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README.md

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+\*\*Candidate Analysis using Emotion and Transcript Data\*\*
+
+This project analyzes candidate profiles based on their \*\*emotion
+scores\*\*, \*\*transcript scores\*\*, and \*\*transcript text\*\*
+extracted from their introduction videos. Using \*\*ChatGPT\*\* and
+\*\*Exploratory Data Analysis (EDA)\*\*, the goal is to generate
+valuable, actionable insights to aid in recruitment decisions.
+
+\*\*Project Overview\*\*
+
+\*\*Task Description:\*\*
+
+The dataset contains the following components:
+
+• \*\*Emotion Scores\*\*: A dataset tracking candidates' emotional
+responses throughout their video. • \*\*Transcript Scores\*\*: Scores
+based on the content of their transcript. • \*\*Transcript Text\*\*: The
+actual transcript from the candidates' introduction videos.
+
+Your objective is to:
+
+1\. \*\*Determine Recruitability\*\*: Analyze whether a candidate should
+be recruited or not, with data-backed reasons. 2. \*\*Communication
+Skills\*\*: Evaluate candidates' communication abilities and identify
+areas of expertise. 3. \*\*Additional Insights\*\*: Generate any other
+insights that aid in making decisions about each candidate.
+
+\*\*Tools Used:\*\*
+
+• \*\*ChatGPT API\*\*: For generating insights based on emotion and
+transcript data through prompt engineering. • \*\*Streamlit\*\*: For
+building an interactive interface to display insights. • \*\*Python
+Libraries\*\*: • pandas, numpy: For data manipulation and analysis. •
+matplotlib, seaborn: For visualizations. • google.generativeai: For
+leveraging the ChatGPT API. • Custom modules for processing candidate
+data and generating results.
+
+\*\*Key Components:\*\*
+
+• \*\*Emotion Analysis\*\*: Visualizes and compares emotions across
+candidates, providing insights into their emotional state during the
+interview. • \*\*Transcript Analysis\*\*: Evaluates the transcript
+content and generates actionable summaries through the use of prompt
+engineering. • \*\*Interactive Interface\*\*: Uses Streamlit to enable
+users to interact with the data, select candidates, and generate
+customized insights.
+
+\*\*Files\*\*
+
+• app.py: Main application file for running the Streamlit app. •
+transcrip.py: Handles candidate data loading, prompt generation, and
+calling the ChatGPT API to generate responses. • emotion.py: Manages
+visualization of emotion data, including comparisons and
+single-candidate analysis. • transcrip_score.py: Contains logic for
+analyzing and visualizing transcript scores. • gaze.py: Processes gaze
+data to complement transcript and emotion analysis.
+
+\*\*How to Run\*\*
+
+1\. Install dependencies: pip install -r requirements.txt 2. Run the
+Streamlit application: streamlit run app.py
+
+Deliverables
+
+• Prompt Engineering Documentation: Detailed steps for crafting
+effective prompts to extract relevant insights from the data. •
+Exploratory Data Analysis Report: Comprehensive analysis of emotion and
+transcript data, including visualizations and findings. • Actionable
+Insights: Insights about each candidate's suitability for recruitment,
+communication skills, and expertise.
+
+Evaluation Criteria:
+
+Your work will be evaluated based on:
+
+• The effectiveness of the prompts. • Clarity and depth of EDA findings.
+• Creativity and innovation in approach. • Quality and organization of
+documentation.
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference