Insight-Hire

Description

I've developed a powerful sentiment analysis tool called "Insight Hire" that leverages cutting-edge natural language processing and machine learning techniques to streamline the interview evaluation process. This application analyzes interview transcripts or videos and provides valuable insights into a candidate's sentiment, enabling hiring teams to make more informed decisions. Insight Hire utilizes OpenAI's state-of-the-art language models (GPT-3.5 and Whisper) for sentiment analysis and transcript generation. It offers a user-friendly Streamlit interface, allowing users to input text or YouTube video links and receive data-driven sentiment scores, detailed feedback, and downloadable reports.

Deployment

Check out the app in any of the links below .Any feedbacks or suggestionsare welcome.

• Hugging Face Spaces
• Streamlit Sharing

Features

• Sentiment Analysis: Get data-driven sentiment scores for key parameters such as enthusiasm, communication skills, and technical knowledge.
• Transcript Generation: Automatically generate transcripts from YouTube interview videos using OpenAI's Whisper API.
• Detailed Feedback: Receive in-depth feedback on the candidate's responses, including specific examples and actionable insights.
• Download Options: Download the detailed feedback as a .txt or .pdf file for easy sharing and archiving.

Real-Life Use Cases

Insight Hire can be a valuable tool for HR professionals and hiring teams to evaluate candidate sentiment during interviews. By analyzing interview transcripts or videos, the application provides objective insights and feedback, enabling more informed hiring decisions.

Usage

1. Clone the repository: git clone https://github.com/your-repo/insight-hire.git
2. Install the required dependencies: pip install -r requirements.txt
3. Set your OpenAI API key: openai.api_key = 'your-api-key'
4. Run the Streamlit app: streamlit run app.py
5. Follow the prompts in the app to analyze interview transcripts or videos.
6. Specify the parameters for sentiment analysis and review the sentiment scores and detailed feedback.

Forking and Contributions

We welcome contributions to improve Insight Hire! If you'd like to fork the project or contribute, please follow these steps:

1. Fork the repository on GitHub.
2. Create a new branch for your feature or bug fix: git checkout -b my-new-feature
3. Make your changes and commit them: git commit -am 'Add some feature'
4. Push your changes to the branch: git push origin my-new-feature
5. Submit a pull request detailing your changes.