CV/Netflix/Netflix_MachineLearning_resume.json") + # Generate cover letter + cv_details, cv_path = self.cover_letter_generator(job_details, user_data) + # Calculate metrics for metric in ['jaccard_similarity', 'overlap_coefficient', 'cosine_similarity']: print(f"\nCalculating {metric}...") From 9f9991b87c1c7e91ba419abd1aae83b4fc40c116 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:21:44 -0700 Subject: [PATCH 10/22] ENH #30: Integrate Llama3.1 through Ollama support --- README.md | 22 +++++- packages.txt | 5 +- zlm/utils/llm_models.py | 166 ++++++++++++++++------------------------ 3 files changed, 89 insertions(+), 104 deletions(-) diff --git a/README.md b/README.md index 1b51bbf..4b9b300 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,11 @@ cd job-llm brew install basictex sudo tlmgr install enumitem fontawesome ``` -5. Run following script to get result +5. If you want to run ollama models + ```sh + ollama pull llama3.1 + ``` +6. Run following script to get result ```bash >>> python main.py / --url "JOB_POSTING_URL" / @@ -119,6 +123,22 @@ cd job-llm ## 3. Citations If you find JobLLM useful in your research or applications, please consider giving us a star 🌟 and citing it. +```bibtex +@inproceedings{10.1145/3626772.3657680, +author = {Zinjad, Saurabh Bhausaheb and Bhattacharjee, Amrita and Bhilegaonkar, Amey and Liu, Huan}, +title = {ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement}, +series = {SIGIR '24}, +booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval}, +publisher = {Association for Computing Machinery}, +doi = {10.1145/3626772.3657680}, +url = {https://doi.org/10.1145/3626772.3657680}, +year = {2024}, +isbn = {9798400704314}, +location = {Washington DC, USA}, +address = {New York, NY, USA}, +} +``` + ```bibtex @misc{zinjad2024resumeflow, title={ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement}, diff --git a/packages.txt b/packages.txt index 83c352b..24df5a4 100644 --- a/packages.txt +++ b/packages.txt @@ -1,4 +1,7 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra -poppler-utils \ No newline at end of file +poppler-utils +ollama pull llama3.1 +ollama pull bge-m3 +playwright install \ No newline at end of file diff --git a/zlm/utils/llm_models.py b/zlm/utils/llm_models.py index 31c03ca..4d54b23 100644 --- a/zlm/utils/llm_models.py +++ b/zlm/utils/llm_models.py @@ -12,14 +12,20 @@ import pandas as pd import streamlit as st from openai import OpenAI +from langchain_community.llms.ollama import Ollama +from langchain_ollama import OllamaEmbeddings import google.generativeai as genai +from google.generativeai.types.generation_types import GenerationConfig + from zlm.utils.utils import parse_json_markdown +from zlm.variables import GEMINI_EMBEDDING_MODEL, GPT_EMBEDDING_MODEL, OLLAMA_EMBEDDING_MODEL class ChatGPT: - def __init__(self, api_key, system_prompt): + def __init__(self, api_key, model, system_prompt): if system_prompt.strip(): self.system_prompt = {"role": "system", "content": system_prompt} self.client = OpenAI(api_key=api_key) + self.model = model def get_response(self, prompt, expecting_longer_output=False, need_json_output=False): user_prompt = {"role": "user", "content": prompt} @@ -27,7 +33,7 @@ def get_response(self, prompt, expecting_longer_output=False, need_json_output=F try: # TODO: Decide value(temperature, top_p, max_tokens, stop) to get apt response completion = self.client.chat.completions.create( - model="gpt-4-1106-preview", + model=self.model, messages = [self.system_prompt, user_prompt], temperature=0, max_tokens = 4000 if expecting_longer_output else None, @@ -45,9 +51,9 @@ def get_response(self, prompt, expecting_longer_output=False, need_json_output=F except Exception as e: print(e) st.error(f"Error in OpenAI API, {e}") - st.markdown("

Please try again!

", unsafe_allow_html=True) + st.markdown("

Please try again! Check the log in the dropdown for more details.

", unsafe_allow_html=True) - def get_embedding(self, text, model="text-embedding-ada-002", task_type="retrieval_document"): + def get_embedding(self, text, model=GPT_EMBEDDING_MODEL, task_type="retrieval_document"): try: text = text.replace("\n", " ") return self.client.embeddings.create(input = [text], model=model).data[0].embedding @@ -56,22 +62,25 @@ def get_embedding(self, text, model="text-embedding-ada-002", task_type="retriev class Gemini: # TODO: Test and Improve support for Gemini API - def __init__(self, api_key, system_prompt): + def __init__(self, api_key, model, system_prompt): genai.configure(api_key=api_key) - self.system_prompt = "System Prompt\n======\n" + system_prompt if system_prompt.strip() else "" + self.system_prompt = system_prompt + self.model = model def get_response(self, prompt, expecting_longer_output=False, need_json_output=False): try: - user_prompt = "\n\nUser Prompt\n======\n" + prompt - entire_prompt = self.system_prompt + user_prompt + model = genai.GenerativeModel( + model_name=self.model, + system_instruction=self.system_prompt + ) - model = genai.GenerativeModel('gemini-pro') content = model.generate_content( - entire_prompt, - generation_config={ - "temperature": 0.7, - "max_output_tokens": 4000 if expecting_longer_output else None, - } + contents=prompt, + generation_config=GenerationConfig( + temperature=0.7, + max_output_tokens = 4000 if expecting_longer_output else None, + response_mime_type = "application/json" if need_json_output else None + ) ) if need_json_output: @@ -88,10 +97,10 @@ def get_response(self, prompt, expecting_longer_output=False, need_json_output=F except Exception as e: print(e) st.error(f"Error in Gemini API, {e}") - st.markdown("

Please try again!

", unsafe_allow_html=True) + st.markdown("

Please try again! Check the log in the dropdown for more details.

", unsafe_allow_html=True) return None - def get_embedding(self, content, model="models/embedding-001", task_type="retrieval_document"): + def get_embedding(self, content, model=GEMINI_EMBEDDING_MODEL, task_type="retrieval_document"): try: def embed_fn(data): result = genai.embed_content( @@ -111,100 +120,53 @@ def embed_fn(data): except Exception as e: print(e) - -class TogetherAI: - def __init__(self, api_key, system_prompt): - self.system_prompt = {"role": "system", "content": system_prompt} - self.client = OpenAI( - api_key=api_key, - base_url='https://api.together.xyz', - ) +class OllamaModel: + def __init__(self, model, system_prompt): + self.model = model + self.system_prompt = system_prompt def get_response(self, prompt, expecting_longer_output=False, need_json_output=False): - user_prompt = {"role": "user", "content": prompt} - try: - if expecting_longer_output: - completion = self.client.chat.completions.create( - model="mistralai/Mistral-7B-Instruct-v0.2", - messages = [self.system_prompt, user_prompt], - max_tokens = 7000, - ) - else: - completion = self.client.chat.completions.create( - model="mistralai/Mistral-7B-Instruct-v0.2", - messages = [self.system_prompt, user_prompt], + llm = Ollama( + model=self.model, + system=self.system_prompt, + temperature=0.8, + top_p=0.999, + top_k=250, + num_predict=4000 if expecting_longer_output else None, + # format='json' if need_json_output else None, ) - - response = completion.choices[0].message - content = response.content.strip() + content = llm.invoke(prompt) if need_json_output: - return parse_json_markdown(content) + result = parse_json_markdown(content) else: - return content + result = content + + if result is None: + st.write("LLM Response") + st.markdown(f"```json\n{content.text}\n```") + + return result except Exception as e: print(e) - -class Llama2: - def __init__(self, hf_token, system_prompt): - # !pip install sentencepiece==0.1.99 - # !pip install transformers==4.31.0 - # !pip install accelerate==0.21.0 - # !pip install bitsandbytes==0.41.1 - # https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212 + st.error(f"Error in Ollama model - {self.model}, {e}") + st.markdown("

Please try again! Check the log in the dropdown for more details.

", unsafe_allow_html=True) + return None + + def get_embedding(self, content, model=OLLAMA_EMBEDDING_MODEL, task_type="retrieval_document"): + try: + def embed_fn(data): + embedding = OllamaEmbeddings(model=model) + result = embedding.embed_query(data) + return result + + df = pd.DataFrame(content) + df.columns = ['chunk'] + df['embedding'] = df.apply(lambda row: embed_fn(row['chunk']), axis=1) + + return df - from transformers import LlamaForCausalLM, LlamaTokenizer - - self.system_prompt = system_prompt - self.tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token=hf_token) - self.model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", load_in_8bit=True, device_map="auto", token=hf_access_token) - self.generation_kwargs = { - "max_new_tokens": 512, - "top_p": 0.9, - "temperature": 0.6, - "repetition_penalty": 1.2, - "do_sample": True, - } - - def get_response(self, prompt_text, need_json_output=False): - B_INST, E_INST = "[INST]", "[/INST]" - B_SYS, E_SYS = "<>\n", "\n<>\n\n" - - # Special format required by the Llama2 Chat Model where we can use system messages to provide more context about the task - prompt = f"{B_INST} {B_SYS} {self.system_prompt} {E_SYS} {prompt_text} {E_INST}" - - prompt_ids = tokenizer(prompt, return_tensors="pt") - prompt_size = prompt_ids['input_ids'].size()[1] - - generate_ids = self.model.generate(prompt_ids.input_ids.to(self.model.device), **self.generation_kwargs) - generate_ids = generate_ids.squeeze() - - response = tokenizer.decode(generate_ids.squeeze()[prompt_size+1:], skip_special_tokens=True).strip() - - if need_json_output: - return parse_json_markdown(response) - else: - return response - - return response - -# DO: https://ai.google.dev/tutorials/python_quickstart#use_embeddings -# def compute_embedding(self, chunks): -# try: -# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") -# vector_embedding = FAISS.from_texts( texts = chunks, embedding=embeddings) -# return vector_embedding -# except Exception as e: -# print(e) -# return None - -# Define a function to compute embeddings for the text -# def compute_embedding(self, text): -# response = openai.Embed( -# input=text, -# model="text-davinci-003-001", -# max_tokens=50 -# ) -# return response['embedding'] \ No newline at end of file + except Exception as e: + print(e) \ No newline at end of file From 96eda35add5401f5c28a56d153da32c0501a6434 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:27:38 -0700 Subject: [PATCH 11/22] ENH: Update packages.txt with sudo commands for playwright and ollama pull --- packages.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/packages.txt b/packages.txt index 24df5a4..57d9bd0 100644 --- a/packages.txt +++ b/packages.txt @@ -2,6 +2,6 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra poppler-utils -ollama pull llama3.1 -ollama pull bge-m3 -playwright install \ No newline at end of file +sudo playwright install +sudo ollama pull llama3.1 +sudo ollama pull bge-m3 \ No newline at end of file From 40505d20a782b4ee1f3454999af220579eb43b61 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 08:40:01 -0700 Subject: [PATCH 12/22] ENH: Update packages.txt with latest dependencies and commands for playwright and ollama pull --- packages.txt | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/packages.txt b/packages.txt index 57d9bd0..3205f32 100644 --- a/packages.txt +++ b/packages.txt @@ -2,6 +2,7 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra poppler-utils -sudo playwright install -sudo ollama pull llama3.1 -sudo ollama pull bge-m3 \ No newline at end of file +ollama +playwright +"ollama pull llama3.1" +"ollama pull bge-m3" \ No newline at end of file From 19a851b22fdbdbad8a7b38cf1e848a184f15ac6a Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 17:57:47 -0700 Subject: [PATCH 13/22] ENH: remove Ollama option temporarily --- README.md | 9 +++++++++ packages.txt | 6 +----- zlm/variables.py | 8 ++++---- 3 files changed, 14 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 4b9b300..d386d24 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,15 @@ address = {New York, NY, USA}, ## 4. License JobLLM is under the MIT License and is supported for commercial usage. +## 5. TODO +Need to find way to install following command in streamlit +```sh +ollama +playwright +"ollama pull llama3.1" +"ollama pull bge-m3" +``` + ## 4. References - [Prompt engineering Guidelines](https://platform.openai.com/docs/guides/prompt-engineering) - [Overleaf LaTex Resume Template](https://www.overleaf.com/latex/templates/jakes-resume-anonymous/cstpnrbkhndn) diff --git a/packages.txt b/packages.txt index 3205f32..83c352b 100644 --- a/packages.txt +++ b/packages.txt @@ -1,8 +1,4 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra -poppler-utils -ollama -playwright -"ollama pull llama3.1" -"ollama pull bge-m3" \ No newline at end of file +poppler-utils \ No newline at end of file diff --git a/zlm/variables.py b/zlm/variables.py index 572e364..12603a0 100644 --- a/zlm/variables.py +++ b/zlm/variables.py @@ -31,10 +31,10 @@ "api_env": "GEMINI_API_KEY", "model": ["gemini-1.5-flash", "gemini-1.5-flash-latest", "gemini-1.5-pro", "gemini-1.5-pro-latest", "gemini-1.5-pro-exp-0801"], # "gemini-1.0-pro", "gemini-1.0-pro-latest" }, - 'Ollama': { - "api_env": None, - "model": ['llama3.1', 'llama3'], - } + # 'Ollama': { + # "api_env": None, + # "model": ['llama3.1', 'llama3'], + # } } section_mapping = { From aa1658744693d941655009f9d82d4209391b315b Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 18:08:20 -0700 Subject: [PATCH 14/22] ENH #30: Playwright installation --- packages.txt | 18 +++++++++++++++++- web_app.py | 2 ++ 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/packages.txt b/packages.txt index 83c352b..3231f7a 100644 --- a/packages.txt +++ b/packages.txt @@ -1,4 +1,20 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra -poppler-utils \ No newline at end of file +poppler-utils +libnss3 +libnspr4 +libatk1.0-0 +libatk-bridge2.0-0 +libcups2 +libdrm2 +libatspi2.0-0 +libxcomposite1 +libxdamage1 +libxfixes3 +libxrandr2 +libgbm1 +libxkbcommon0 +libpango-1.0-0 +libcairo2 +libasound2 \ No newline at end of file diff --git a/web_app.py b/web_app.py index 0c6bb0c..ddf6c1a 100644 --- a/web_app.py +++ b/web_app.py @@ -19,6 +19,8 @@ from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity from zlm.variables import LLM_MAPPING +os.system("playwright install") + st.set_page_config( page_title="Resume Generator", page_icon="📑", From c8e9dd6b2ba52407e5f1f733ed7f7dc9308d426f Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 18:18:24 -0700 Subject: [PATCH 15/22] ENH #30: system package for playwright --- packages.txt | 43 +++++++++++++++++++++++++++---------------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/packages.txt b/packages.txt index 3231f7a..943991e 100644 --- a/packages.txt +++ b/packages.txt @@ -2,19 +2,30 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra poppler-utils -libnss3 -libnspr4 -libatk1.0-0 -libatk-bridge2.0-0 -libcups2 -libdrm2 -libatspi2.0-0 -libxcomposite1 -libxdamage1 -libxfixes3 -libxrandr2 -libgbm1 -libxkbcommon0 -libpango-1.0-0 -libcairo2 -libasound2 \ No newline at end of file + +libxslt.so.1 +libevent-2.1.so.7 +libopus.so.0 +libwebpdemux.so.2 +libharfbuzz-icu.so.0 +libenchant-2.so.2 +libsecret-1.so.0 +libhyphen.so.0 +libmanette-0.2.so.0 +libflite.so.1 +libflite_usenglish.so.1 +libflite_cmu_grapheme_lang.so.1 +libflite_cmu_grapheme_lex.so.1 +libflite_cmu_indic_lang.so.1 +libflite_cmu_indic_lex.so.1 +libflite_cmulex.so.1 +libflite_cmu_time_awb.so.1 +libflite_cmu_us_awb.so.1 +libflite_cmu_us_kal16.so.1 +libflite_cmu_us_kal.so.1 +libflite_cmu_us_rms.so.1 +libflite_cmu_us_slt.so.1 +libEGL.so.1 +libgudev-1.0.so.0 +libGLESv2.so.2 +libx264.so \ No newline at end of file From 9d3363cf078ee407ee48c13b530225504eab84d9 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 18:24:19 -0700 Subject: [PATCH 16/22] ENH #30: package not found for nltk and playwright issue --- packages.txt | 43 ++++++++++++++++--------------------------- zlm/utils/metrics.py | 1 + 2 files changed, 17 insertions(+), 27 deletions(-) diff --git a/packages.txt b/packages.txt index 943991e..3231f7a 100644 --- a/packages.txt +++ b/packages.txt @@ -2,30 +2,19 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra poppler-utils - -libxslt.so.1 -libevent-2.1.so.7 -libopus.so.0 -libwebpdemux.so.2 -libharfbuzz-icu.so.0 -libenchant-2.so.2 -libsecret-1.so.0 -libhyphen.so.0 -libmanette-0.2.so.0 -libflite.so.1 -libflite_usenglish.so.1 -libflite_cmu_grapheme_lang.so.1 -libflite_cmu_grapheme_lex.so.1 -libflite_cmu_indic_lang.so.1 -libflite_cmu_indic_lex.so.1 -libflite_cmulex.so.1 -libflite_cmu_time_awb.so.1 -libflite_cmu_us_awb.so.1 -libflite_cmu_us_kal16.so.1 -libflite_cmu_us_kal.so.1 -libflite_cmu_us_rms.so.1 -libflite_cmu_us_slt.so.1 -libEGL.so.1 -libgudev-1.0.so.0 -libGLESv2.so.2 -libx264.so \ No newline at end of file +libnss3 +libnspr4 +libatk1.0-0 +libatk-bridge2.0-0 +libcups2 +libdrm2 +libatspi2.0-0 +libxcomposite1 +libxdamage1 +libxfixes3 +libxrandr2 +libgbm1 +libxkbcommon0 +libpango-1.0-0 +libcairo2 +libasound2 \ No newline at end of file diff --git a/zlm/utils/metrics.py b/zlm/utils/metrics.py index 03829b0..e3e3c43 100644 --- a/zlm/utils/metrics.py +++ b/zlm/utils/metrics.py @@ -20,6 +20,7 @@ from nltk.corpus import stopwords from nltk.stem import PorterStemmer, WordNetLemmatizer from nltk.tokenize import word_tokenize +nltk.download('averaged_perceptron_tagger') nltk.download('stopwords') nltk.download('punkt') From d7ad9872c6186a3b7348a6abe339d4b2b9b4050b Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Sun, 18 Aug 2024 18:30:45 -0700 Subject: [PATCH 17/22] ENH #25: Update packages.txt with additional system dependencies for Playwright and other libraries --- packages.txt | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/packages.txt b/packages.txt index 3231f7a..dc688ec 100644 --- a/packages.txt +++ b/packages.txt @@ -17,4 +17,30 @@ libgbm1 libxkbcommon0 libpango-1.0-0 libcairo2 -libasound2 \ No newline at end of file +libasound2 +libxslt.so.1 +libevent-2.1.so.7 +libopus.so.0 +libwebpdemux.so.2 +libharfbuzz-icu.so.0 +libenchant-2.so.2 +libsecret-1.so.0 +libhyphen.so.0 +libmanette-0.2.so.0 +libflite.so.1 +libflite_usenglish.so.1 +libflite_cmu_grapheme_lang.so.1 +libflite_cmu_grapheme_lex.so.1 +libflite_cmu_indic_lang.so.1 +libflite_cmu_indic_lex.so.1 +libflite_cmulex.so.1 +libflite_cmu_time_awb.so.1 +libflite_cmu_us_awb.so.1 +libflite_cmu_us_kal16.so.1 +libflite_cmu_us_kal.so.1 +libflite_cmu_us_rms.so.1 +libflite_cmu_us_slt.so.1 +libEGL.so.1 +libgudev-1.0.so.0 +libGLESv2.so.2 +libx264.so \ No newline at end of file From bf90ff7db38667a594cd28ee0cb63a3ab7598e90 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Fri, 23 Aug 2024 16:30:11 -0700 Subject: [PATCH 18/22] chore: update Playwright installation command --- web_app.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/web_app.py b/web_app.py index ddf6c1a..59f094f 100644 --- a/web_app.py +++ b/web_app.py @@ -13,13 +13,14 @@ import shutil import zipfile import streamlit as st +import subprocess from zlm import AutoApplyModel from zlm.utils.utils import display_pdf, download_pdf, read_file, read_json from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity from zlm.variables import LLM_MAPPING -os.system("playwright install") +subprocess.run(["playwright", "install", "--with-deps"]) st.set_page_config( page_title="Resume Generator", From ac09cf4e78686ce502a4ca41cbc6e194850d5393 Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Fri, 23 Aug 2024 16:31:25 -0700 Subject: [PATCH 19/22] Added Dev Container Folder --- .devcontainer/devcontainer.json | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 .devcontainer/devcontainer.json diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000..cb5c316 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,33 @@ +{ + "name": "Python 3", + // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile + "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye", + "customizations": { + "codespaces": { + "openFiles": [ + "README.md", + "web_app.py" + ] + }, + "vscode": { + "settings": {}, + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance" + ] + } + }, + "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y Date: Fri, 23 Aug 2024 16:36:04 -0700 Subject: [PATCH 20/22] Update packages.txt with poppler-utils system dependency --- packages.txt | 44 +------------------------------------------- 1 file changed, 1 insertion(+), 43 deletions(-) diff --git a/packages.txt b/packages.txt index dc688ec..83c352b 100644 --- a/packages.txt +++ b/packages.txt @@ -1,46 +1,4 @@ texlive-latex-base texlive-fonts-recommended texlive-fonts-extra -poppler-utils -libnss3 -libnspr4 -libatk1.0-0 -libatk-bridge2.0-0 -libcups2 -libdrm2 -libatspi2.0-0 -libxcomposite1 -libxdamage1 -libxfixes3 -libxrandr2 -libgbm1 -libxkbcommon0 -libpango-1.0-0 -libcairo2 -libasound2 -libxslt.so.1 -libevent-2.1.so.7 -libopus.so.0 -libwebpdemux.so.2 -libharfbuzz-icu.so.0 -libenchant-2.so.2 -libsecret-1.so.0 -libhyphen.so.0 -libmanette-0.2.so.0 -libflite.so.1 -libflite_usenglish.so.1 -libflite_cmu_grapheme_lang.so.1 -libflite_cmu_grapheme_lex.so.1 -libflite_cmu_indic_lang.so.1 -libflite_cmu_indic_lex.so.1 -libflite_cmulex.so.1 -libflite_cmu_time_awb.so.1 -libflite_cmu_us_awb.so.1 -libflite_cmu_us_kal16.so.1 -libflite_cmu_us_kal.so.1 -libflite_cmu_us_rms.so.1 -libflite_cmu_us_slt.so.1 -libEGL.so.1 -libgudev-1.0.so.0 -libGLESv2.so.2 -libx264.so \ No newline at end of file +poppler-utils \ No newline at end of file From 8fb21819ddf33f6ada0c6fe2f5be8235c61ee64d Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Fri, 23 Aug 2024 16:48:49 -0700 Subject: [PATCH 21/22] FIX #30: update Playwright installation command --- web_app.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web_app.py b/web_app.py index 59f094f..427bea9 100644 --- a/web_app.py +++ b/web_app.py @@ -13,14 +13,14 @@ import shutil import zipfile import streamlit as st -import subprocess from zlm import AutoApplyModel from zlm.utils.utils import display_pdf, download_pdf, read_file, read_json from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity from zlm.variables import LLM_MAPPING -subprocess.run(["playwright", "install", "--with-deps"]) +print("Installing playwright...") +os.system("playwright install") st.set_page_config( page_title="Resume Generator", From a9437efeaf07fb9cda9cb9f25f2ec920a2f4973b Mon Sep 17 00:00:00 2001 From: Saurabh Zinjad Date: Fri, 23 Aug 2024 16:53:49 -0700 Subject: [PATCH 22/22] ENH: Update packages.txt with system dependencies for Playwright and other libraries --- packages.txt | 2 ++ web_app.py | 1 + 2 files changed, 3 insertions(+) diff --git a/packages.txt b/packages.txt index 83c352b..63343f3 100644 --- a/packages.txt +++ b/packages.txt @@ -1,3 +1,5 @@ +libgbm1 +libasound2 texlive-latex-base texlive-fonts-recommended texlive-fonts-extra diff --git a/web_app.py b/web_app.py index 427bea9..9a5c581 100644 --- a/web_app.py +++ b/web_app.py @@ -21,6 +21,7 @@ print("Installing playwright...") os.system("playwright install") +os.system("sudo playwright install-deps") st.set_page_config( page_title="Resume Generator",