src/ui/app.py

from __future__ import annotations

import asyncio
import json
import logging
import sys
import time
from pathlib import Path

import gradio as gr

# Ensure project root is on the path for direct execution
_project_root = str(Path(__file__).resolve().parent.parent.parent)
if _project_root not in sys.path:
    sys.path.insert(0, _project_root)

from src.core.config import DATA_DIR  # noqa: E402
from src.core.models import MatchScores, Rationale, SearchResultItem  # noqa: E402
from src.matching.scorer import DEFAULT_SLIDER_WEIGHTS, CandidateScorer  # noqa: E402
from src.ui.components import (  # noqa: E402
    LOADING_STEPS,
    create_analytics_dashboard,
    create_candidate_card,
    create_empty_state,
    create_loading_overlay,
    create_progress_html,
    create_rationale_panel,
)

logger = logging.getLogger(__name__)

indexes_dir = DATA_DIR / "indexes"
faiss_path = indexes_dir / "faiss_index.bin"
id_map_path = indexes_dir / "faiss_id_map.json"
bm25_path = indexes_dir / "bm25_index.pkl"

_search_initialized = False


def _ensure_search_system() -> bool:
    """Lazy-initialize heavy model components (embeddings, FAISS, cross-encoder)
    on first search instead of loading at import time."""
    global _search_initialized
    if _search_initialized:
        return True
    if not faiss_path.exists():
        logger.warning("No FAISS index found. Run 'python scripts/build_indexes.py' first.")
        return False

    from src.api.routes.search import init_orchestrator
    from src.core.config import build_orchestrator

    orchestrator, _, _ = build_orchestrator(
        faiss_path=faiss_path,
        id_map_path=id_map_path,
        bm25_path=bm25_path,
    )
    init_orchestrator(orchestrator)
    logger.info("Search system initialized")
    _search_initialized = True
    return True

SLIDER_DIMS = [
    ("Skill Match", "skill_match", DEFAULT_SLIDER_WEIGHTS["skill_match"]),
    ("Experience", "experience_match", DEFAULT_SLIDER_WEIGHTS["experience_match"]),
    ("Education", "education_match", DEFAULT_SLIDER_WEIGHTS["education_match"]),
    ("Assessment", "assessment_score", DEFAULT_SLIDER_WEIGHTS["assessment_score"]),
    ("Behavioral", "behavioral_signals", DEFAULT_SLIDER_WEIGHTS["behavioral_signals"]),
    ("Cultural Fit", "cultural_fit", DEFAULT_SLIDER_WEIGHTS["cultural_fit"]),
]

SLIDER_KEYS = [k for _, k, _ in SLIDER_DIMS]


def _parse_slider_weights(*slider_values: float) -> dict[str, float]:
    return {key: val for key, val in zip(SLIDER_KEYS, slider_values)}


DOMAIN_SUBSKILLS: dict[str, list[str]] = {
    # Machine Learning / AI
    "machine learning": ["ml", "pytorch", "tensorflow", "scikit-learn", "deep learning", "keras", "jax", "mlops", "neural networks", "computer vision", "nlp", "transformers", "llm", "genai", "rag"],
    "ml": ["machine learning", "pytorch", "tensorflow", "scikit-learn", "deep learning", "keras", "jax", "mlops", "neural networks", "computer vision", "nlp", "transformers", "llm", "genai", "rag"],
    "artificial intelligence": ["ai", "machine learning", "deep learning", "neural networks", "nlp", "computer vision", "transformers", "llm", "generative ai", "genai"],
    "ai": ["artificial intelligence", "machine learning", "deep learning", "neural networks", "nlp", "computer vision", "transformers", "llm", "generative ai", "genai"],
    "deep learning": ["dl", "neural networks", "pytorch", "tensorflow", "keras", "cnn", "rnn", "transformers", "gan"],
    "dl": ["deep learning", "neural networks", "pytorch", "tensorflow", "keras", "cnn", "rnn", "transformers", "gan"],
    "natural language processing": ["nlp", "transformers", "bert", "gpt", "spacy", "nltk", "tokenization", "text classification", "llm", "rag"],
    "nlp": ["natural language processing", "transformers", "bert", "gpt", "spacy", "nltk", "tokenization", "text classification", "llm", "rag"],
    "computer vision": ["cv", "opencv", "cnn", "image processing", "yolo", "pytorch", "tensorflow", "object detection", "image segmentation"],
    "cv": ["computer vision", "opencv", "cnn", "image processing", "yolo", "pytorch", "tensorflow", "object detection", "image segmentation"],

    # Data Science & Data Engineering
    "data science": ["python", "r", "pandas", "numpy", "scipy", "scikit-learn", "statistics", "machine learning", "sql", "tableau", "data analysis", "data visualization"],
    "data engineering": ["python", "scala", "sql", "spark", "hadoop", "airflow", "kafka", "dbt", "snowflake", "redshift", "bigquery", "spark-sql", "spark streaming", "hive", "etl", "data pipeline"],
    "big data": ["hadoop", "spark", "hive", "pig", "mapreduce", "kafka", "cassandra", "hbase", "flink"],

    # Cloud & DevOps
    "devops": ["ci/cd", "docker", "kubernetes", "k8s", "terraform", "jenkins", "ansible", "aws", "prometheus", "grafana", "git", "github actions", "argocd", "helm", "linux", "bash", "chef", "puppet"],
    "cloud": ["aws", "gcp", "azure", "cloud computing", "serverless", "iam", "s3", "ec2", "lambda", "kubernetes", "docker", "terraform"],
    "aws": ["amazon web services", "ec2", "s3", "lambda", "rds", "dynamodb", "ecs", "eks", "cloudformation", "iam", "route53", "sqs", "sns"],
    "gcp": ["google cloud platform", "compute engine", "gcs", "bigquery", "gke", "cloud functions", "cloud run", "app engine", "pub/sub"],
    "azure": ["microsoft azure", "azure vms", "azure blob storage", "azure functions", "aks", "azure sql", "active directory"],

    # Web Development (Frontend, Backend, Fullstack)
    "frontend": ["react", "reactjs", "vue", "angular", "javascript", "typescript", "html", "css", "next.js", "nuxt", "svelte", "tailwind", "sass", "webpack", "vite", "bootstrap"],
    "backend": ["node.js", "nodejs", "express", "nestjs", "django", "flask", "fastapi", "spring boot", "spring", "golang", "go", "java", "python", "postgresql", "mysql", "mongodb", "redis", "ruby on rails", "rails", "php", "laravel", "graphql", "rest api"],
    "fullstack": ["react", "vue", "angular", "node.js", "nodejs", "javascript", "typescript", "html", "css", "next.js", "sql", "nosql", "postgresql", "mongodb", "graphql", "rest api"],
    "web development": ["html", "css", "javascript", "typescript", "react", "node.js", "backend", "frontend", "fullstack", "web design"],

    # Mobile Development
    "mobile": ["android", "ios", "flutter", "react native", "swift", "kotlin", "objective-c", "java", "dart", "xcode", "android studio"],
    "android": ["kotlin", "java", "android sdk", "jetpack compose", "android studio", "retrofit", "rxjava"],
    "ios": ["swift", "objective-c", "xcode", "swiftui", "cocoapods", "core data", "ios sdk"],
    "flutter": ["dart", "flutter sdk", "flutter widgets", "bloc", "provider", "mobile"],
    "react native": ["javascript", "typescript", "react", "react-native-navigation", "expo", "mobile"],

    # QA & Testing
    "qa": ["testing", "quality assurance", "manual testing", "automation testing", "selenium", "cypress", "playwright", "junit", "pytest", "jest", "postman", "api testing", "mobile testing"],
    "testing": ["qa", "manual testing", "automation testing", "selenium", "cypress", "playwright", "junit", "pytest", "jest", "postman", "api testing", "mobile testing"],
    "automation testing": ["selenium", "cypress", "playwright", "pytest", "junit", "cucumber", "test automation", "webdriver", "appium"],

    # System Architecture & Networking
    "system design": ["microservices", "distributed systems", "load balancing", "caching", "scalability", "message queues", "database sharding", "replication", "system architecture"],
    "distributed systems": ["microservices", "kafka", "rabbitmq", "grpc", "kubernetes", "consensus algorithms", "raft", "paxos", "load balancing"],
    "cybersecurity": ["security", "penetration testing", "ethical hacking", "cryptography", "firewalls", "owasp", "vulnerability assessment", "siem", "soc", "network security"],
    "security": ["cybersecurity", "penetration testing", "ethical hacking", "cryptography", "firewalls", "owasp", "vulnerability assessment", "siem", "soc", "network security"],

    # Blockchain & Web3
    "blockchain": ["web3", "solidity", "smart contracts", "ethereum", "bitcoin", "hyperledger", "rust", "truffle", "hardhat", "ethers.js"],
    "web3": ["blockchain", "solidity", "smart contracts", "ethereum", "ethers.js", "web3.js", "dapps"],

    # Product & Agile Management
    "product management": ["product roadmap", "agile", "scrum", "jira", "confluence", "product strategy", "user stories", "wireframing"],
    "agile": ["scrum", "kanban", "jira", "confluence", "sprint planning", "standups", "retrospectives"],
}


def serialize_query_to_json(parsed: Any, original: str) -> str:
    import json
    data = {
        "required_skills": [s.name for s in parsed.required_skills],
        "preferred_skills": [s.name for s in parsed.preferred_skills],
        "subskills": parsed.subskills if hasattr(parsed, "subskills") else {},
        "min_experience": parsed.experience.min_years if parsed.experience else None,
        "max_experience": parsed.experience.max_years if parsed.experience else None,
        "location": parsed.location.city if parsed.location else "",
        "remote_ok": parsed.location.remote_ok if parsed.location else False,
        "original_query": original,
    }
    return json.dumps(data, indent=2)


def is_json_query(query: str) -> bool:
    q = query.strip()
    return q.startswith("{") and q.endswith("}")


def parse_json_to_query_object(json_str: str) -> Any:
    import json
    from src.core.models import ParsedQuery, RequiredSkill, PreferredSkill, ExperienceRequirements, LocationRequirements
    data = json.loads(json_str)
    
    req_skills = []
    for s in data.get("required_skills", []):
        if isinstance(s, dict):
            req_skills.append(RequiredSkill(name=s.get("name", "")))
        else:
            req_skills.append(RequiredSkill(name=str(s)))
            
    pref_skills = []
    for s in data.get("preferred_skills", []):
        if isinstance(s, dict):
            pref_skills.append(PreferredSkill(name=s.get("name", "")))
        else:
            pref_skills.append(PreferredSkill(name=str(s)))
            
    min_exp = data.get("min_experience")
    max_exp = data.get("max_experience")
    exp = ExperienceRequirements(
        min_years=float(min_exp) if min_exp is not None else None,
        max_years=float(max_exp) if max_exp is not None else None,
    )
    
    loc = LocationRequirements(
        city=data.get("location", ""),
        remote_ok=bool(data.get("remote_ok", False)),
    )
    
    subskills = data.get("subskills", {})
    
    return ParsedQuery(
        required_skills=req_skills,
        preferred_skills=pref_skills,
        subskills=subskills,
        experience=exp,
        location=loc,
        original_query=data.get("original_query", ""),
    )


async def parse_query_to_ui(query: str, use_turbo: bool) -> tuple[str, int, bool, Any]:
    if not query or not query.strip():
        return "", 0, False, None

    from src.core.models import ParsedQuery

    if is_json_query(query):
        try:
            parsed = parse_json_to_query_object(query)
            
            # Automatically populate subskills if empty
            if not parsed.subskills:
                parsed.subskills = {}
            for rsk in parsed.required_skills:
                name_lower = rsk.name.lower().strip()
                if name_lower in DOMAIN_SUBSKILLS and rsk.name not in parsed.subskills:
                    parsed.subskills[rsk.name] = DOMAIN_SUBSKILLS[name_lower]
            for psk in parsed.preferred_skills:
                name_lower = psk.name.lower().strip()
                if name_lower in DOMAIN_SUBSKILLS and psk.name not in parsed.subskills:
                    parsed.subskills[psk.name] = DOMAIN_SUBSKILLS[name_lower]
            
            formatted_json = serialize_query_to_json(parsed, parsed.original_query)
            min_exp = int(parsed.experience.min_years) if (parsed.experience and parsed.experience.min_years is not None) else 0
            remote_ok = bool(parsed.location.remote_ok) if parsed.location else False
            return formatted_json, min_exp, remote_ok, parsed
        except Exception as e:
            logger.warning(f"Failed to parse user JSON query, falling back to natural language: {e}")

    from src.core.config import check_llm_provider_connected
    llm_connected = check_llm_provider_connected()
    if not llm_connected and not use_turbo:
        gr.Warning("No LLM provider detected (OpenAI/Gemini keys missing or Ollama offline). Automatically switching to Turbo Mode.")
        use_turbo = True

    parsed = None
    if use_turbo:
        from src.core.query_parser import parse_query
        try:
            parsed = parse_query(query)
        except Exception as e:
            logger.warning(f"Fast parser failed: {e}")
    else:
        from src.agents.planner import PlannerAgent
        try:
            planner = PlannerAgent()
            parsed = await planner.plan(query)
        except Exception as e:
            logger.warning(f"Planner LLM failed: {e}")
            
    if parsed is None:
        parsed = ParsedQuery()

    # Automatically populate subskills
    if not parsed.subskills:
        parsed.subskills = {}
    for rsk in parsed.required_skills:
        name_lower = rsk.name.lower().strip()
        if name_lower in DOMAIN_SUBSKILLS:
            parsed.subskills[rsk.name] = DOMAIN_SUBSKILLS[name_lower]
    for psk in parsed.preferred_skills:
        name_lower = psk.name.lower().strip()
        if name_lower in DOMAIN_SUBSKILLS and psk.name not in parsed.subskills:
            parsed.subskills[psk.name] = DOMAIN_SUBSKILLS[name_lower]

    # Determine min experience
    min_exp = 0
    if parsed.experience and parsed.experience.min_years is not None:
        min_exp = int(parsed.experience.min_years)
    else:
        q_lower = query.lower()
        if "senior" in q_lower or "sr" in q_lower:
            min_exp = 5
        elif "lead" in q_lower or "principal" in q_lower or "staff" in q_lower:
            min_exp = 7
        elif "junior" in q_lower or "jr" in q_lower or "fresher" in q_lower:
            min_exp = 0
            
    # Determine remote status
    remote_ok = "remote" in query.lower() or (parsed.location is not None and bool(parsed.location.remote_ok))
    
    # Sync remote_ok and min_years into the ParsedQuery object itself
    if parsed.location is None:
        from src.core.models import LocationRequirements
        parsed.location = LocationRequirements()
    parsed.location.remote_ok = remote_ok

    if parsed.experience is None:
        from src.core.models import ExperienceRequirements
        parsed.experience = ExperienceRequirements()
    if parsed.experience.min_years is None and min_exp > 0:
        parsed.experience.min_years = float(min_exp)

    # Serialize to JSON string
    json_str = serialize_query_to_json(parsed, query)
    return json_str, min_exp, remote_ok, parsed, use_turbo


def on_turbo_toggle(use_turbo: bool) -> bool:
    from src.core.config import check_llm_provider_connected
    if not use_turbo:
        if not check_llm_provider_connected():
            gr.Warning("No LLM provider detected (OpenAI/Gemini keys missing or Ollama offline). Keeping Turbo Mode on.")
            return True
    return use_turbo


async def search_handler(
    query: str, location: str, min_experience: int, remote_ok: bool, max_results: int,
    use_turbo: bool,
    parsed_query: Any,
    *slider_values: float,
    progress: gr.Progress = gr.Progress(),
) -> tuple[str, str, str, Any]:
    if not query.strip():
        return (
            create_empty_state(),
            "",
            "[]",
            gr.update(visible=False),
        )

    from src.ui.components import create_error_panel

    progress(0.05, desc="🔍 Initializing search system...")
    if not _ensure_search_system():
        return (
            create_error_panel(
                "Search system not initialized. Please build indexes first "
                "by running <tt>python scripts/build_indexes.py</tt>."
            ),
            "",
            "[]",
            gr.update(visible=False),
        )

    slider_weights = _parse_slider_weights(*slider_values)

    from src.api.routes.search import _orchestrator
    from src.core.models import SearchFilters

    filters = SearchFilters(
        location=location.strip() if location.strip() else None,
        min_experience_years=float(min_experience) if min_experience > 0 else None,
        remote_ok=bool(remote_ok),
    )

    try:
        t0 = time.time()

        progress(0.15, desc="📝 Parsing query — understanding skills, experience, location...")
        await asyncio.sleep(0.01)  # Let progress render

        progress(0.30, desc="📡 Hybrid search — scanning 100K profiles (FAISS + BM25)...")
        await asyncio.sleep(0.01)

        response = await _orchestrator.run(
            query,
            slider_weights=slider_weights,
            use_turbo=use_turbo,
            top_k=max_results,
            filters=filters,
            parsed_query=parsed_query,
        )

        progress(0.65, desc="⚡ AI reranking — cross-encoder precision scoring...")
        await asyncio.sleep(0.01)

        progress(0.80, desc="📊 Computing multi-signal scores across 6 dimensions...")
        await asyncio.sleep(0.01)

        elapsed = time.time() - t0
        logger.info(f"Search completed in {elapsed:.1f}s")
    except Exception as e:
        logger.exception("Search failed")
        return (
            create_error_panel(f"Search failed: {e}"),
            "",
            "[]",
            gr.update(visible=False),
        )

    # Serialize results to JSON for caching in Gradio State
    raw_results = []
    for item in response.results[:max_results]:
        raw_results.append({
            "rank": item.rank,
            "profile_id": item.profile_id,
            "name": item.name,
            "current_title": item.current_title,
            "current_company": item.current_company,
            "location": item.location,
            "experience_years": item.experience_years,
            "scores": item.scores.model_dump() if hasattr(item.scores, "model_dump") else {},
            "matched_skills": item.matched_skills,
            "missing_skills": item.missing_skills,
            "rationale": (
                item.rationale.model_dump() if hasattr(item.rationale, "model_dump") else {}
            ),
        })

    results_json = json.dumps(raw_results)

    results_html = "<div class='results-container'>"
    for item in response.results[:max_results]:
        results_html += create_candidate_card(item)
    results_html += "</div>"

    # Metadata header
    md = response.search_metadata
    methods_str = " + ".join(md.methods_used) if md and md.methods_used else "hybrid"
    badges = ""
    if md and md.listwise_ranked:
        badges += '<span class="badge badge-listwise">&#127942; Listwise Ranked</span>'
    if md and md.pii_anonymized:
        badges += '<span class="badge badge-pii">&#128737;&#65039; PII Anonymized</span>'
    badges += f'<span class="badge badge-method">{methods_str}</span>'
    metadata_header = f"""
    <div style="display:flex;gap:8px;align-items:center;margin-bottom:12px;flex-wrap:wrap;">
        {badges}
        <span style="font-size:12px;color:#9ca3af;margin-left:auto;">
            {response.total_candidates_searched} candidates searched
            | {response.processing_time_ms}ms
        </span>
    </div>
    """

    rationales_html = ""
    for item in response.results[:5]:
        rationales_html += create_rationale_panel(item.rationale, item.name)

    return metadata_header + results_html, rationales_html, results_json, gr.update(visible=True)


def re_rank_handler(results_json: str, *slider_values: float) -> str:
    if not results_json or results_json == "[]":
        return "<p>No results to re-rank. Search first.</p>"

    from src.ui.components import create_error_panel

    try:
        raw = json.loads(results_json)
    except (json.JSONDecodeError, TypeError) as e:
        logger.warning("Re-rank received invalid JSON: %s", e)
        return create_error_panel("Could not parse cached results. Please re-run your search.")

    try:
        slider_weights = _parse_slider_weights(*slider_values)
    except Exception as e:
        logger.warning("Slider parsing error in re_rank_handler: %s", e)
        return create_error_panel(f"Could not parse slider weights: {e}")

    scorer = CandidateScorer()

    try:
        for r in raw:
            scores_dict = r.get("scores", {})
            match_scores = scorer.compute_overall(scores_dict, slider_weights)
            r["_re_score"] = match_scores.overall
            r["_re_scores"] = match_scores

        raw.sort(key=lambda x: x.get("_re_score", 0), reverse=True)

        html = "<div class='results-container'>"
        for rank, r in enumerate(raw, 1):
            r["rank"] = rank
            rationale_dict = r.get("rationale", {}) or {}
            item = SearchResultItem(
                rank=rank,
                profile_id=r.get("profile_id", ""),
                name=r.get("name", ""),
                current_title=r.get("current_title"),
                current_company=r.get("current_company"),
                location=r.get("location"),
                experience_years=r.get("experience_years"),
                scores=r.get("_re_scores", MatchScores()),
                matched_skills=r.get("matched_skills", []),
                missing_skills=r.get("missing_skills", []),
                rationale=Rationale(**rationale_dict) if isinstance(rationale_dict, dict) else Rationale(),
            )
            html += create_candidate_card(item)
        html += "</div>"

        return html
    except Exception as e:
        logger.exception("Re-rank processing failed")
        return create_error_panel(f"Re-ranking failed: {e}")


def create_app() -> gr.Blocks:
    with gr.Blocks(
        title="India Runs — AI-Powered Candidate Discovery",
    ) as app:
        gr.HTML("""
        <div class="app-header">
            <div class="app-title">India Runs</div>
            <div class="app-subtitle">AI-Powered Candidate Discovery — Beyond keywords, beyond filters.</div>
        </div>
        """)

        results_state = gr.State("")
        parsed_query_state = gr.State(None)

        with gr.Tabs():
            with gr.Tab("🔍 Search"):
                with gr.Row():
                    with gr.Column(scale=1):
                        query_input = gr.Textbox(
                            label="Job Query",
                            placeholder="e.g., senior DevOps engineer with 5+ yrs AWS...",
                            lines=3,
                        )
                        use_turbo_toggle = gr.Checkbox(
                            label="⚡ Turbo Mode (Skip LLM planner/agent loops)",
                            value=True,
                        )
                        transform_btn = gr.Button("🪄 Transform Query", variant="secondary")
                        gr.Examples(
                            examples=[
                                "Find a senior Python developer with ML experience in Bangalore",
                                "aws devops engineer kubernetes terraform ci/cd",
                                "Product manager with B2B SaaS experience and growth mindset",
                                "Senior frontend engineer react typescript remote",
                            ],
                            inputs=query_input,
                        )
                        location_filter = gr.Textbox(label="📍 Location")
                        experience_filter = gr.Slider(
                            label="📅 Min Experience (years)", minimum=0, maximum=20, step=1, value=0,
                        )
                        remote_ok = gr.Checkbox(label="🏠 Remote OK", value=False)
                        max_results = gr.Slider(
                            label="📋 Max Results", minimum=5, maximum=50, step=5, value=10,
                        )

                        with gr.Accordion("🔍 Parsed Requirements JSON", open=False):
                            parsed_query_display = gr.JSON(label="Parsed Requirements", value={})

                        with gr.Accordion("🎛️ Scoring Weights", open=False):
                            gr.Markdown(
                                "Adjust the importance of each dimension. "
                                "Results re-rank automatically after search."
                            )
                            slider_inputs = []
                            for label, key, default in SLIDER_DIMS:
                                slider = gr.Slider(
                                    minimum=0, maximum=100, step=5, value=int(default * 100),
                                    label=label,
                                )
                                slider_inputs.append(slider)

                        search_btn = gr.Button("🔎 Search Candidates", variant="primary", size="lg")

                    with gr.Column(scale=2):
                        results_area = gr.HTML(
                            label="Results",
                            value=create_empty_state(),
                        )
                        rationale_area = gr.HTML(label="Rationale Report", value="")
                        re_rank_btn = gr.Button("🔄 Re-Rank with Current Weights", variant="secondary", visible=False)

                search_inputs = [
                    query_input, location_filter,
                    experience_filter, remote_ok, max_results,
                    use_turbo_toggle,
                    parsed_query_state,
                    *slider_inputs,
                ]
                transform_btn.click(
                    fn=parse_query_to_ui,
                    inputs=[query_input, use_turbo_toggle],
                    outputs=[parsed_query_display, experience_filter, remote_ok, parsed_query_state, use_turbo_toggle],
                    show_progress="hidden",
                )
                search_btn.click(
                    fn=parse_query_to_ui,
                    inputs=[query_input, use_turbo_toggle],
                    outputs=[parsed_query_display, experience_filter, remote_ok, parsed_query_state, use_turbo_toggle],
                    show_progress="hidden",
                ).then(
                    fn=lambda: create_loading_overlay("Initializing search..."),
                    outputs=[results_area],
                    show_progress="hidden",
                ).then(
                    fn=search_handler,
                    inputs=search_inputs,
                    outputs=[results_area, rationale_area, results_state, re_rank_btn],
                    show_progress="hidden",
                )

                use_turbo_toggle.change(
                    fn=on_turbo_toggle,
                    inputs=[use_turbo_toggle],
                    outputs=[use_turbo_toggle],
                    show_progress="hidden",
                )

                re_rank_inputs = [results_state, *slider_inputs]
                re_rank_btn.click(
                    fn=re_rank_handler,
                    inputs=re_rank_inputs,
                    outputs=[results_area],
                    show_progress="hidden",
                )
                for slider in slider_inputs:
                    slider.change(
                        fn=re_rank_handler,
                        inputs=re_rank_inputs,
                        outputs=[results_area],
                        show_progress="hidden",
                    )

            with gr.Tab("📊 Analytics"):
                analytics_html = gr.HTML(label="Analytics Dashboard")
                refresh_btn = gr.Button("Refresh Analytics", variant="secondary")
                search_btn.click(
                    fn=create_analytics_dashboard,
                    inputs=[results_state],
                    outputs=[analytics_html],
                    show_progress="hidden",
                )
                refresh_btn.click(
                    fn=create_analytics_dashboard,
                    inputs=[results_state],
                    outputs=[analytics_html],
                    show_progress="hidden",
                )

            with gr.Tab("ℹ️ About"):
                gr.HTML("""
                <div class="about-section">
                    <h2>About This System</h2>
                    <p style="font-size:15px;"><strong>Intelligent Candidate Discovery</strong> — a hybrid semantic search system that goes beyond keyword matching.</p>

                    <h3>Architecture</h3>
                    <div class="about-feature-grid">
                        <div class="about-feature-card">
                            <strong>🔍 Hybrid Search</strong>
                            <span>BM25 + FAISS vector search + Reciprocal Rank Fusion for maximum recall</span>
                        </div>
                        <div class="about-feature-card">
                            <strong>⚡ Cross-Encoder</strong>
                            <span>MiniLM-L6 reranker for precision re-ranking of top candidates</span>
                        </div>
                        <div class="about-feature-card">
                            <strong>🧠 Agentic Workflow</strong>
                            <span>LangGraph: Plan → Execute → Reflect → Re-plan with LLM reasoning</span>
                        </div>
                        <div class="about-feature-card">
                            <strong>🌐 Multilingual</strong>
                            <span>30+ Indian languages via paraphrase-multilingual-MiniLM embeddings</span>
                        </div>
                        <div class="about-feature-card">
                            <strong>📋 Listwise Ranking</strong>
                            <span>Plackett-Luce tournament ranking for nuanced candidate comparison</span>
                        </div>
                        <div class="about-feature-card">
                            <strong>📝 Rationale Reports</strong>
                            <span>Every match includes human-readable explanations and evidence</span>
                        </div>
                    </div>

                    <h3>Interactive Scoring</h3>
                    <p>Adjust 6 recruiter-facing dimensions via sliders. Results re-rank instantly without re-searching. Fine-tune for each role's unique priorities.</p>

                    <h3>Fairness First</h3>
                    <p>Bias monitoring across demographics, location, and university. PII anonymization prevents name-based bias. Transparent, explainable rankings with fairness metrics in every search.</p>

                    <h3>Tech Stack</h3>
                    <p>FastAPI · FAISS · Sentence-Transformers · LangGraph · Gradio · Python</p>

                    <h3>🏆 India Runs — Track 1: Data & AI Challenge</h3>
                    <p>Built by Team Atlas — Nikhil Choudhary</p>
                </div>
                """)

    return app


app = create_app()

if __name__ == "__main__":
    css_path = Path(__file__).resolve().parent / "styles.css"
    css_content = css_path.read_text(encoding="utf-8") if css_path.exists() else ""
    app.launch(
        server_name="127.0.0.1",
        server_port=7860,
        css=css_content,
    )