"""
app.py — AI Resume ATS Analyzer
================================
 
Main Streamlit application entry point.
 
Features:
  - Upload PDF or DOCX resume
  - Automatic text extraction
  - Named entity recognition (name, orgs, locations)
  - Skills extraction (technical + soft)
  - Section detection (Skills, Experience, Projects, Education, Summary)
  - Resume base score (0–100) with breakdown
  - Job description semantic similarity via Sentence-BERT
  - ATS Score = 0.6 × base_score + 0.4 × job_match
  - Classification: Good / Average / Poor
  - Actionable suggestions
  - AI-powered resume rewrite via FLAN-T5
  - Manual resume editing + re-evaluation
 
Run locally:
    streamlit run app.py
 
Deploy on Hugging Face Spaces:
    1. Create a new Space (SDK: Streamlit)
    2. Upload all project files
    3. The Space will install requirements.txt and launch automatically
"""
 
import streamlit as st
import pandas as pd
import time
 
# Local utility modules
from utils.parser import parse_resume
from utils.nlp_utils import (
    extract_entities,
    detect_sections,
    extract_skills,
    get_missing_sections,
    classify_resume,
    generate_suggestions,
)
from utils.scorer import compute_base_score, compute_ats_score
from utils.similarity import compute_similarity, keyword_overlap_score
from utils.generator import generate_improved_resume, generate_professional_summary
 
# ---------------------------------------------------------------------------
# Page configuration
# ---------------------------------------------------------------------------
st.set_page_config(
    page_title="AI Resume ATS Analyzer",
    page_icon="📄",
    layout="wide",
    initial_sidebar_state="expanded",
)
 
# ---------------------------------------------------------------------------
# Custom CSS — clean, professional look
# ---------------------------------------------------------------------------
st.markdown(
    """
    <style>
        /* Main header */
        .main-header {
            font-size: 2.4rem;
            font-weight: 800;
            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
            -webkit-background-clip: text;
            -webkit-text-fill-color: transparent;
            margin-bottom: 0.2rem;
        }
        /* Score cards */
        .score-card {
            background: #f8f9fa;
            border-radius: 12px;
            padding: 1.2rem;
            text-align: center;
            border: 1px solid #e9ecef;
            box-shadow: 0 2px 6px rgba(0,0,0,0.06);
        }
        .score-number {
            font-size: 2.2rem;
            font-weight: 700;
            margin: 0;
        }
        .score-label {
            font-size: 0.85rem;
            color: #6c757d;
            text-transform: uppercase;
            letter-spacing: 0.05em;
        }
        /* Section headers */
        .section-header {
            font-size: 1.1rem;
            font-weight: 600;
            color: #343a40;
            border-bottom: 2px solid #667eea;
            padding-bottom: 0.3rem;
            margin-top: 1.5rem;
            margin-bottom: 0.8rem;
        }
        /* Skill badges */
        .skill-badge {
            display: inline-block;
            background: #e8f4fd;
            color: #1a6a9a;
            border-radius: 20px;
            padding: 0.2rem 0.7rem;
            margin: 0.15rem;
            font-size: 0.82rem;
            font-weight: 500;
        }
        /* Suggestion items */
        .suggestion-item {
            background: #fffbf0;
            border-left: 4px solid #ffc107;
            padding: 0.6rem 0.9rem;
            border-radius: 0 6px 6px 0;
            margin-bottom: 0.5rem;
            font-size: 0.9rem;
        }
        /* Good/Average/Poor banner */
        .classification-good {
            background: #d4edda; color: #155724;
            padding: 0.5rem 1rem; border-radius: 8px;
            font-size: 1.1rem; font-weight: 600;
            text-align: center;
        }
        .classification-average {
            background: #fff3cd; color: #856404;
            padding: 0.5rem 1rem; border-radius: 8px;
            font-size: 1.1rem; font-weight: 600;
            text-align: center;
        }
        .classification-poor {
            background: #f8d7da; color: #721c24;
            padding: 0.5rem 1rem; border-radius: 8px;
            font-size: 1.1rem; font-weight: 600;
            text-align: center;
        }
        /* Info boxes */
        .stAlert { border-radius: 8px; }
    </style>
    """,
    unsafe_allow_html=True,
)
 
# ---------------------------------------------------------------------------
# Sidebar — About & Instructions
# ---------------------------------------------------------------------------
with st.sidebar:
    st.image(
        "https://img.icons8.com/color/96/resume.png",
        width=70,
    )
    st.markdown("## 📄 AI Resume Analyzer")
    st.markdown(
        """
        **How to use:**
        1. Upload your resume (PDF or DOCX)
        2. Paste the job description
        3. Click **Analyze Resume**
        4. Review your scores & suggestions
        5. Use **Generate Improved Resume** for AI rewrite
        6. Edit & **Re-evaluate** anytime
 
        ---
        **Scoring Formula:**
        - Resume Score: up to 100 pts
          - Skills (20) + Experience (30)
          - Projects (20) + Education (10)
          - Length (10) + Diversity (10)
        - ATS Score = 0.6 × Resume Score
                    + 0.4 × Job Match %
 
        ---
        **Powered by:**
        - 🤗 FLAN-T5 (text generation)
        - 🔤 Sentence-BERT (similarity)
        - 🧠 spaCy (NER)
        - 📄 PyMuPDF + python-docx
        """
    )
    st.markdown("---")
    st.caption("Built with ❤️ using open-source AI")
 
 
# ---------------------------------------------------------------------------
# Main UI — Header
# ---------------------------------------------------------------------------
st.markdown('<p class="main-header">🎯 AI Resume ATS Analyzer</p>', unsafe_allow_html=True)
st.markdown(
    "Upload your resume and a job description to get an **AI-powered ATS score**, "
    "skill analysis, and personalized improvement suggestions.",
    unsafe_allow_html=True,
)
st.markdown("---")
 
# ---------------------------------------------------------------------------
# Input Section — two columns
# ---------------------------------------------------------------------------
col_upload, col_jd = st.columns([1, 1], gap="large")
 
with col_upload:
    st.markdown("### 📤 Upload Resume")
    uploaded_file = st.file_uploader(
        "Supported formats: PDF, DOCX",
        type=["pdf", "docx"],
        help="Upload your resume in PDF or Word format",
    )
 
with col_jd:
    st.markdown("### 📋 Job Description")
    job_description = st.text_area(
        "Paste the job description here",
        height=200,
        placeholder=(
            "e.g., We are looking for a Python Developer with experience in "
            "FastAPI, PostgreSQL, and Docker. You will work on backend systems, "
            "REST APIs, and cloud deployments on AWS..."
        ),
        help="The job description is used for semantic matching and tailored suggestions.",
    )
 
# ---------------------------------------------------------------------------
# Session state — persist results across reruns
# ---------------------------------------------------------------------------
if "results" not in st.session_state:
    st.session_state.results = None
if "resume_text_editable" not in st.session_state:
    st.session_state.resume_text_editable = ""
if "improved_resume" not in st.session_state:
    st.session_state.improved_resume = ""
 
 
# ---------------------------------------------------------------------------
# Helper: run the full analysis pipeline
# ---------------------------------------------------------------------------
def run_analysis(resume_text: str, job_desc: str) -> dict:
    """
    Execute the complete resume analysis pipeline.
 
    Args:
        resume_text: extracted or edited resume text
        job_desc   : job description text
 
    Returns:
        Dictionary with all analysis results.
    """
    # 1. NLP Analysis
    with st.spinner("🔍 Running NLP analysis..."):
        entities = extract_entities(resume_text)
        sections = detect_sections(resume_text)
        skills = extract_skills(resume_text)
        missing_sections = get_missing_sections(sections)
 
    # 2. Scoring
    with st.spinner("📊 Computing resume score..."):
        score_result = compute_base_score(resume_text, sections, skills)
 
    # 3. Job Description Similarity
    with st.spinner("🎯 Matching against job description..."):
        if job_desc.strip():
            job_match = compute_similarity(resume_text, job_desc)
            kw_overlap = keyword_overlap_score(resume_text, job_desc)
        else:
            job_match = 0.0
            kw_overlap = {"overlap_pct": 0.0, "matched": [], "missing": []}
 
    # 4. ATS Score + Classification
    ats_score = compute_ats_score(score_result["total"], job_match)
    classification = classify_resume(ats_score)
 
    # 5. Suggestions
    suggestions = generate_suggestions(
        sections, skills, score_result["total"], job_match
    )
 
    return {
        "entities": entities,
        "sections": sections,
        "skills": skills,
        "missing_sections": missing_sections,
        "base_score": score_result["total"],
        "score_breakdown": score_result["breakdown"],
        "job_match": job_match,
        "kw_overlap": kw_overlap,
        "ats_score": ats_score,
        "classification": classification,
        "suggestions": suggestions,
    }
 
 
# ---------------------------------------------------------------------------
# Helper: render analysis results
# ---------------------------------------------------------------------------
def render_results(results: dict, resume_text: str):
    """Render the full analysis results in the Streamlit UI."""
 
    st.markdown("---")
    st.markdown("## 📊 Analysis Results")
 
    # ── Score Dashboard ────────────────────────────────────────────────────
    c1, c2, c3, c4 = st.columns(4)
 
    def _color_score(score, thresholds=(70, 45)):
        high, mid = thresholds
        if score >= high:
            return "#28a745"
        elif score >= mid:
            return "#ffc107"
        return "#dc3545"
 
    with c1:
        color = _color_score(results["base_score"])
        st.markdown(
            f"""<div class="score-card">
                <p class="score-number" style="color:{color}">{results["base_score"]}</p>
                <p class="score-label">Resume Score</p>
            </div>""",
            unsafe_allow_html=True,
        )
    with c2:
        color = _color_score(results["job_match"])
        st.markdown(
            f"""<div class="score-card">
                <p class="score-number" style="color:{color}">{results["job_match"]}%</p>
                <p class="score-label">Job Match</p>
            </div>""",
            unsafe_allow_html=True,
        )
    with c3:
        color = _color_score(results["ats_score"])
        st.markdown(
            f"""<div class="score-card">
                <p class="score-number" style="color:{color}">{results["ats_score"]}%</p>
                <p class="score-label">ATS Score</p>
            </div>""",
            unsafe_allow_html=True,
        )
    with c4:
        label = results["classification"]["label"]
        cls_color = results["classification"]["color"]
        css_class = f"classification-{cls_color}"
        st.markdown(
            f"""<div class="score-card">
                <div class="{css_class}" style="margin-top:0.5rem">{label}</div>
                <p class="score-label" style="margin-top:0.5rem">Classification</p>
            </div>""",
            unsafe_allow_html=True,
        )
 
    st.markdown("<br>", unsafe_allow_html=True)
 
    # ── Score Breakdown Bar Chart ──────────────────────────────────────────
    with st.expander("📈 Score Breakdown", expanded=True):
        breakdown = results["score_breakdown"]
        max_vals = {
            "Skills": 20,
            "Experience": 30,
            "Projects": 20,
            "Education": 10,
            "Length": 10,
            "Diversity": 10,
        }
        df = pd.DataFrame(
            {
                "Category": list(breakdown.keys()),
                "Your Score": list(breakdown.values()),
                "Max Score": [max_vals.get(k, 10) for k in breakdown.keys()],
            }
        )
        st.bar_chart(df.set_index("Category")[["Your Score", "Max Score"]])
 
    # ── Two column layout for details ─────────────────────────────────────
    left_col, right_col = st.columns([1, 1], gap="large")
 
    with left_col:
        # ── Detected Information ──────────────────────────────────────────
        st.markdown('<p class="section-header">🔍 Detected Information</p>', unsafe_allow_html=True)
 
        entities = results["entities"]
        if entities.get("name"):
            st.markdown(f"**👤 Candidate Name:** {entities['name']}")
        if entities.get("organizations"):
            st.markdown(f"**🏢 Organizations:** {', '.join(entities['organizations'][:5])}")
        if entities.get("locations"):
            st.markdown(f"**📍 Locations:** {', '.join(entities['locations'][:5])}")
 
        # ── Sections Detected ─────────────────────────────────────────────
        st.markdown('<p class="section-header">📑 Sections Detected</p>', unsafe_allow_html=True)
 
        section_display = {
            "skills": "🛠️ Skills",
            "education": "🎓 Education",
            "experience": "💼 Experience",
            "projects": "🚀 Projects",
            "summary": "📝 Summary",
            "certifications": "🏆 Certifications",
        }
        cols = st.columns(2)
        for i, (key, label) in enumerate(section_display.items()):
            present = results["sections"].get(key, False)
            icon = "✅" if present else "❌"
            cols[i % 2].markdown(f"{icon} {label}")
 
        # ── Missing Sections ──────────────────────────────────────────────
        if results["missing_sections"]:
            st.markdown('<p class="section-header">⚠️ Missing Sections</p>', unsafe_allow_html=True)
            for ms in results["missing_sections"]:
                st.warning(f"Missing: **{ms}**")
 
    with right_col:
        # ── Technical Skills ──────────────────────────────────────────────
        st.markdown('<p class="section-header">⚙️ Technical Skills Found</p>', unsafe_allow_html=True)
        tech_skills = results["skills"].get("technical", [])
        if tech_skills:
            badges = "".join(
                f'<span class="skill-badge">{s.title()}</span>'
                for s in tech_skills
            )
            st.markdown(badges, unsafe_allow_html=True)
        else:
            st.info("No technical skills detected. Add a Skills section.")
 
        # ── Soft Skills ───────────────────────────────────────────────────
        st.markdown('<p class="section-header">🤝 Soft Skills Found</p>', unsafe_allow_html=True)
        soft_skills = results["skills"].get("soft", [])
        if soft_skills:
            badges = "".join(
                f'<span class="skill-badge" style="background:#f0fff0;color:#276749">{s.title()}</span>'
                for s in soft_skills
            )
            st.markdown(badges, unsafe_allow_html=True)
        else:
            st.info("No soft skills detected.")
 
        # ── Keyword Overlap (if JD provided) ─────────────────────────────
        kw = results["kw_overlap"]
        if kw["matched"] or kw["missing"]:
            st.markdown('<p class="section-header">🔑 Keyword Overlap with JD</p>', unsafe_allow_html=True)
            k1, k2 = st.columns(2)
            with k1:
                st.metric("Overlap", f"{kw['overlap_pct']}%")
                if kw["matched"]:
                    st.markdown("**✅ Matched:**")
                    st.markdown(", ".join(kw["matched"][:10]))
            with k2:
                if kw["missing"]:
                    st.markdown("**❌ Missing from JD:**")
                    st.markdown(", ".join(kw["missing"][:10]))
 
    # ── Suggestions ────────────────────────────────────────────────────────
    st.markdown("---")
    st.markdown("## 💡 Improvement Suggestions")
    for suggestion in results["suggestions"]:
        st.markdown(
            f'<div class="suggestion-item">{suggestion}</div>',
            unsafe_allow_html=True,
        )
 
    # ── Resume Text Preview ───────────────────────────────────────────────
    st.markdown("---")
    st.markdown("## 📄 Resume Preview")
    word_count = len(resume_text.split())
    st.caption(f"Word count: {word_count}")
    with st.expander("View extracted resume text", expanded=False):
        st.text_area(
            "Resume Text (read-only preview)",
            value=resume_text,
            height=300,
            disabled=True,
            key="preview_text",
        )
 
 
# ---------------------------------------------------------------------------
# BUTTON 1 — Analyze Resume
# ---------------------------------------------------------------------------
st.markdown("<br>", unsafe_allow_html=True)
analyze_col, _, _ = st.columns([1, 1, 1])
with analyze_col:
    analyze_clicked = st.button(
        "🔍 Analyze Resume",
        type="primary",
        use_container_width=True,
        disabled=(uploaded_file is None),
    )
 
if analyze_clicked:
    if uploaded_file is None:
        st.error("Please upload a resume file first.")
    else:
        # Parse the uploaded file
        with st.spinner("📂 Extracting text from resume..."):
            parse_result = parse_resume(uploaded_file)
 
        if parse_result["error"]:
            st.error(f"❌ {parse_result['error']}")
            st.stop()
 
        resume_text = parse_result["text"]
 
        if len(resume_text.split()) < 20:
            st.warning(
                "⚠️ Very little text was extracted. "
                "The file might be image-based. Try a text-based PDF or DOCX."
            )
 
        # Store editable text in session state
        st.session_state.resume_text_editable = resume_text
 
        # Run full pipeline
        results = run_analysis(resume_text, job_description)
        st.session_state.results = results
 
        st.success("✅ Analysis complete!")
 
# Render results if available
if st.session_state.results:
    render_results(st.session_state.results, st.session_state.resume_text_editable)
 
 
# ---------------------------------------------------------------------------
# BUTTON 2 — Generate Improved Resume (AI Rewrite)
# ---------------------------------------------------------------------------
if st.session_state.results:
    st.markdown("---")
    st.markdown("## 🤖 AI Resume Improvement")
    st.markdown(
        "Click below to generate an **ATS-optimized rewrite** of your resume "
        "using FLAN-T5. The AI will use professional tone, bullet points, "
        "and strong action verbs."
    )
 
    gen_col, _ = st.columns([1, 2])
    with gen_col:
        gen_clicked = st.button(
            "✨ Generate Improved Resume",
            use_container_width=True,
        )
 
    if gen_clicked:
        with st.spinner("🤖 AI is rewriting your resume... (this may take 30–60 seconds on CPU)"):
            improved = generate_improved_resume(
                st.session_state.resume_text_editable,
                job_description,
                st.session_state.results.get("missing_sections", []),
            )
            st.session_state.improved_resume = improved
 
    if st.session_state.improved_resume:
        st.markdown("### ✅ AI-Generated Improved Resume")
        st.text_area(
            "You can copy this improved resume text:",
            value=st.session_state.improved_resume,
            height=400,
            key="improved_output",
        )
        st.info(
            "💡 **Tip:** Copy this improved text, paste it into the editor below, "
            "and click **Re-evaluate** to see your new ATS score!"
        )
 
    # ── Professional Summary Generator ─────────────────────────────────────
    with st.expander("🖊️ Generate Professional Summary"):
        summ_col, _ = st.columns([1, 2])
        with summ_col:
            target_role = st.text_input(
                "Target job title (optional)",
                placeholder="e.g., Backend Engineer",
            )
            summ_btn = st.button("Generate Summary", key="summ_btn")
 
        if summ_btn:
            with st.spinner("Generating summary..."):
                summary = generate_professional_summary(
                    name=st.session_state.results["entities"].get("name", ""),
                    skills=st.session_state.results["skills"].get("technical", []),
                    experience_present=st.session_state.results["sections"].get(
                        "experience", False
                    ),
                    job_title=target_role,
                )
            st.markdown("**Generated Summary:**")
            st.markdown(f"> {summary}")
 
 
# ---------------------------------------------------------------------------
# BUTTON 3 — Resume Editor + Re-evaluation
# ---------------------------------------------------------------------------
if st.session_state.results:
    st.markdown("---")
    st.markdown("## ✏️ Edit & Re-evaluate Resume")
    st.markdown(
        "Edit your resume text below (paste the AI-improved version or make "
        "manual changes) and click **Re-evaluate** to get a fresh ATS score."
    )
 
    edited_text = st.text_area(
        "Edit your resume here:",
        value=st.session_state.resume_text_editable,
        height=400,
        key="editor_area",
        help="Make any changes to improve your resume, then click Re-evaluate.",
    )
 
    re_eval_col, _ = st.columns([1, 2])
    with re_eval_col:
        re_eval_clicked = st.button(
            "🔄 Re-evaluate Edited Resume",
            type="secondary",
            use_container_width=True,
        )
 
    if re_eval_clicked:
        if not edited_text.strip():
            st.error("The editor is empty. Please paste your resume text.")
        else:
            # Update session state
            st.session_state.resume_text_editable = edited_text
 
            with st.spinner("📊 Re-analyzing your edited resume..."):
                new_results = run_analysis(edited_text, job_description)
                st.session_state.results = new_results
 
            st.success("✅ Re-evaluation complete!")
            st.markdown("### 📊 Updated Results")
            render_results(new_results, edited_text)
 
 
# ---------------------------------------------------------------------------
# Footer
# ---------------------------------------------------------------------------
st.markdown("---")
st.markdown(
    """
    <div style="text-align:center; color:#6c757d; font-size:0.85rem; padding: 1rem 0;">
        🎯 AI Resume ATS Analyzer — Built with open-source AI tools<br>
        Powered by FLAN-T5 · Sentence-BERT · spaCy · PyMuPDF
    </div>
    """,
    unsafe_allow_html=True,
)