AI & Data Science Projects

This project uses PyTorch to demonstrate the application of various explainability techniques on a pre-trained ResNet-50 model using butterfly images. It helps to understand how Grad-CAM explainability techniques can be applied to deep learning models.
In this case, the ResNet-50 Classifier is used to classify images of butterflies, and the explainability techniques help to visualize which parts of the images are most important for the model's predictions.
It also highlights the importance of model explainability, and can help us identify potential areas for improvement. It can assist us in the event that we want to retrain the model, to give it effective images, knowing what it attends to, or use a different architecture that may be better suited for distinguishing between similar species.

This project demonstrates a LangGraph pipeline served with FastAPI + Strawberry GraphQL, integrated with vector search in Pinecone. A crawler scans my website to collect all accessible links, and the contents are loaded and chunked for efficient processing by the models. Embeddings are then generated from these chunks and stored in Pinecone for later retrieval.
When a user enters a question in the Flask frontend, it is sent to the FastAPI GraphQL backend, which triggers the execution of the LangGraph pipeline. An embedding is created from the question, and a similarity search is performed in Pinecone to find semantically related text. This context is combined with the question to form a prompt for Google's Gemini Flash chat model. The answer is returned through the same pipeline and displayed to the user.
The RAG application is designed to be adaptable: it can work with any website by changing the target domain in the GitHub code, and it can be extended to other data sources by modifying the loader.

I used Python and Streamlit to create a web application that connects to OpenAI's GPT2, hosted on Hugging Face to complete a sentence. It then uses SD-Turbo to generate a related image and Toxic-BERT to check for inappropriate content generation by the LLM. GitHub CI/CD was used to push the code to an AWS EC2 instance hosting the application.

Written in Rust and containerized with Docker using the Amazon Linux 2023 base image during the CI/CD process in GitLab. The image was pushed to Amazon Elastic Container Registry and used to create a Lambda function with an API gateway to serve the search function. Its main function is to connect to a Qdrant vector database and do a vector search using the text provided to the API. It returns records containing semantically similar words.
The vector database was populated with information on movies from a JSONL file, and the embeddings were created with Cohere’s Embed API.