1. Understanding the Framework: I started by thoroughly reviewing the CrewAI documentation and tutorials. This gave me a solid understanding of how to set up and configure the framework, as well as how to utilize its features effectively. I took time to understand the key concepts and best practices recommended in the tutorials, and even built a small side project to learn first hand how everything worked.
2. Setting Up the Environment: I installed CrewAI along with any necessary dependencies, ensuring my development environment was correctly configured. This involved setting up virtual environments and managing dependencies with Poetry.
3. Handling Input: I created a module to handle the input blood test report. I focused on extracting text from the PDF or text file using tools and libraries. The challenge here was ensuring accurate text extraction from potentially varied report formats. I used PyPDF library to handle different types of PDFs and image-based reports.
4. Analyzing the Report: I developed an analysis agent to interpret the extracted data. This agent focused on identifying key health indicators such as cholesterol levels, blood sugar, and other relevant metrics. Summarizing the results in a user-friendly manner required careful consideration of medical terminology and readability. I implemented natural language processing techniques to ensure the analysis was both accurate and understandable.
5. Searching for Articles: For the article search, I created a search agent that could find relevant health articles online. This involved using web scraping tools and APIs to gather articles that matched the user's health needs based on the analysis. A challenge here was ensuring the relevance and credibility of the articles retrieved.
6. Generating Recommendations: I built an agent to generate personalized health recommendations based on the analysis and the articles found. This agent provided actionable advice tailored to the user's specific health indicators. One challenge was ensuring the recommendations were practical and backed by reliable sources. I cross-referenced the articles to ensure consistency and accuracy in the advice given.
7. Testing and Refining: I conducted extensive testing of the entire system using sample data to verify that each component functioned as intended. This included testing text extraction accuracy, analysis precision, and the relevance of search results. I encountered and resolved issues related to data format inconsistencies and integration challenges, making adjustments to enhance system performance and reliability.
8. Documentation: Finally, I prepared a comprehensive README file. This document explains how to set up and run the code, provides an overview of the project, and describes the approach taken. I included details on the challenges faced and the solutions implemented to address them.
This approach allowed me to build a system for analyzing blood test reports and providing valuable health recommendations, while also addressing key challenges along the way.