main.py

'''
-----------------------------------------------------------------------
File: app.py
Creation Time: Jan 30th 2024, 11:00 am
Author: Saurabh Zinjad
Developer Email: saurabhzinjad@gmail.com
Copyright (c) 2023-2024 Saurabh Zinjad. All rights reserved | https://github.com/Ztrimus
-----------------------------------------------------------------------
'''
import os
import json
import shutil
import streamlit as st


from zlm import AutoApplyModel
from zlm.utils.utils import display_pdf, download_pdf, read_file, read_json
from zlm.utils.metrics import jaccard_similarity, overlap_coefficient, cosine_similarity

st.set_page_config(
    page_title="Resume Generator",
    page_icon="📑",
    menu_items={
        'Get help': 'https://www.youtube.com/watch?v=Agl7ugyu1N4',
        'About': 'https://github.com/Ztrimus/job-llm',
        'Report a bug': "https://github.com/Ztrimus/job-llm/issues",
    }
)

try:
    # st.markdown("<h1 style='text-align: center; color: grey;'>Get :green[Job Aligned] :orange[Killer] Resume :sunglasses:</h1>", unsafe_allow_html=True)
    st.header("Get :green[Job Aligned] :orange[Personalized] Resume", divider='rainbow')
    # st.subheader("Skip the writing, land the interview")

    col_text, col_url,_,_ = st.columns(4)
    with col_text:
        st.write("Job Description Text")
    with col_url:
        is_url_button = st.toggle('Job URL', False)

    url, text = "", ""
    if is_url_button:
        url = st.text_input("Enter job posting URL:", placeholder="Enter job posting URL here...", label_visibility="collapsed")
    else:
        text = st.text_area("Paste job description text:", max_chars=5500, height=200, placeholder="Paste job description text here...", label_visibility="collapsed")

    file = st.file_uploader("Upload your resume or any work-related data(PDF, JSON). [Recommended templates](https://github.com/Ztrimus/job-llm/tree/main/zlm/demo_data)", type=["json", "pdf"])

    col_1, col_2 = st.columns(2)
    with col_1:
        provider = st.selectbox("Select LLM provider([OpenAI](https://openai.com/blog/openai-api), [Gemini Pro](https://ai.google.dev/)):", ["gemini-pro", "gpt-4"])
    with col_2:
        api_key = st.text_input("Enter API key:", type="password")
    st.markdown("<sub><sup>💡 GPT-4 is recommended for better results.</sup></sub>", unsafe_allow_html=True)

    # Buttons side-by-side with styling
    col1, col2, col3 = st.columns(3)
    with col1:
        get_resume_button = st.button("Get Resume", key="get_resume", type="primary", use_container_width=True)

    with col2:
        get_cover_letter_button = st.button("Get Cover Letter", key="get_cover_letter", type="primary", use_container_width=True)

    with col3:
        get_both = st.button("Resume + Cover letter", key="both", type="primary", use_container_width=True)
        if get_both:
            get_resume_button = True
            get_cover_letter_button = True

    if get_resume_button or get_cover_letter_button:
        if file is None:
            st.toast(":red[Upload user's resume or work related data to get started]", icon="⚠️")
            st.stop()
        
        if url == "" and text == "":
            st.toast(":red[Please enter a job posting URL or paste the job description to get started]", icon="⚠️") 
            st.stop()
        
        if api_key == "":
            st.toast(":red[Please enter the API key to get started]", icon="⚠️")
            st.stop()
        
        if file is not None and (url != "" or text != ""):
            download_resume_path = os.path.join(os.path.dirname(__file__), "output")

            # st.write(f"download_resume_path: {download_resume_path}")

            llm_mapping = {'gpt-4':'openai', 'gemini-pro':'gemini'}

            resume_llm = AutoApplyModel(api_key=api_key, provider=llm_mapping[provider], downloads_dir=download_resume_path)
            
            # Save the uploaded file
            os.makedirs("uploads", exist_ok=True)
            file_path = os.path.abspath(os.path.join("uploads", file.name))
            with open(file_path, "wb") as f:
                f.write(file.getbuffer())
        
            # Extract user data
            with st.status("Extracting user data..."):
                user_data = user_data = {
  "name": "Shah Aakash R",
  "summary": "WEB3 Enthusiast\nFreelancing at Infinity Linkage, I\ndelivered tailored solutions, collaborated\nremotely, honed skills in diverse\nprojects, and ensured client satisfaction\nconsistently.",
  "phone": "+91-8866172310",
  "email": "hello@smilechain.app",
  "media": {
    "linkedin": "None",
    "github": "None",
    "devpost": "None",
    "medium": "None",
    "leetcode": "None",
    "dagshub": "None",
    "kaggle": "None",
    "instagram": "None"
  },
  "education": [
    {
      "degree": "Master Of Computer Application",
      "university": "Dharmsinh Desai university, Nadiad",
      "from": "2023",
      "to": "Present"
    },
    {
      "degree": "Bachelor Of Computer Application",
      "university": "Dharmsinh Desai university, Nadiad",
      "from": "2020",
      "to": "2023"
    }
  ],
  "skill_section": [
    {
      "name": "None",
      "skills": [
        "React Native",
        "NodeJS / ReactJS",
        "Python / PHP",
        "Blockchain",
        "Github / Gitlab",
        "C / C# / C++ / JAVA",
        "Solidity",
        "SQL / MongoDB",
        "Postman / Swagger"
      ]
    }
  ],
  "work_experience": [
    {
      "role": "Freelancer",
      "company": "Infinity Linkage",
      "from": "2022",
      "to": "Present",
      "description": []
    },
    {
      "role": "Intern",
      "company": "Digiflux Technologies",
      "from": "Dec 2022",
      "to": "March 2023",
      "description": [
        "Working at Digiflux was an enriching\nexperience, delving into administrative\ntasks, coordinating schedules, aiding\nteamwork, and fostering efficient office\noperations."
      ]
    },
    {
      "role": "Owner",
      "company": "Bloginezone.com",
      "from": "2017",
      "to": "2018",
      "description": [
        "Crafted 'Bloginezone,' my inaugural\nWordPress blog, showcasing my adeptness in\nwebsite creation, content curation, and\ninitiating online presence."
      ]
    }
  ],
  "projects": [
    {
      "name": "Decentralized Asset Management",
      "description": "Developed a decentralized application for managing digital assets on Ethereum blockchain. Implemented smart contracts and integrated with web interfaces.",
      "technologies": ["Ethereum", "Solidity", "React"],
      "from": "2021",
      "to": "2022"
    },
    {
      "name": "Automated Trading System",
      "description": "Created an automated trading system for cryptocurrencies which uses machine learning to optimize trading strategies.",
      "technologies": ["Python", "Machine Learning", "Blockchain"],
      "from": "2020",
      "to": "2021"
    }
  ],
  "certifications": [
    {
      "title": "Certified Blockchain Developer",
      "issuer": "Blockchain Council",
      "year": "2022"
    },
    {
      "title": "React Native Mobile Developer Certification",
      "issuer": "Udemy",
      "year": "2021"
    }
  ],
  "achievements": [
    "Polygon Guild 2022 - Online Blockchain Developer Internship at Polygon, partnered with IIT MADRAS."
  ]
}
                st.write(user_data)

            shutil.rmtree(os.path.dirname(file_path))

            if user_data is None:
                st.error("User data not able process. Please upload a valid file")
                st.markdown("<h3 style='text-align: center;'>Please try again</h3>", unsafe_allow_html=True)
                st.stop()

            # Extract job details
            with st.status("Extracting job details..."):
                if url != "":
                    job_details, jd_path = resume_llm.job_details_extraction(url=url, is_st=True)
                elif text != "":
                    job_details, jd_path = resume_llm.job_details_extraction(job_site_content=text, is_st=True)
                st.write(job_details)

            if job_details is None:
                st.error("Please paste job description. Job details not able process.")
                st.markdown("<h3 style='text-align: center;'>Please paste job description text and try again!</h3>", unsafe_allow_html=True)
                st.stop()

            # Build Resume
            if get_resume_button:
                with st.status("Building resume..."):
                    resume_path, resume_details = resume_llm.resume_builder(job_details, user_data, is_st=True)
                    # st.write("Outer resume_path: ", resume_path)
                    # st.write("Outer resume_details is None: ", resume_details is None)
                resume_col_1, resume_col_2 = st.columns([0.7, 0.3])
                with resume_col_1:
                    st.subheader("Generated Resume")
                with resume_col_2:
                    pdf_data = read_file(resume_path, "rb")

                    st.download_button(label="Download Resume ⬇",
                                        data=pdf_data,
                                        file_name=os.path.basename(resume_path),
                                        # on_click=download_pdf(resume_path),
                                        key="download_pdf_button",
                                        mime="application/pdf",
                                        use_container_width=True)
                
                display_pdf(resume_path, type="image")
                st.toast("Resume generated successfully!", icon="✅")
                # Calculate metrics
                st.subheader("Resume Metrics")
                for metric in ['overlap_coefficient', 'cosine_similarity']:
                    user_personlization = globals()[metric](json.dumps(resume_details), json.dumps(user_data))
                    job_alignment = globals()[metric](json.dumps(resume_details), json.dumps(job_details))
                    job_match = globals()[metric](json.dumps(user_data), json.dumps(job_details))

                    if metric == "overlap_coefficient":
                        title = "Overlap Coefficient"
                        help_text = "The overlap coefficient is a measure of the overlap between two sets, and is defined as the size of the intersection divided by the smaller of the size of the two sets."
                    elif metric == "cosine_similarity":
                        title = "Cosine Similarity"
                        help_text = "The cosine similarity is a measure of the similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them."

                    st.caption(f"## **:rainbow[{title}]**", help=help_text)
                    col_m_1, col_m_2, col_m_3 = st.columns(3)
                    col_m_1.metric(label=":green[User Personlization Score]", value=f"{user_personlization:.3f}", delta="[resume,master_data]", delta_color="off")
                    col_m_2.metric(label=":blue[Job Alignment Score]", value=f"{job_alignment:.3f}", delta="[resume,JD]", delta_color="off")
                    col_m_3.metric(label=":violet[Job Match Score]", value=f"{job_match:.3f}", delta="[master_data,JD]", delta_color="off")
                st.markdown("---")

            # Build Cover Letter
            if get_cover_letter_button:
                with st.status("Building cover letter..."):
                    cv_details, cv_path = resume_llm.cover_letter_generator(job_details, user_data, is_st=True)
                cv_col_1, cv_col_2 = st.columns([0.7, 0.3])
                with cv_col_1:
                    st.subheader("Generated Cover Letter")
                with cv_col_2:
                    cv_data = read_file(cv_path, "rb")
                    st.download_button(label="Download CV ⬇",
                                    data=cv_data,
                                    file_name=os.path.basename(cv_path),
                                    # on_click=download_pdf(cv_path),
                                    key="download_cv_button",
                                    mime="application/pdf", 
                                    use_container_width=True)
                st.markdown(cv_details, unsafe_allow_html=True)
                st.markdown("---")
                st.toast("cover letter generated successfully!", icon="✅")
            
            st.toast(f"Done", icon="👍🏻")
            st.success(f"Done", icon="👍🏻")
            st.balloons()
            
            refresh = st.button("Refresh")

            if refresh:
                st.caching.clear_cache()
                st.rerun()
        
except Exception as e:
    st.error(f"An error occurred: {e}")
    st.markdown("<h3 style='text-align: center;'>Please try again!</h3>", unsafe_allow_html=True)
    st.stop()

st.link_button("Report Feedback, Issues, or Contribute!", "https://github.com/Ztrimus/job-llm/issues", use_container_width=True)