AI-Enabled Candidate Evaluation

---

Overview

This project aims to revolutionize the candidate selection process by leveraging Google's Gemini Pro and Phone call platform Bland.ai . In the fast-paced world of recruitment, traditional methods can be inefficient and time-consuming. With the integration of AI, we streamline resume screening, phone interviews, and candidate evaluation, enhancing efficiency and improving the quality of hires.

---

Key Features

• AI-Powered Resume Screening: Utilizes the GPT-3.5 model to analyze resumes and provide a detailed summary, conclusion, and relevant questions for initial screening.

• Automated Phone Interviews: Integrates with Bland.ai to conduct phone interviews, customizing tasks and greetings for a tailored candidate experience.

• Interactive Chatbots: AI-powered chatbots provide real-time responses to candidates' queries, enhancing their experience and engagement throughout the recruitment process.

• Personalized Candidate Recommendations: AI analyzes candidates' profiles and preferences to recommend personalized job opportunities, improving the candidate matching process.

• Onboarding and Training Plans: AI assesses new hires' skills and identifies knowledge gaps, recommending targeted training programs to accelerate their integration into the company.

---

Installation

1. Clone the repository:

   git clone https://github.com/Ionio-io/AI-Driven-Automation-for-Candidate-Screening.git

2. Install the required Python libraries:

   pip install -r requirements.txt

---

Usage

Step 1: Resume/CV Upload and Extraction

• Upload the resume/CV and extract the text using the PyPDF library. The extracted text will be stored in a text file named “extracted_text.txt”.

Step 2: Job Role Input

• Specify the job role for which the candidate is being evaluated, assigning the {role} variable.

Step 3: Large Language Model (LLM) Analysis

• Pass the extracted resume/CV text and the specified job role to the "gpt-3.5-turbo-0125" model.

Step 4: Initial Screening Decision

• Based on the LLM's final conclusion, decide whether to proceed with the application, completing the first phase of screening.

Step 5: Phone Interview

• Utilize the Bland.ai service to conduct a phone interview, customizing tasks and greetings for a tailored candidate experience.

Step 6: Final Conclusion

• Provide the phone interview transcript to the LLM again to generate a final conclusion on the candidate's suitability for the role.

---

License

This project is licensed under the MIT License.

---