import streamlit as st
from docx import Document
import pdfplumber
import re
from collections import Counter

# Custom CSS for UI enhancements
def apply_custom_css():
    st.markdown("""
        <style>
            .title { 
                font-size: 2.5rem; 
                color: #4CAF50; 
                text-align: center; 
                margin-bottom: 20px; 
            }
            .subtitle { 
                font-size: 1.5rem; 
                color: #FF5722; 
                margin-top: 20px; 
                margin-bottom: 10px; 
            }
            .metric { 
                font-size: 1.2rem; 
                text-align: center; 
            }
            .separator { 
                border-top: 2px solid #eee; 
                margin-top: 30px; 
                margin-bottom: 30px; 
            }
            .suggestions { 
                background-color: #f9f9f9; 
                border-left: 4px solid #2196F3; 
                padding: 10px; 
                margin: 10px 0; 
            }
        </style>
    """, unsafe_allow_html=True)

# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
    with pdfplumber.open(pdf_file) as pdf:
        text = ""
        for page in pdf.pages:
            text += page.extract_text() + "\n"
    return text

# Function to extract text from Word Document
def extract_text_from_docx(docx_file):
    doc = Document(docx_file)
    text = ""
    for paragraph in doc.paragraphs:
        text += paragraph.text + "\n"
    return text

# Function to check ATS-friendliness
def check_ats_friendly(text):
    score = 100
    suggestions = []

    # Keywords check
    keywords = ["experience", "skills", "education", "certification", "achievements"]
    for keyword in keywords:
        if keyword not in text.lower():
            score -= 10
            suggestions.append(f"Add more emphasis on '{keyword}' in your resume.")

    # Formatting check
    if re.search(r"[\t]", text):  # Tabs are not ATS-friendly
        score -= 10
        suggestions.append("Avoid using tabs for formatting; use spaces or standard bullet points.")

    # Font styles check
    if re.search(r"[{}<>]", text):  # Symbols like curly braces and angled brackets can confuse ATS
        score -= 10
        suggestions.append("Remove any special symbols like {}, <>, etc., which might confuse ATS.")

    # Contact info check
    if not re.search(r"\b\d{10}\b", text):  # Check for a 10-digit phone number
        score -= 10
        suggestions.append("Ensure your resume includes a valid phone number.")
    if not re.search(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", text):  # Check for an email
        score -= 10
        suggestions.append("Include a valid professional email address.")

    # Suggestion for simplicity
    if len(text.split()) > 1000:  # Resume too long
        score -= 10
        suggestions.append("Try to keep your resume concise and limit it to one or two pages.")

    return score, suggestions

# Function to calculate job fit score and recommend missing keywords
def calculate_job_fit_score(resume_text, job_description):
    resume_words = Counter(resume_text.lower().split())
    job_words = Counter(job_description.lower().split())

    # Find overlap between resume and job description keywords
    common_words = resume_words & job_words
    total_job_words = sum(job_words.values())
    missing_keywords = set(job_words.keys()) - set(resume_words.keys())

    # Calculate match percentage
    if total_job_words == 0:
        match_percentage = 0
    else:
        match_percentage = sum(common_words.values()) / total_job_words * 100

    suggestions = []
    if match_percentage < 100:
        suggestions.append(
            "Consider including the missing keywords in your resume to improve matching."
        )

    return match_percentage, suggestions, missing_keywords

# Main App
def main():
    apply_custom_css()

    st.markdown("<div class='title'>ATS Resume Checker</div>", unsafe_allow_html=True)
    st.write(
        "Upload your resume to check its ATS-friendliness and compare it with a job description for keyword matching."
    )

    st.markdown("<hr class='separator'>", unsafe_allow_html=True)

    uploaded_resume = st.file_uploader(
        "📂 Upload your Resume (PDF or Word):", type=["pdf", "docx"]
    )
    job_description = st.text_area("📋 Paste the Job Description:")

    if uploaded_resume:
        # Extract text based on file type
        if uploaded_resume.name.endswith(".pdf"):
            resume_text = extract_text_from_pdf(uploaded_resume)
        elif uploaded_resume.name.endswith(".docx"):
            resume_text = extract_text_from_docx(uploaded_resume)
        else:
            st.error("Unsupported file format. Please upload a PDF or Word document.")
            return

        # Analyze ATS-friendliness
        st.markdown("<div class='subtitle'>🛠️ ATS Analysis</div>", unsafe_allow_html=True)
        ats_score, ats_suggestions = check_ats_friendly(resume_text)
        st.metric("ATS Score", f"{ats_score}/100")
        if ats_score >= 80:
            st.success("✅ Your resume is ATS-friendly!")
        else:
            st.warning("⚠️ Your resume needs improvement to be ATS-friendly.")

        # Display ATS suggestions
        st.markdown("<div class='subtitle'>📝 Suggestions for ATS Improvement</div>", unsafe_allow_html=True)
        if ats_suggestions:
            for idx, suggestion in enumerate(ats_suggestions, 1):
                st.markdown(f"<div class='suggestions'>{idx}. {suggestion}</div>", unsafe_allow_html=True)
        else:
            st.write("Your resume is well-optimized for ATS!")

        st.markdown("<hr class='separator'>", unsafe_allow_html=True)

        # Analyze Job Fit
        if job_description.strip():
            st.markdown("<div class='subtitle'>🔍 Job Description Matching</div>", unsafe_allow_html=True)
            job_fit_score, job_fit_suggestions, missing_keywords = calculate_job_fit_score(
                resume_text, job_description
            )
            st.metric("Job Fit Score", f"{job_fit_score:.2f}%")
            if job_fit_score >= 70:
                st.success("✅ Your resume matches well with the job description!")
            else:
                st.warning("⚠️ Your resume could be tailored better to match the job description.")

            # Display job fit suggestions
            st.markdown("<div class='subtitle'>🔑 Missing Keywords</div>", unsafe_allow_html=True)
            if missing_keywords:
                st.write("To improve matching, consider including these keywords:")
                st.markdown(f"<div class='suggestions'>{', '.join(missing_keywords)}</div>", unsafe_allow_html=True)
            else:
                st.write("Your resume already includes all the required keywords!")

if __name__ == "__main__":
    main()