The CobBot project aims to revolutionize debt collection by providing an intelligent and automated solution for sending personalized messages to debtors. Its core purpose is to streamline the recovery process, making it more efficient and user-friendly. The system empowers users to create highly customized messages and schedule their automated delivery via WhatsApp, ultimately simplifying debt management and improving recovery rates.

The CobBot's robust backend is built with Python, leveraging the FastAPI API for high performance and scalability. This setup ensures efficient handling of requests and seamless integration of new features. A key aspect of the backend is its intelligent automation: it uses Gemini AI to generate personalized and humanized collection messages, dynamically adapting to each debtor's profile for maximum effectiveness. For persistent data storage, the system utilizes JSON files as its database, offering a lightweight and flexible solution. The backend also handles the automated and scheduled delivery of messages via WhatsApp, employing Python libraries like PyWhatKit to ensure timely dispatch without incurring additional SMS costs. This project was developed as the final assignment for the 2nd edition of the Alura IA Immersion.

The user-friendly interface of the CobBot is crafted with modern web technologies: HTML for structuring the content, CSS for styling, and JavaScript for dynamic interactions. This combination delivers an intuitive and responsive web application. The frontend communicates with the backend via Fetch requests, ensuring a smooth and modern user experience. This design allows users to easily customize messages, schedule deliveries, and efficiently monitor the collection process, all through a clean and accessible interface.

Environment.IA is an innovative full-stack project engineered around the advanced capabilities of the Gemini API. This platform provides an intelligent, conversational interface designed to empower users with actionable insights for both sustainable living practices and effective plant lifecycle management. It leverages advanced AI methodologies to facilitate waste reduction, foster resourcefulness, and optimize horticultural outcomes.

This project showcases a robust application of cutting-edge AI technologies, demonstrating the potential for intelligent systems to drive positive environmental impact and enhance individual resource management. Ongoing development is focused on expanding feature sets, refining model accuracy, and optimizing user experience for a more comprehensive and impactful solution.

This project is a powerful web-based tool designed to generate comprehensive business reports. By leveraging a combination of web scraping and advanced AI, it can quickly gather and analyze information about any company, providing detailed insights into its logistics, supply chain, and competitive advantages. This tool is ideal for sales professionals, researchers, or anyone needing to prepare for a meeting or gain a deeper understanding of a business.

Core Features and Technologies

The application is built on a robust technology stack, integrating several key Python libraries to handle data collection, processing, and analysis.

Data Collection and Search

• google-api-python-client & googlesearch-python: These libraries are the primary engines for web search. The google-api-python-client uses the Google Custom Search API for structured queries, while googlesearch-python serves as a reliable fallback, ensuring the tool can always find relevant web links and company CNPJs (Brazil's business registry number).
• requests: This library handles all HTTP requests, fetching the HTML content from the websites found during the search phase.
• beautifulsoup4: After requests downloads the HTML, beautifulsoup4 parses the content. It's essential for navigating the page structure and extracting clean text, links, and other key information, while also removing unnecessary scripts and styles.

Processing and Analysis

• google-genai: This is the core of the analysis engine. The tool uses this library to communicate with Google's powerful Gemini AI models. It feeds all the collected text and CNPJ data to the AI, which then generates a detailed, customized report about the company.
• re (RegEx): A native Python library used for pattern matching. It's crucial for efficiently extracting CNPJ numbers, which follow a specific numerical or formatted pattern, from web content and links.

Application Interface

• streamlit: This library builds the entire web interface, transforming a simple Python script into an interactive web application. It creates all the front-end elements, including input forms, buttons, and the final report display, providing a seamless user experience.

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