app.py

# -*- coding: utf-8 -*-
import streamlit as st
import os
import pandas as pd
import matplotlib.pyplot as plt
from resume_generation_gemini_pro import generate_gemini
from similarity_score_refined import similarity_main
from pdf2image import convert_from_path


# Helper function to save uploaded files temporarily and return their paths
def save_uploaded_file(uploaded_file):
    file_path = os.path.join("/tmp", uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return file_path

# Custom CSS for styling
st.markdown("""
    <style>
        .main {
            background-color: #f5f5f5;
            font-family: Arial, sans-serif;
        }
        h1, h2 {
            color: #4B7BE5;
            text-align: center;
        }
        .stContainer {
            # background-color: #000000;
            display: flex;
            justify-content: center;
            align-items: center;
            # max-width: 100%;
            height: 30%;
            width: 45%;
        }
        .logo-container {
            # background-color: black;
            display: flex;
            justify-content: center;
            align-items: center;
            padding: 10px;
            # max-width: 100%;
            
        }
        .logo-container img {
            max-width: 60%;
            height: 40%;
        }
        .stButton>button {
            # background-color: #4B7BE5;
            # color: white;
            # font-size: 18px;
            appearance: none;
            background-color: transparent;
            border: 0.125em solid #1A1A1A;
            border-radius: 0.9375em;
            box-sizing: border-box;
            color: #3B3B3B;
            cursor: pointer;
            display: inline-block;
            font-family: Roobert,-apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";
            font-size: 16px;
            font-weight: 600;
            line-height: normal;
            margin: 0;
            min-height: 3.75em;
            min-width: 0;
            outline: none;
            padding: 1em 2.3em;
            text-align: center;
            text-decoration: none;
            transition: all 300ms cubic-bezier(.23, 1, 0.32, 1);
            user-select: none;
            -webkit-user-select: none;
            touch-action: manipulation;
            will-change: transform;
        }
        .stButton>button:hover {
            color: #fff;
            background-color: #1A1A1A;
            box-shadow: rgba(0, 0, 0, 0.25) 0 8px 15px;
            transform: translateY(-2px);
            border: none !important;            
            
        }
        /* From Uiverse.io by e-coders */ 
             
        # .stButton>btn:disabled {
        #  pointer-events: none;
        # }
        
        .stButton>:active {
         box-shadow: none;
         transform: translateY(0);
        }
    </style>
""", unsafe_allow_html=True)

# Add ResumeMagic Logo
# st.image("logo.jpeg", use_container_width=True)
st.markdown('<div class="logo-container"></div>', unsafe_allow_html=True)
st.image("logo.jpeg", width=80)
st.markdown('</div>', unsafe_allow_html=True)


# Title and Description
st.title("Resume Tailoring with Google Generative AI")
st.markdown("### Upload your resume and job description to check similarity and generate a tailored resume.")

# Two columns for file uploaders
col1, col2 = st.columns(2)
with col1:
    uploaded_resume = st.file_uploader("Upload Current Resume (.docx or .pdf)", type=["docx", "pdf"], key="resume")
with col2:
    uploaded_job_description = st.file_uploader("Upload Job Description (.docx or .pdf)", type=["docx", "pdf"], key="job_description")

def get_score(resume_path, job_description_path):
    similarity_score = similarity_main(resume_path, job_description_path)
    if isinstance(similarity_score, str) and '%' in similarity_score:
        similarity_score = float(similarity_score.replace('%', ''))
        
        # Display messages based on score range
        if similarity_score < 50:
            st.markdown('<p style="color: red; font-weight: bold;">Low chance, skills gap identified!</p>', unsafe_allow_html=True)
            pie_colors = ['#FF4B4B', '#E5E5E5']  
        elif 50 <= similarity_score < 70:
            st.markdown('<p style="color: red; font-weight: bold;">Good chance but you can improve further!</p>', unsafe_allow_html=True)
            pie_colors = ['#FFC107', '#E5E5E5']  
        else:
            st.markdown('<p style="color: green; font-weight: bold;">Excellent! You can submit your CV.</p>', unsafe_allow_html=True)
            pie_colors = ['#4CAF50', '#E5E5E5']  
    
    return similarity_score, pie_colors

def display_score(similarity, colors):
    # Display Score as a Pie Chart
    st.markdown(f"### Resume - Job Match: {int(similarity_score)}%")
    
    # Pie chart to show similarity
    fig, ax = plt.subplots()
    # ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=['#4B7BE5', '#E5E5E5'])
    ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=pie_colors)
        
    ax.axis('equal')  
    st.pyplot(fig)

def save_docx_as_pdf(doc_content, output_path='output.pdf'):
    # Save document content as a .docx file
    temp_doc_path = 'temp.docx'
    doc = Document()
    doc.add_paragraph(doc_content)
    doc.save(temp_doc_path)
    
    # Convert .docx to PDF
    from docx2pdf import convert
    convert(temp_doc_path, output_path)
    os.remove(temp_doc_path)
    
def display_doc_as_image(pdf_path):
    poppler_path = 'usr/bin'
    images = convert_from_path(pdf_path, poppler_path=poppler_path)
    for img in images:
        buf = BytesIO()
        img.save(buf, format="PNG")
        st.image(buf)

# Process if files are uploaded
if uploaded_resume and uploaded_job_description:
    # Save files
    resume_path = save_uploaded_file(uploaded_resume)
    job_description_path = save_uploaded_file(uploaded_job_description)

    # Similarity Score Section
    st.markdown("---")
    # st.subheader("Check Job Match")

    if st.button("Resume-JD Matching"):
        with st.spinner("Computing Match"):
            similarity_score, pie_colors = get_score(resume_path, job_description_path)
            display_score(similarity_score, pie_colors)

        
        #Autoscroll
        st.markdown("""
            <script>
                window.scrollTo(0, document.body.scrollHeight);
            </script>
        """, unsafe_allow_html=True)

    # Generate Tailored Resume Section
    st.markdown("---")
    # st.subheader("Tailor Resume")

    if st.button("Tailor Resume"):
        with st.spinner("Generating resume..."):
            generated_resume, new_resume_path = generate_gemini(resume_path, job_description_path)
            # st.markdown("Generated Tailored Resume:")
            # st.write(generated_resume)

            #Autoscroll
            st.markdown("""
                <script>
                    window.scrollTo(0, document.body.scrollHeight);
                </script>
            """, unsafe_allow_html=True)

            col1, col2 = st.columns(2)
            with col1: 
                st.markdown("### Uploaded Resume:")
                # if resume_path.endswith('.docx'):
                #     save_docx_as_pdf(uploaded_resume.getvalue().decode('utf-8'), 'uploaded_resume.pdf')
                if uploaded_resume.type == "application/pdf":
                    display_doc_as_image(resume_path)
                else:
                    save_docx_as_pdf(resume_path, 'uploaded_resume.pdf')
                    display_doc_as_image('uploaded_resume.pdf')
                
                
            with col2:
                st.markdown("### Tailored Resume:")
                save_docx_as_pdf(generated_resume, 'tailored_resume.pdf')
                display_doc_as_image('tailored_resume.pdf')
            
            with st.spinner("Computing Match"):
                similarity_score, pie_colors = get_score(resume_path, job_description_path)
                display_score(similarity_score, pie_colors)
                
            if generated_resume is not None:
                from io import BytesIO
                from docx import Document
                
                doc = Document()
                doc.add_paragraph(generated_resume)
                
                resume_bytes = BytesIO()
                doc.save(resume_bytes)
                resume_bytes.seek(0)
                
                st.download_button(
                    label="Download Resume",
                    data=resume_bytes,
                    file_name="tailored_resume.docx",
                    mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
                )
                
else:
    st.warning("Please upload both the resume and job description files.")