ChatS-GPT

Relevancy for your texts and messages. Gain daily insights & create custom queries about your messages.

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Inspiration

The inspiration for this transformative project stemmed from the growing need to mitigate information overload and focus on meaningful, personal connections. As existing social platforms pivot towards influencer-centric content, we realized the necessity for a space dedicated solely to stay updated within one's personal social graph. Recognizing the significance of streamlined information consumption and the impact of personalized content, we were motivated to create an integrated platform that seamlessly curates and consolidates messages from various messaging apps, offering users a concise daily roundup of the top 5 discussions within their unique social circles. By harnessing the power of semantic analysis and tailored categorization, our aim is to empower individuals to stay connected with what truly matters, effortlessly enhancing their social experiences.

What it does

Our project is a groundbreaking social integration platform that seamlessly aggregates and categorizes messages from various messaging apps. It utilizes generative AI to identify and present the top five daily discussions from a user's personal social circle, focusing on topics of interest rather than individuals. Distinct from existing apps like Instagram, our solution prioritizes a user's personal social graph, delivering only the most significant discussions for each day. By curating these highlights, we aim to reduce information overload and provide users with a streamlined, personalized experience that keeps them connected to what matters most.

How we built it

Our project, ChatS-GPT, was meticulously crafted using a diverse tech stack to ensure optimal performance and user engagement. Leveraging the power of Next.js, we developed an intuitive and responsive front-end interface that provides a seamless user experience. MongoDB, our robust NoSQL database, played a pivotal role in securely storing user conversations and chats, enabling efficient data retrieval and management. Additionally, we integrated Python endpoints with OpenAI to facilitate quick and accurate responses, enhancing the overall user interaction. The implementation of prompt engineering techniques helped us curate the most pertinent highlights and insights from the vast array of conversations, ensuring that users receive only the most relevant information. Furthermore, the integration of the WhatsApp API client enabled us to effortlessly extract conversations from WhatsApp, effectively storing them in the database for further analysis and processing.

Challenges we ran into

  • Difficulty in migrating WhatsApp messages to MongoDB, leading to data integration challenges.
  • Challenging prompt engineering to format responses for optimal user display.
  • Establishing clear criteria for identifying the most important discussions from the chat proved challenging.
  • Integration of the front end with the backend presented a complex technical hurdle.

Accomplishments that we're proud of

  • Successful integration of diverse technologies, including Next.js, MongoDB, and WhatsApp API
  • Efficient data management through effective utilization of prompt engineering and AI responses from OpenAI
  • Seamless implementation of data migration strategies, ensuring a smooth flow of conversations from WhatsApp to the database
  • Development of a user-friendly interface that offers valuable insights and highlights from the user's personal social circle
  • Streamlined user experience by providing relevant and significant daily discussions through the top 5 chat recommendations.

What we learned

  • Proficiency in integrating various technologies such as Next.js, MongoDB, and WhatsApp API
  • Enhanced understanding of prompt engineering for optimized data extraction
  • Deeper insights into leveraging AI capabilities through OpenAI's Python endpoints
  • Improved problem-solving skills by overcoming challenges related to data migration and prompt formatting

What's next for ChatS-GPT

In the next phase of ChatS-GPT, we aim to enhance user interaction and personalization by implementing sentiment analysis to gauge the emotional tone of discussions. We plan to integrate a recommendation engine based on user preferences to suggest relevant topics. Additionally, we aspire to expand the platform's compatibility with a wider range of messaging applications and integrate features for seamless multimedia content sharing. Our team will focus on refining the user interface for a more intuitive and immersive experience, as well as improving the backend algorithms for more accurate topic categorization and trend analysis.

We will also look into the business side. We see this as being useful for business message upkeep, in addition to personal use.

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