Answer_Evaluation

Overview

This application uses Streamlit to facilitate recording or uploading audio files for technical interview answers. It processes the audio to generate a detailed report, which includes transcription, summary, key points extraction, clarity, relevance, depth evaluation, and sentiment analysis. The report is saved in a .docx file that can be downloaded.

Requirements

• Python 3.10
• streamlit
• openai
• transformers
• torch
• docx
• audio_recorder_streamlit
• python-dotenv

Setup

1. Install Dependencies:

   pip install streamlit openai transformers torch docx audio_recorder_streamlit python-dotenv

   or

   pip install -r req_st.txt

2. Create a .env File: Add your OpenAI API key to a .env file in the root directory:

   OPENAI_API_KEY=your_openai_api_key

Code Explanation

Functions

• transcribe_audio(): Transcribes audio files using OpenAI's API.
• abstract_summary_extraction(): Summarizes the transcription.
• key_points_extraction(): Extracts key points.
• evaluate_clarity_of_key_points(): Rates clarity of key points.
• evaluate_relevance_of_key_points(): Rates relevance of key points.
• evaluate_depth_of_key_points(): Rates depth of key points.
• evaluate_sentiment_of_transcription(): Performs sentiment analysis.
• split_complex_responses(): Splits responses into manageable parts.
• save_as_docx(): Saves the results in a .docx file.
• process_audio_and_generate_report(): Main function to process audio and generate the report.
• generate_interview_question(): Randomly selects an interview question.

Running the Application

To run the Streamlit app, use the following command:

streamlit run streamlit.py