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Technologies Involved:
PYTHON
Area Of Work: Machine Learning
Project Description

P2P_AI, a digital media innovation firm focused on reshaping how Nigerians engage with news, approached Oodles for a solution that could automate the process of turning trending stories into interactive polls. The client sought a backend-driven system to fetch trending topics, analyze news articles from local sources, and generate polls using AI. Oodles delivered a dynamic backend setup with Django, LLM integration, and Docker support. 
Scope Of Work

The client aimed to automate real-time topic tracking and transform content into engaging audience polls. The client required a backend solution with trend fetching, article parsing, poll creation, and a flexible deployment mechanism. Oodles designed the solution to cover areas of work like topic extraction, LLM-powered poll generation, secure environment configuration, and Docker-based deployment.
Our Solution

To address the client's requirements, Oodles implemented a modular and AI-enhanced backend system using Django and Python 3.10. 

Key features include:

  • Automated Topic & Article Extraction: APIs for trending topics and articles allowed real-time data capture from Google Trends and sources like PunchNG.
  • AI-Powered Poll Generation: GPT-based LLMs generated context-aware polls, transforming extracted articles into meaningful engagement tools.
  • Manual Input Flexibility: Custom topic addition via a dedicated endpoint enabled users to manually push content for poll generation.
  • Secure & Scalable Environment Setup: Environment variables were managed via .env files. Django's settings enabled safe debugging and host control.

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