app.py

import gradio as gr
from huggingface_hub import InferenceClient
from PyPDF2 import PdfReader
import requests
from bs4 import BeautifulSoup

# Initialize the Inference Client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def extract_text_from_pdf(file):
    if file is None:
        return ""
    reader = PdfReader(file)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    return text

def ats_friendly_checker(file):
    resume_text = extract_text_from_pdf(file)
    system_message = "Evaluate the following resume for ATS-friendliness and provide 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
    
    feedback = response
    return feedback

def scrape_job_description(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        job_description = soup.get_text(separator=" ", strip=True)
        return job_description
    except requests.exceptions.RequestException as e:
        return f"Error accessing job description: {e}"

def resume_match_checker(file, job_url):
    resume_text = extract_text_from_pdf(file)
    job_description = scrape_job_description(job_url)
    
    if job_description.startswith("Error accessing job description:"):
        return job_description
    
    system_message = "Compare the following resume with the job description and provide feedback."
    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

    feedback = response
    return feedback

def resume_quality_score(file):
    resume_text = extract_text_from_pdf(file)
    system_message = "Evaluate the following resume for overall quality and provide 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

    interpretation = response
    return interpretation

def text_to_overleaf(resume_text):
    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 🚀
    
    Welcome to the Resume Enhancement Tool! This tool offers several features to help you enhance your resume, ensuring it's ATS-friendly, matches job descriptions, and is of high quality. You can also convert your resume text to Overleaf code for a polished, professional look.
    """)
    
    with gr.Tab("ATS-Friendly Checker 📝"):
        with gr.Row():
            resume = gr.File(label="Upload your Resume (PDF)")
            feedback = gr.Textbox(label="Feedback", interactive=False, lines=15, max_lines=50)
        resume.upload(ats_friendly_checker, resume, 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")
            feedback = gr.Textbox(label="Feedback", interactive=False, lines=15, max_lines=50)
        gr.Button("Check Match").click(resume_match_checker, [resume, job_url], feedback)
    
    with gr.Tab("Resume Quality Score 🌟"):
        with gr.Row():
            resume = gr.File(label="Upload your Resume (PDF)")
            interpretation = gr.Textbox(label="Interpretation", interactive=False, lines=15, max_lines=50)
        resume.upload(resume_quality_score, resume, interpretation)
    
    with gr.Tab("Text to Overleaf Code 🖋️"):
        with gr.Row():
            resume_text = gr.Textbox(label="Resume Text", placeholder="Enter your resume text here or paste the example below:\n\nJohn Doe\n\nExperience\n- Software Engineer at Company A\n- Data Scientist at Company B\n\nEducation\n- BSc in Computer Science")
            overleaf_code = gr.Textbox(label="Overleaf Code", interactive=False, lines=15, max_lines=50)
        resume_text.submit(text_to_overleaf, resume_text, overleaf_code)
        gr.Markdown("Paste the generated code into [Overleaf](https://www.overleaf.com/) to create your resume.")

    gr.Markdown("---\nBuilt with love by [Bahae Eddine HALIM](https://www.linkedin.com/in/halimbahae/)")

if __name__ == "__main__":
    demo.launch(share=True)