https://github.com/zahidshaikh10101/hirebot

HireBot is built using Streamlit. It extracts key details from a resume (PDF) and provides insights like skills, experience, education, strengths, and weaknesses. It also integrates a job search feature to match resumes with relevant job postings.
https://github.com/zahidshaikh10101/hirebot

api beautifulsoup gemini generative-ai nlp python streamlit transformers

Last synced: 2 months ago
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Host: GitHub
URL: https://github.com/zahidshaikh10101/hirebot
Owner: zahidshaikh10101
Created: 2025-02-13T17:24:11.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2025-02-16T18:29:40.000Z (over 1 year ago)
Last Synced: 2025-04-09T09:50:12.825Z (about 1 year ago)
Topics: api, beautifulsoup, gemini, generative-ai, nlp, python, streamlit, transformers
Language: Python
Homepage: https://hirebot-z7s8s6.streamlit.app/
Size: 407 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# HireBot

### Resume Parsing Functions:

- **extract\_text\_from\_pdf(pdf\_file):** Extracts text content from a given PDF file by reading its bytes and using a text extraction function.
- **extract\_contact\_number\_from\_resume(text):** Uses regex to identify and extract a contact number from the resume text.
- **extract\_email\_from\_resume(text):** Uses regex to identify and extract an email address from the resume text.
- **extract\_name(resume\_text):** Uses the spaCy NLP library and pattern matching to extract a full name (first, middle, and last names) from the resume text.
- **extract\_degree\_name(resume\_text):** Uses a generative AI model to extract the degree name from the resume text, ensuring that the current degree appears first.
- **extract\_college\_name(resume\_text):** Uses a generative AI model to extract the college/university name from the resume text.
- **extract\_company\_name(resume\_text):** Uses a generative AI model to identify company names and their corresponding years of experience from the resume text.
- **extract\_total\_experience(resume\_text):** Uses a generative AI model to calculate the total years of experience by summing up the experience across multiple companies.
- **extract\_strength(resume\_text):** Identifies four key strengths from the resume text using Google Generative AI.
- **extract\_weakness(resume\_text):** Identifies four key weaknesses from the resume text using Google Generative AI.
- **extract\_skills(resume\_text):** Extracts and returns the top 20 relevant skills from the resume text, ensuring uniqueness.
- **extract\_designation(resume\_text):** Identifies job designations mentioned in the resume, prioritizing the most recent one.
- **extract\_recommended\_skills(designations):** Suggests 15 relevant skills based on the extracted job designations.
- **extract\_recommended\_job\_position(designations):** Recommends 10 job positions based on the extracted designations.
- **extract\_social(resume\_text):** Extracts LinkedIn and GitHub profile links from the resume text using regex.

### Match Percentage Calculation:

- **extract\_match\_percentage(resume\_text, job\_description):** Uses a weighted scoring system to determine how well a resume matches a job description based on four criteria: skills, experience, education, and keywords.

#### Weighted Function Breakdown:

Match Percentage is computed as:

```
Match Percentage = (Skills Score × 0.40) + (Experience Score × 0.30) + (Education Score × 0.15) + (Keywords Score × 0.15)
```

- **Skills (40%)**: Exact skill matches score highest, related skills score lower.
- **Experience (30%)**: More years of experience get higher scores.
- **Education (15%)**: Full match gets full points; related degrees get partial points.
- **Keywords (15%)**: Important job description keywords are matched against the resume.

This ensures that skills and experience have the most weight, while education and keyword relevance contribute to the final match percentage.

## Resume Analyzer Features

A Streamlit-based resume analysis tool that allows users to upload a PDF resume and extract key information, providing insights into the candidate’s personal details, education, professional experience, skills, strengths, and weaknesses.

### How It Works:

1. **User Uploads a Resume:**

- The user selects the "Resume Analyzer" page and uploads a PDF file.
- If no file is uploaded, a warning message prompts the user.

2. **Resume Processing with a Progress Bar:**

- Progress stages: Text extraction → Analysis → Output generation → Final results.

3. **Extracting Resume Information:**

- The function `extract_resume_information(pdf_file)` processes the file and extracts:
- **Basic Information:** Name, email, contact number, LinkedIn, GitHub, years of experience.
- **Education Details:** Degree name and university.
- **Professional Details:** Previous company, designations, and recommended job roles.
- **Skills:** Extracted and recommended skills.
- **Strengths & Weaknesses:** Key strengths and weaknesses identified from the resume.

### Key Features:

✔️ PDF Upload Support\
✔️ Real-time Progress Updates\
✔️ Automatic Resume Parsing\
✔️ Skill & Job Recommendations\
✔️ Strength & Weakness Analysis

This interactive resume analyzer streamlines resume processing and provides insights for recruiters and job seekers. 🚀

## Job Search Feature

A Streamlit web app feature that allows users to upload a resume (PDF), extract job-related details (role, experience, skills), and search for matching job postings via an API.

### How It Works:

1. **Resume Upload & Extraction:**

- User uploads a PDF resume.
- The function `extract_resume_information_job_search()` extracts job-related details.

2. **User Input Fields:**

- Extracted job role is auto-filled in a text box.
- Users enter preferred location and select employment type (Full-time, Part-time, Intern, Contractor).
- Job postings can be filtered by date posted (Today, Week, Month).
- Skills are extracted from the resume and displayed using `st_tags`.

3. **Job Search via API:**

- Upon submission, an API request is sent to `jobs-api14.p.rapidapi.com`.
- Filters include job role, location, employment type, and date posted.
- The response is parsed, extracting job details (title, company, location, employment type, date posted, job provider).

4. **Displaying Job Listings:**

- Results are shown as interactive job cards using HTML and CSS.
- Each job listing includes a **match percentage** (calculated via `extract_match_percentage()`).
- Users can expand/collapse the job description.
- "Apply Now" button directs users to the job application link.

5. **Error Handling & API Limitations:**

- Displays a message if no jobs are found.
- A 30-second delay (`time.sleep(30)`) prevents API rate limiting.

### Key Features:

✅ Resume parsing for automated job search input\
✅ API-based job retrieval with filtering options\
✅ Match percentage calculation for better job relevance\
✅ Interactive job cards with "Read More" functionality\
✅ Streamlit UI integration for a seamless user experience

This feature simplifies the job search process by leveraging resume insights and real-time job postings. 🚀

## Installation & Usage

### Prerequisites

- Python 3.8+
- Install dependencies using:

```
pip install -r requirements.txt
```

### Running the Application

To start the Streamlit app, run:

```
streamlit run app.py
```

### Screenshot
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