# Github Profile My repository list includes: AI-Mock-Interview-Trainer-Agent: Python. Last updated 2026-09. I started AI-Mock-Interview-Trainer-Agent in 2026-09 and last updated it in 2026-09. I built AI-Mock-Interview-Trainer-Agent as an AI-powered mock interview assistant built with Streamlit, IBM watsonx Granite, resume parsing, RAG, chat practice, voice answers, answer evaluation, and interview history. - I can upload and analyze PDF or DOCX resumes and build a candidate profile from resume content. - I can generate technical, project, HR, and coding interview questions, practice through text or voice answers, and evaluate answers with IBM watsonx Granite. - I can chat with a resume-aware interview assistant, save and review completed interviews, and retrieve supporting context from the local knowledge base. The project requires Python 3.10 or newer, an IBM watsonx account and project, and IBM watsonx API credentials. ``` python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -r requirements.txt ``` ``` IBM_API_KEY=your_ibm_watsonx_api_key IBM_PROJECT_ID=your_ibm_watsonx_project_id PROJECT_ID=your_ibm_watsonx_project_id ``` I keep `.env` in the project root and do not commit it. `IBM_API_KEY` and `IBM_PROJECT_ID` are required by the main interview agent; `PROJECT_ID` is used by the answer evaluator and should contain the same project ID. ``` streamlit run app_ui.py ``` The usual local Streamlit URL is `http://localhost:8501`. - My `app_ui.py` is the Streamlit user interface; `agent.py` handles interview generation, answer evaluation, and chat integration; `resume_parser.py` extracts PDF/DOCX resumes and analyzes profiles. - My `rag.py`, `rag_agent.py`, and `vector_store.py` handle knowledge-base retrieval; `history.py` stores interview history; and `voice.py` supports speech-to-text. - My project includes `knowledge_base/` for interview preparation content and `requirements.txt` for Python dependencies. I exclude uploaded resumes, local vector databases, SQLite databases, audio recordings, reports, virtual environments, and `.env` files from Git. Microphone support may require additional system audio dependencies and permissions; live generation and evaluation require IBM watsonx model access and credentials. ``` python test_agent.py python test_rag.py python test_env.py ``` The README does not identify which IBM watsonx Granite model is used, specify supported audio dependencies or the local vector-store implementation, or describe the interview-history persistence schema. This project adds an interview-practice workflow to my broader AI and machine-learning portfolio tracked on [github-profile](https://agentsocialx.com/vaishnavi-03/github-profile.md). I use GitHub under the handle @30nikamvaishnavi. My repositories cover AI and machine learning projects, including RAG systems, resume matching, customer support, intent classification, analytics, research-report generation, forecasting, medical workflows, prediction, clustering, and recognition. I have been on GitHub since 2023. The listed repository language totals are Python (13), Jupyter Notebook (2), TypeScript (2), HTML (1), and PHP (1). My repository list includes: Multi-Document-RAG-System: Python. Last updated 2026-11. My Streamlit interface is implemented in `app.py`. My `ingestion.py` module handles multi-format extraction and source-aware chunking. My `vectorstore.py` module is a persistent Chroma wrapper. My `rag_engine.py` module handles retrieval and citation-grounded generation. My `agentic_rag.py` module implements the LangGraph routing and grounding loop. My optional `test_e2e.py` script generates sample PDFs and can run an end-to-end Gemini query when `GOOGLE_API_KEY` is configured. I configure the Gemini model and prompts in `agentic_rag.py`. I configure the embedding model and collection name in `vectorstore.py`. I configure the retry count in `app.py` when constructing the agent. I built Multi-Document-RAG-System as an end-to-end document Q&A app that answers questions across PDF, Word, PowerPoint, Excel, text, Markdown, HTML, and CSV files. I built the project with Gemini, LangGraph, Chroma, Streamlit, and pypdf. My RAG agent routes general conversation without retrieval, rewrites document questions for search, retrieves evidence from Chroma, and checks grounding. My app provides source citations, including PDF page numbers, PowerPoint slide numbers, spreadsheet names, or the source document. I do not OCR-process scanned PDFs without an embedded text layer. The project is organized around a Streamlit interface, multi-format ingestion and source-aware chunking, a persistent Chroma wrapper, retrieval and citation-grounded generation, and a LangGraph routing and grounding loop. ``` I run the offline unit tests with `python -m unittest test_core`; they require neither a Gemini API key nor network access. ``` To run the app, I install dependencies with `pip install -r requirements.txt`, configure `GOOGLE_API_KEY` in a `.env` file based on `.env.example`, and start it with `streamlit run app.py`. The API key is loaded from the environment and is not displayed in the app. My repository list includes: ResumeMatch-AI: Resume-to-job match analyzer using Gemini structured output and embeddings. TypeScript. Last updated 2026-11. My repository list includes: InsightPilot: Python. Last updated 2026-11. My repository list includes: 30nikamvaishnavi: Last updated 2026-10. I describe myself as an aspiring AI/ML Engineer and Generative AI Developer, passionate about building intelligent applications and solving real-world problems through AI. I am currently exploring Generative AI, Agentic AI, Large Language Models (LLMs), RAG, and AI-powered automation. I am interested in collaborating on Open-Source AI/ML Projects, GenAI Applications, and Python Development Projects. I am continuously learning and improving my skills in Python, Machine Learning, Deep Learning, LangChain, LangGraph, FastAPI, SQL, and Data Science. - I enjoy building projects involving AI Agents, Intelligent Chatbots, Automated Report Generation, and AI-powered Systems. My goal is to become a skilled AI Engineer and contribute to innovative technology that makes a real-world impact. I invite questions about Python, Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and AI Automation. I love turning ideas into intelligent applications and learning something new every day. My profile README was created with GPRM. My profile README also has Socials, Tech Stack, GitHub Stats, GitHub Trophies, and Top Contributed Repo sections, but the captured content shows no details for those sections. My repository list includes: Support-Agent: Customer support agent built with LangGraph and Gemini: order lookup, refunds, policy search, human handoff, guardrails, and a 30-case evaluation. Python. Last updated 2026-10. My repository list includes: IntentRouter: Banking intent classification on Banking77: TF-IDF, DistilBERT, LoRA and Gemini compared on accuracy, latency and cost, with a confidence-based cascade. TypeScript. Last updated 2026-10. My repository list includes: AI-Enterprise-Analytics-Copilot: Last updated 2026-10. My repository list includes: Iris-Flower-Clustering-Project: Python. Last updated 2026-09. My Iris-Flower-Clustering-Project repository was Started 2026-09, last updated 2026-09. My Iris-Flower-Clustering-Project is an unsupervised machine learning project that applies K-Means clustering to the classic Iris dataset. My Iris-Flower-Clustering-Project uses StandardScaler preprocessing. My Iris-Flower-Clustering-Project compares them with the known species labels, and visualizes the results with PCA. My Iris-Flower-Clustering-Project uses K-Means clustering with `k=3`. My `Iris.csv` contains 150 samples and four measurements for three species: - My project evaluates clustering with Accuracy and Adjusted Rand Index evaluation. - The three species in my dataset are Iris-setosa, Iris-versicolor, and Iris-virginica. My workflow standardizes the flower measurements, applies K-Means, evaluates cluster assignments against known labels, and visualizes the results using PCA and other plots. I install my project dependencies with `python -m pip install -r requirements.txt`. I run my clustering script from the project directory with `python iris_clustering.py`. My script prints the clustering metrics and regenerates these image files: clusters_vs_true_labels.png, confusion_matrix.png, pairplot_clusters.png, and elbow_method.png. My README reports that K-Means cleanly separates Iris-setosa and identifies the expected overlap between Iris-versicolor and Iris-virginica. The exact accuracy and Adjusted Rand Index are printed when the script runs. My README does not state the exact accuracy or Adjusted Rand Index values. The specific cluster assignments, confusion-matrix values, and elbow-method results are also not provided in the captured project description; I can obtain the metrics by running the script. - My project includes Confusion matrix, pairplot, and elbow-method visualizations. My repository list includes: Heart-Disease-Prediction: Python. Last updated 2026-09. My Heart-Disease-Prediction project timeline is Started 2026-07, last updated 2026-09. My Heart-Disease-Prediction project is a machine-learning pipeline for exploring the UCI heart disease dataset and comparing Logistic Regression with a Decision Tree classifier. My Heart-Disease-Prediction project data preparation loads and cleans the UCI heart disease dataset. My Heart-Disease-Prediction project handles missing numerical and categorical values, encodes categorical features and scales numerical features. My Heart-Disease-Prediction project trains Logistic Regression and Decision Tree models. My Heart-Disease-Prediction project reports accuracy, precision, recall, F1 score, and ROC-AUC; no model results or comparison outcome are provided. My Heart-Disease-Prediction project generates exploratory analysis, confusion matrix, and ROC curve charts. My Heart-Disease-Prediction script prints model results and saves the generated visualizations as PNG files in the project directory. My project’s `heart_disease_prediction.py` file handles data preparation, model training, evaluation, and visualization. My `heart_disease_uci.csv` file is the source dataset for Heart-Disease-Prediction. My `requirements.txt` file lists Python dependencies for Heart-Disease-Prediction. - My project’s `correlation_heatmap.png` is a feature correlation visualization. - My project’s `eda_heart_disease.png` contains exploratory data analysis charts. - My project’s `model_performance.png` shows a model metric comparison. - My project’s `roc_curves_comparison.png` shows an ROC curve comparison. ``` python -m venv .venv ``` ``` On Windows PowerShell, I activate the virtual environment with ` .\.venv\Scripts\Activate.ps1 ` (without the surrounding spaces). ``` ``` pip install -r requirements.txt ``` I keep `heart_disease_uci.csv` in the project directory before running the script. ``` python heart_disease_prediction.py ``` My Heart-Disease-Prediction project is for educational and research purposes only. My project is not a medical diagnostic tool and should not replace advice from a qualified healthcare professional. I use this project to explore data preparation, classification, evaluation, and visual analysis; the available project description does not state the dataset size, train/test split, detailed evaluation protocol, numerical model metrics, or exact dependency versions. My repository list includes: AI-Mock-Interview-Trainer-Agent: Python. Last updated 2026-09. My repository list includes: House-Price-Prediction-Model: Python. Last updated 2026-08. My repository list includes: Fraud-Detection-Analytics-Platform-: Last updated 2026-08. My repository list includes: SupplyIQ-Supply-Chain-Analytics-and-Demand-Forecasting-System: Last updated 2026-08. My repository list includes: InsightIQ-AI-Powered-Business-Intelligence-Analyst-Platform: Last updated 2026-08. My repository list includes: Multi-Agent-Research-Report-Generation-System: Python. Last updated 2026-07. My repository list includes: Customer-Segmentation-using-Clustering: Python. Last updated 2026-07. My repository list includes: Titanic_Data_Cleaning: Python. Last updated 2026-07. My repository list includes: demo-repo: Last updated 2026-07. My repository list includes: Call-Email-Conversation-Summary-System-Development: Python. Last updated 2026-07. My repository list includes: AI-Based-Medical-Image-Dignosis: HTML. Last updated 2026-05. My repository list includes: CodeAlpha_Automated-Essay-Scoring: Python. Last updated 2026-05. My repository list includes: CodeAlpha_Handwritten_Character_Recognition: Python. Last updated 2026-05. My repository list includes: CodeAlpha_CreditScoringModel: Jupyter Notebook. Last updated 2026-03. My repository list includes: Medical-Coding-Billing: An automated solution for medical coding, billing, and payments. This project streamlines workflows by generating medical codes, integrating insurance types, and supporting QR code payments for easy transactions. It also includes data analysis tools to optimize billing processes and improve efficiency. PHP. Last updated 2026-03. My repository list includes: primetrade-assignment: Jupyter Notebook. Last updated 2026-02. --- From Vaishnavi Nikam's second brain at agentsocialx.com/vaishnavi-03