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 parse_score(score_text):
    try:
        return float(score_text.strip('%').strip()) / 100
    except ValueError:
        return None

def ats_friendly_checker(file):
    resume_text = extract_text_from_pdf(file)
    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_text = response.split("\n")[0].split(":")[-1].strip()
    feedback = "\n".join(response.split("\n")[1:])
    score = parse_score(score_text)
    if score is not None:
        score *= 100  # Convert to percentage
    else:
        score = 0
        feedback = "Error parsing score from the response."
    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)
    system_message = "Compare the following resume with the job description and provide a match score and 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

    match_score_text = response.split(":")[-1].strip()
    feedback = "\n".join(response.split("\n")[1:])
    match_score = parse_score(match_score_text)
    if match_score is not None:
        match_score *= 100  # Convert to percentage
    else:
        match_score = 0
        feedback = "Error parsing match score from the response."
    return match_score, feedback

def resume_quality_score(file):
    resume_text = extract_text_from_pdf(file)
    system_message = "Evaluate the following resume for overall quality and provide a score and interpretation."
    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_lines = response.split("\n")
    quality_score_text = score_lines[0].split(":")[-1].strip()
    interpretation = "\n".join(score_lines[1:])
    quality_score = parse_score(quality_score_text)
    if quality_score is not None:
        quality_score *= 100  # Convert to percentage
    else:
        quality_score = 0
        interpretation = "Error parsing quality score from the response."
    return quality_score, 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\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)
            feedback = gr.Textbox(label="Feedback", interactive=False)
        gr.Button("Check Match").click(resume_match_checker, [resume, job_url], [match_score, feedback])
    
    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)
            interpretation = gr.Textbox(label="Interpretation", interactive=False)
        resume.upload(resume_quality_score, resume, [quality_score, interpretation])
    
    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(share=True)