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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from PyPDF2 import PdfFileReader | |
| import requests | |
| from bs4 import BeautifulSoup | |
| # Initialize the Inference Client | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| def extract_text_from_pdf(file): | |
| reader = PdfFileReader(file) | |
| text = "" | |
| for page in range(reader.getNumPages()): | |
| text += reader.getPage(page).extract_text() | |
| return text | |
| def ats_friendly_checker(file): | |
| resume_text = extract_text_from_pdf(file) | |
| # Implement ATS-friendly checker logic using LLM | |
| system_message = "Evaluate the following resume for ATS-friendliness and provide a score and feedback." | |
| message = resume_text | |
| response = client.chat_completion( | |
| [{"role": "system", "content": system_message}, {"role": "user", "content": message}], | |
| max_tokens=512, | |
| temperature=0.7, | |
| top_p=0.95 | |
| ).choices[0].message["content"] | |
| score = response.split("\n")[0].split(":")[-1].strip() | |
| feedback = "\n".join(response.split("\n")[1:]) | |
| return score, feedback | |
| def scrape_job_description(url): | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| job_description = soup.get_text(separator=" ", strip=True) | |
| return job_description | |
| def resume_match_checker(file, job_url): | |
| resume_text = extract_text_from_pdf(file) | |
| job_description = scrape_job_description(job_url) | |
| # Implement resume match checker logic using LLM | |
| system_message = "Compare the following resume with the job description and provide a match score." | |
| message = f"Resume: {resume_text}\n\nJob Description: {job_description}" | |
| response = client.chat_completion( | |
| [{"role": "system", "content": system_message}, {"role": "user", "content": message}], | |
| max_tokens=512, | |
| temperature=0.7, | |
| top_p=0.95 | |
| ).choices[0].message["content"] | |
| match_score = response.split(":")[-1].strip() | |
| return match_score | |
| def resume_quality_score(file): | |
| resume_text = extract_text_from_pdf(file) | |
| # Implement resume quality scoring logic using LLM | |
| system_message = "Evaluate the following resume for overall quality and provide a score." | |
| message = resume_text | |
| response = client.chat_completion( | |
| [{"role": "system", "content": system_message}, {"role": "user", "content": message}], | |
| max_tokens=512, | |
| temperature=0.7, | |
| top_p=0.95 | |
| ).choices[0].message["content"] | |
| quality_score = response.split(":")[-1].strip() | |
| return quality_score | |
| def text_to_overleaf(resume_text): | |
| # Implement the conversion to Overleaf code using LLM | |
| system_message = "Convert the following resume text to Overleaf code." | |
| message = resume_text | |
| response = client.chat_completion( | |
| [{"role": "system", "content": system_message}, {"role": "user", "content": message}], | |
| max_tokens=512, | |
| temperature=0.7, | |
| top_p=0.95 | |
| ).choices[0].message["content"] | |
| overleaf_code = response | |
| return overleaf_code | |
| # Define the Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Resume Enhancement Tool\nEnhance your resume with the following features.") | |
| with gr.Tab("ATS-Friendly Checker"): | |
| with gr.Row(): | |
| resume = gr.File(label="Upload your Resume (PDF)") | |
| score = gr.Number(label="ATS Score", interactive=False) | |
| feedback = gr.Textbox(label="Feedback", interactive=False) | |
| resume.upload(ats_friendly_checker, resume, [score, feedback]) | |
| with gr.Tab("Resume Match Checker"): | |
| with gr.Row(): | |
| resume = gr.File(label="Upload your Resume (PDF)") | |
| job_url = gr.Textbox(label="Job Description URL") | |
| match_score = gr.Number(label="Match Score", interactive=False) | |
| gr.Button("Check Match").click(resume_match_checker, [resume, job_url], match_score) | |
| with gr.Tab("Resume Quality Score"): | |
| with gr.Row(): | |
| resume = gr.File(label="Upload your Resume (PDF)") | |
| quality_score = gr.Number(label="Quality Score", interactive=False) | |
| resume.upload(resume_quality_score, resume, quality_score) | |
| with gr.Tab("Text to Overleaf Code"): | |
| with gr.Row(): | |
| resume_text = gr.Textbox(label="Resume Text") | |
| overleaf_code = gr.Textbox(label="Overleaf Code", interactive=False) | |
| resume_text.submit(text_to_overleaf, resume_text, overleaf_code) | |
| gr.Markdown("---\nBuilt with love by [Bahae Eddine HALIM](https://www.linkedin.com/in/halimbahae/)") | |
| if __name__ == "__main__": | |
| demo.launch() | |