HyderabadIndiaChapter_ChatbotInterviewPreparation/src/task-4

Hyderabad, India Chapter - Chatbot for Interview Preparation using NLP

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Table of Contents

• About
• Architecture
• Development
  • Prerequisites
  • Developing
  • Troubleshooting
  • Contributing

Development

This section will guide you through the development process of the Chatbot for Interview Preparation using NLP.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed the latest version of Python, pip and poetry
• You have a <Windows/Linux/Mac> machine. State which OS is supported/required.

Developing

Development Tasks:

1. User Introduction and Details Collection:

   • Integrate a general-purpose LLM (e.g., GPT-3.5/GPT-4, Cohere, Google Palm API) for handling user greetings and basic conversation.
   • Implement logic for extracting relevant details from user input to personalize the interaction.

2. Question Generation:

   • Develop a specific LLM agent using technologies like RAG or ICL for generating interview questions based on user details.
   • Ensure the agent understands contextual information provided by the user and tailors questions accordingly.

3. Interview Simulation:

   • Build an LLM capable of conducting a conversational interview, simulating a realistic environment.
   • Incorporate logic to ask questions, understand, and evaluate user responses, providing a dynamic interview experience.

4. Text-to-Speech (TTS) Integration:

   • Integrate Text-to-Speech functionality using Google Cloud Text-to-Speech API.
   • Implement TTS for converting written text, including interview questions and feedback, into spoken words for an interactive experience.

5. Speech-to-Text (STT) Integration:

   • Implement Speech-to-Text capability using technologies like Sphinx or Google Cloud Speech-to-Text API.
   • Enable the system to accurately convert spoken language into written text, facilitating analysis and feedback on user responses.

6. Overall Evaluation and Feedback:

   • Develop an LLM agent for evaluating user responses based on predefined criteria.
   • Implement logic to analyze answers in the context of interview performance, providing constructive feedback.

7. Closing and Encouragement:

   • Integrate a general-purpose LLM for delivering closing remarks, expressing gratitude, and offering encouragement.
   • Ensure the model provides positive and motivating language to conclude the interaction.

8. Optional: Follow-up Actions:

   • Develop an LLM capable of suggesting additional resources, practice sessions, or saving user progress.
   • Implement a recommendation system using the language model to provide relevant suggestions based on user performance.

9. Optional: User Feedback Analysis:

   • Integrate an LLM specialized in gathering user feedback.
   • Implement logic for sentiment analysis and feedback processing to make improvements based on user input.

10. Testing and Iteration:

    • Conduct thorough testing of the integrated system to ensure smooth and accurate functionality at each stage.
    • Gather user feedback during testing and iterate on the models and interactions for continuous improvement.

Troubleshooting

If you encounter any problems during the development, please check the Issues section of our GitHub repository.

Contributing

To contribute to the Chatbot, please see the CONTRIBUTING.md file.