The problem Chatfiles.ai solves

This application is a versatile tool designed to enhance user interactions with PDF documents. Its core functionality allows users to upload PDF files and input questions, streamlining the process of obtaining information and answers from these files. By intelligently parsing and extracting relevant text from the PDFs, the app employs advanced algorithms to provide accurate responses to user queries, significantly improving search and research efficiency.

One of the app's standout features is its comprehensive history log, which records all prior searches and interactions. This feature empowers users by granting them easy access to past inquiries, responses, and research progress, eliminating the need to duplicate searches.

Furthermore, the "All Information" feature consolidates all pertinent content from a particular search, offering users a consolidated overview of their research topic. This comprehensive summary simplifies access to valuable insights.

However, the app's most innovative feature is its support for speech-to-text functionality. Users can conveniently interact with the app by speaking their questions, which are then transcribed and processed. This voice-activated feature not only enhances accessibility but also introduces hands-free information retrieval capabilities.

In essence, this multifaceted application combines document processing, information retrieval, history tracking, and voice interaction to create a sophisticated tool that caters to users' needs for efficient, comprehensive, and user-friendly document research. It has the potential to significantly boost productivity and streamline the process of accessing valuable information within PDF documents.

Challenges we ran into

During the project, several hurdles were encountered that posed challenges to its successful implementation. The primary obstacles included difficulties with the speech-to-text functionality, issues related to the cost associated with OpenAI services, and limitations in effectively extracting and processing content from PDF files. While the backend of the application was well-developed, these hurdles impeded the overall functionality and user experience.

Firstly, the integration of speech-to-text technology within the Django framework presented challenges. The technology struggled to accurately transcribe spoken input, impacting the application's voice interaction feature. These difficulties required troubleshooting and fine-tuning to achieve reliable speech recognition.

Secondly, the integration of OpenAI services for natural language processing and question-answering required the provision of billing information, rendering some functionalities behind a paywall. This posed a limitation for users seeking comprehensive responses.

Lastly, the application faced issues when it came to efficiently reading and extracting information from PDF files. PDF parsing was not as effective as desired, which hampered the app's ability to provide detailed answers from uploaded documents.

Despite these hurdles, the core backend of the application was well-implemented and functional. Addressing the challenges with speech-to-text accuracy, OpenAI costs, and PDF content extraction could further enhance the application's capabilities and user satisfaction.