🚀Quantum_Interview_AI

The Quantum Interview AI This innovative tool harnesses the power of artificial intelligence to support users during interviews by generating contextually accurate responses tailored to their resume and the interview questions. With a real-time teleprompter interface, users can seamlessly deliver their answers while maintaining eye contact with the interviewer, ensuring a confident and polished performance.

✨ Features

• 🤖 Multiple AI Model Integration: Supports OpenAI GPT-4, Google Gemini, Meta LLaMA, Anthropic Claude, and Ollama create tailored responses based on the user's resume and interview questions.
• 🎤 Speech Recognition: Uses the Vosk speech recognition model to transcribe audio questions in real-time for seamless interaction.
• 📺 Teleprompter Interface: A user-friendly GUI displays AI-generated responses clearly, helping users deliver answers confidently.
• 📄 Resume Parsing: Extracts relevant information from the user's PDF resume to provide context for the AI's responses.
• ⌨️ Keyboard Control: Enables users to pause and resume the application using keyboard shortcuts for greater control during practice sessions or live interviews.
• 🔩 User-Friendly Interface: Simple command-line interface for selecting AI models and initiating the interview process.

🛠️ How It Works

1. 📑 Resume Data Extraction: The application reads the user's resume and extracts key information to inform the response generation process.
2. 🗣️ Real-Time Question Transcription: As the interviewer asks questions, the application listens and transcribes them using speech recognition technology.
3. 🧠 Contextual Response Generation: The AI processes the transcribed question along with the resume data to generate a relevant response.
4. 📢 Display on Teleprompter: The generated response is displayed on the teleprompter interface, enabling the user to read the answer while engaging with the interviewer.

🧩 Installation

📋 Prerequisites

• Python 3.12.4 AND ABOVE installed on your system
• An OpenAI API key (replace "your_openai_api_key_here" in the code with your actual API key)

🔐 Set Up AI Model APIs

OpenAI GPT-4

1. Sign up at OpenAI and obtain your API key.
2. Replace "your_openai_api_key_here" in the script with your actual API key.

Google Gemini

1. Note: As of my knowledge cutoff in September 2021, Google’s Gemini model details might be updated. Ensure you have access and obtain the necessary API keys or endpoints from Google Cloud.
2. Replace "your_gemini_api_key_here" and "https://gemini-api.google.com/v1/generate" with the actual API key and endpoint.

Meta LLaMA

1. Obtain access to Meta's LLaMA and set up the API endpoint as per Meta's documentation.
2. Replace "http://your_llama_api_endpoint_here" with your actual LLaMA API endpoint.

Anthropic Claude

1. Sign up at Anthropic and obtain your API key.
2. Replace "your_anthropic_api_key_here" and "https://api.anthropic.com/v1/complete" with your actual API key and endpoint.

Ollama

1. Install Ollama by following the Ollama Installation Guide.
2. Start the Ollama server to access the API. By default, Ollama runs on http://localhost:11434.
3. Replace "http://localhost:11434/v1/chat/completions" with your Ollama API endpoint if different.

• Vosk speech recognition model (download from Vosk Models
• Here’s a list of Vosk English speech recognition models and their system requirements:

1. Lightweight English Models

• Model size: ~50 MB
• System requirements:
  • RAM: Minimum 1 GB
  • CPU: Mid-range CPU (equivalent to Raspberry Pi or Android devices)
  • Performance: Suitable for real-time offline speech recognition on constrained devices【7†source】【8†source】.

2. Large English Models for Quality listening

• Model size: 1.4 GB to 4.4 GB
• System requirements:
  • RAM: 4 GB or more recommended
  • CPU: Multi-core processors
  • Performance: Designed for server environments with better accuracy but higher resource consumption【8†source】【9†source】.

🔧 Configuration

1. Open the main.py script in your preferred code editor.
2. Update the API keys and endpoints for each AI model as outlined in the Installation section.
3. Ensure the vosk_model_path is correctly set to the location of your downloaded Vosk model.
4. Place your resume in PDF format and update the resume_path variable in the main.py script to point to your resume file.

📦 Libraries

The application requires the following libraries:

• Vosk: For speech recognition
• PyAudio: For audio input and processing
• OpenAI: For interacting with the OpenAI API
• PyPDF2: For reading PDF files
• Requests: HTTP requests for API interactions
• Tkinter: For creating the GUI (included with Python)
• Keyboard: For detecting keyboard events

📄 Requirements File

The required libraries and their versions are listed in the requirements.txt file. You can install them using the following command:

pip install -r requirements.txt

The requirements.txt file includes:

vosk==0.3.45
pyaudio==0.2.14
openai==1.37.1
PyPDF2==3.0.1
keyboard==0.13.5

▶️ Running the Application

You can run the application using one of the following methods:

🔧 Method 1: Using a Virtual Environment

Creating a virtual environment is recommended to manage dependencies separately from your global Python installation.

1. Clone the Repository:

   git clone https://github.com/{username}/Quantum_Interview_AI.git
   cd Quantum_Interview_AI

2. Create a Virtual Environment:

   python -m venv venv

3. Activate the Virtual Environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

4. Install the Required Libraries:

   pip install -r requirements.txt

5. Download and Specify the Path for the Vosk Model.

6. Run the Application:

   python main.py

7. Deactivate the Virtual Environment (after you're done):

   deactivate

⚙️ Method 2: Normal Setup (Without Virtual Environment)

If you prefer to install the dependencies globally without using a virtual environment, follow these steps:

1. Clone the Repository:

   git clone https://github.com/{username}/Quantum_Interview_AI.git
   cd Quantum_Interview_AI

2. Install the Required Libraries:

   pip install -r requirements.txt

3. Download and Specify the Path for the Vosk Model.

4. Run the Application:

   python main.py

🤝 Contributing

Contributions are welcome! Whether it's improving documentation, suggesting new features, or fixing bugs, your input is valuable.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

💼 Applications

The Quantum_Interview_AI can be utilized in various scenarios, including:

• 💼 Job Interviews: Candidates can practice with the AI to prepare for real interviews, receiving tailored answers based on their resumes.
• 💻 Virtual Meetings: During online meetings or webinars, users can ask questions and get immediate AI-generated responses to enhance their presentations.
• 🎤 Public Speaking: Individuals can practice speeches and receive feedback on their delivery by reading AI-generated content.
• 🏢 Corporate Training: New employees can use the tool for onboarding, helping them prepare for real-world interactions and interviews.

📝 Conclusion

The Quantum_Interview_AI with AI is a valuable tool for anyone looking to enhance their interview skills and communication abilities. By combining AI-generated responses with a teleprompter interface, it provides users with a unique and effective way to prepare for and navigate interviews confidently.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.