"""
app.py — Gradio Web Application (Entry Point)

AI-Powered Topic Modelling for Academic Journal Analysis.
Satisfies all deliverables C1–C10.

Run with:  python app.py
"""

from __future__ import annotations

import os
import traceback
import pandas as pd
import gradio as gr
from pathlib import Path


# ── Load .env file (API keys) ────────────────────────────────────────────

def _load_env() -> None:
    env_path = Path(__file__).parent / ".env"
    if not env_path.exists():
        return
    for line in env_path.read_text(encoding="utf-8").splitlines():
        line = line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        key, val = line.split("=", 1)
        key = key.strip()
        val = val.strip().strip('"').strip("'")
        if val and not os.getenv(key):
            os.environ[key] = val

_load_env()


# ── Import the agent ─────────────────────────────────────────────────────
from agent import TopicModellingAgent


# ════════════════════════════════════════════════════════════════════════════
# Colour / Style Tokens
# ════════════════════════════════════════════════════════════════════════════

ACCENT    = "#00d4aa"
ACCENT2   = "#7c3aed"
BG_DARK   = "#0f0f1a"
BG_MID    = "#161625"
BG_CARD   = "#1c1c32"
TXT       = "#e2e8f0"
TXT_MUTED = "#94a3b8"
BORDER    = "#2d2d44"

# ════════════════════════════════════════════════════════════════════════════
# Custom CSS
# ════════════════════════════════════════════════════════════════════════════

CSS = f"""
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@300;400;500;600;700&display=swap');

:root {{
  --accent:    {ACCENT};
  --accent2:   {ACCENT2};
  --bg-dark:   {BG_DARK};
  --bg-mid:    {BG_MID};
  --bg-card:   {BG_CARD};
  --txt:       {TXT};
  --txt-muted: {TXT_MUTED};
  --border:    {BORDER};
}}

/* ── Global ── */
body, .gradio-container {{
  background: var(--bg-dark) !important;
  color: var(--txt) !important;
  font-family: 'Inter', -apple-system, sans-serif !important;
}}

/* ── Header ── */
.app-header {{
  background: linear-gradient(135deg, {BG_CARD} 0%, {BG_MID} 100%);
  border: 1px solid {BORDER};
  border-radius: 16px;
  padding: 32px 40px;
  margin-bottom: 24px;
  position: relative;
  overflow: hidden;
}}
.app-header::before {{
  content: '';
  position: absolute;
  top: -50%; left: -50%;
  width: 200%; height: 200%;
  background:
    radial-gradient(circle at 30% 50%, {ACCENT}08 0%, transparent 50%),
    radial-gradient(circle at 70% 50%, {ACCENT2}06 0%, transparent 50%);
  pointer-events: none;
}}
.app-header h1 {{
  font-family: 'Inter', sans-serif;
  font-size: 2rem; font-weight: 800;
  color: {TXT}; margin: 0 0 6px;
  letter-spacing: -0.03em;
  position: relative;
}}
.app-header h1 span {{ color: {ACCENT}; }}
.app-header p {{
  color: {TXT_MUTED}; font-size: 13px; margin: 0;
  font-family: 'JetBrains Mono', monospace;
  letter-spacing: 1px; position: relative;
}}

/* ── Section cards ── */
.section-card {{
  background: {BG_CARD};
  border: 1px solid {BORDER};
  border-radius: 12px;
  padding: 20px 24px;
  margin-bottom: 16px;
}}
.section-title {{
  font-family: 'JetBrains Mono', monospace;
  font-size: 11px; font-weight: 700;
  letter-spacing: 3px; text-transform: uppercase;
  color: {ACCENT};
  margin-bottom: 16px; padding-bottom: 10px;
  border-bottom: 1px solid {BORDER};
}}

/* ── Run button ── */
.btn-run {{
  background: linear-gradient(135deg, {ACCENT} 0%, #00b894 100%) !important;
  color: {BG_DARK} !important;
  border: none !important; border-radius: 10px !important;
  font-family: 'JetBrains Mono', monospace !important;
  font-weight: 700 !important; font-size: 14px !important;
  letter-spacing: 1px !important;
  padding: 14px 28px !important;
  transition: all 0.3s ease !important;
  box-shadow: 0 4px 15px {ACCENT}30 !important;
}}
.btn-run:hover {{
  transform: translateY(-2px) !important;
  box-shadow: 0 8px 25px {ACCENT}50 !important;
}}

/* ── Tab navigation ── */
.tab-nav button {{
  font-family: 'JetBrains Mono', monospace !important;
  font-size: 12px !important; font-weight: 600 !important;
  letter-spacing: 0.5px !important;
  color: {TXT_MUTED} !important;
  border: none !important; background: transparent !important;
  padding: 10px 16px !important;
  transition: all 0.2s !important;
}}
.tab-nav button.selected {{
  color: {ACCENT} !important;
  border-bottom: 2px solid {ACCENT} !important;
}}

/* ── Dataframe styling ── */
.gradio-dataframe table {{
  background: {BG_MID} !important;
  border-collapse: collapse;
}}
.gradio-dataframe th {{
  background: {BG_CARD} !important;
  color: {ACCENT} !important;
  font-family: 'JetBrains Mono', monospace !important;
  font-size: 11px !important;
  letter-spacing: 1px !important;
  text-transform: uppercase !important;
  padding: 10px 12px !important;
  border-bottom: 1px solid {BORDER} !important;
}}
.gradio-dataframe td {{
  color: {TXT} !important;
  font-size: 12px !important;
  padding: 8px 12px !important;
  border-bottom: 1px solid {BORDER}80 !important;
  font-family: 'JetBrains Mono', monospace !important;
}}
.gradio-dataframe tr:hover td {{
  background: {ACCENT}08 !important;
}}

/* ── Inputs ── */
input, textarea, select, .gradio-textbox textarea {{
  background: {BG_MID} !important;
  border: 1px solid {BORDER} !important;
  color: {TXT} !important;
  font-family: 'JetBrains Mono', monospace !important;
  font-size: 13px !important;
  border-radius: 8px !important;
}}
input:focus, textarea:focus {{
  border-color: {ACCENT} !important;
  box-shadow: 0 0 0 2px {ACCENT}30 !important;
}}

/* ── Labels ── */
label, .gradio-label {{
  color: {TXT_MUTED} !important;
  font-family: 'JetBrains Mono', monospace !important;
  font-size: 11px !important;
  letter-spacing: 1px !important;
  text-transform: uppercase !important;
}}

/* ── File upload ── */
.gradio-file {{
  background: {BG_CARD} !important;
  border: 2px dashed {BORDER} !important;
  border-radius: 10px !important;
  transition: border-color 0.3s !important;
}}
.gradio-file:hover {{
  border-color: {ACCENT} !important;
}}

/* ── Accordion ── */
.gradio-accordion {{
  background: {BG_CARD} !important;
  border: 1px solid {BORDER} !important;
  border-radius: 10px !important;
}}

/* ── Stats badges ── */
.stats-badge {{
  display: inline-block;
  background: {ACCENT}15;
  border: 1px solid {ACCENT}40;
  border-radius: 8px;
  padding: 10px 18px; margin: 4px 6px;
  font-family: 'JetBrains Mono', monospace;
  font-size: 13px; color: {ACCENT};
}}
.stats-badge.novel {{
  background: {ACCENT2}15;
  border-color: {ACCENT2}40;
  color: {ACCENT2};
}}
.stats-badge b {{
  font-size: 18px;
  display: block;
  margin-top: 2px;
}}
"""


# ════════════════════════════════════════════════════════════════════════════
# Helper Functions
# ════════════════════════════════════════════════════════════════════════════

def build_stats_html(results: dict | None) -> str:
    """Build the statistics summary HTML panel."""
    if not results:
        return (
            f'<div style="color:{TXT_MUTED};text-align:center;padding:40px;'
            f'font-family:JetBrains Mono,monospace;font-size:13px;">'
            f'Upload a CSV and click <b style="color:{ACCENT};">Run Analysis</b> '
            f'to see results.</div>'
        )

    total      = results.get("total_topics", 0)
    title_n    = results.get("title_topics", 0)
    abstract_n = results.get("abstract_topics", 0)
    mapped     = results.get("mapped", 0)
    novel      = results.get("novel", 0)

    return f"""
    <div style="display:flex;flex-wrap:wrap;gap:12px;justify-content:center;padding:20px 10px;">
      <div class="stats-badge">📊 Total Topics<b>{total}</b></div>
      <div class="stats-badge">📝 Title Topics<b>{title_n}</b></div>
      <div class="stats-badge">📄 Abstract Topics<b>{abstract_n}</b></div>
      <div class="stats-badge">✅ MAPPED<b>{mapped}</b></div>
      <div class="stats-badge novel">🆕 NOVEL<b>{novel}</b></div>
    </div>
    """


# ════════════════════════════════════════════════════════════════════════════
# Main Analysis Handler
# ════════════════════════════════════════════════════════════════════════════

EMPTY_REVIEW  = pd.DataFrame(columns=["topic_id", "source", "keywords", "label"])
EMPTY_MAPPING = pd.DataFrame(
    columns=["topic_id", "source", "label", "pajais_category", "status", "confidence"]
)


def run_analysis(file_obj, api_key, provider, progress=gr.Progress()):
    """Run the full topic modelling pipeline and return all UI outputs."""

    empty = (
        EMPTY_REVIEW,
        EMPTY_MAPPING,
        build_stats_html(None),
        "", "",
        "",
        [],
    )

    # ── Validate upload ──────────────────────────────────────────────
    if file_obj is None:
        return (
            EMPTY_REVIEW,
            EMPTY_MAPPING,
            build_stats_html(None),
            "", "",
            "❌ No file uploaded. Please upload a CSV file first.",
            [],
        )

    # ── Resolve provider ─────────────────────────────────────────────
    prov = None if provider == "Auto-detect" else provider.lower()
    key  = api_key.strip() if api_key else None

    agent = TopicModellingAgent(api_key=key, provider=prov)

    try:
        # ── Run pipeline ─────────────────────────────────────────────
        csv_path = file_obj.name if hasattr(file_obj, "name") else str(file_obj)
        results = agent.run_pipeline(csv_path, progress_callback=progress)

        review_df   = agent.get_review_table()
        mapping_df  = agent.get_mapping_table()
        stats_html  = build_stats_html(results)
        narrative   = agent.narrative
        reflection  = agent.reflection
        logs        = "\n".join(agent.logs)
        dl_files    = agent.get_download_files()

        return (
            review_df,
            mapping_df,
            stats_html,
            narrative,
            reflection,
            logs,
            dl_files,
        )

    except Exception as e:
        error_msg = f"❌ Error: {e}\n\n{traceback.format_exc()}"
        return (
            EMPTY_REVIEW,
            EMPTY_MAPPING,
            build_stats_html(None),
            "", "",
            error_msg,
            [],
        )


# ════════════════════════════════════════════════════════════════════════════
# Gradio UI
# ════════════════════════════════════════════════════════════════════════════

with gr.Blocks(
    title="AI Topic Modelling — Academic Journal Analysis",
) as demo:

    # ── Header ────────────────────────────────────────────────────────────
    gr.HTML(f"""
    <div class="app-header">
      <h1>Topic<span>Modeller</span> AI</h1>
      <p>NMF Topic Modelling · PAJAIS Taxonomy Mapping · LLM-Enhanced Labeling</p>
    </div>
    """)

    # ════════════════════════════════════════════════════════════════════════
    # SECTION 1 — Upload & Configure
    # ════════════════════════════════════════════════════════════════════════
    with gr.Group(elem_classes="section-card"):
        gr.HTML('<div class="section-title">① Upload & Configure</div>')

        with gr.Row():
            with gr.Column(scale=3):
                file_input = gr.File(
                    label="Upload CSV (requires 'title' and 'abstract' columns)",
                    file_types=[".csv"],
                    file_count="single",
                )
            with gr.Column(scale=2):
                api_key_input = gr.Textbox(
                    label="LLM API Key (optional — enhances labels)",
                    placeholder="sk-... or gsk_... or your API key",
                    type="password",
                )
                provider_dropdown = gr.Dropdown(
                    label="LLM Provider",
                    choices=["Auto-detect", "Groq", "Mistral", "OpenAI"],
                    value="Auto-detect",
                )

        run_btn = gr.Button(
            "🚀  Run Full Analysis",
            elem_classes="btn-run",
            size="lg",
        )

        gr.HTML(f"""
        <div style="margin-top:12px;padding:10px 16px;background:{BG_MID};
                    border:1px solid {BORDER};border-radius:8px;
                    font-size:11px;color:{TXT_MUTED};line-height:1.8;
                    font-family:'JetBrains Mono',monospace;">
          <b style="color:{ACCENT};">ℹ️ Note:</b> Works <b>without</b> an API key
          (uses heuristic keyword labels). For LLM-enhanced labels, provide a
          <b>Groq</b> (free at
          <a href="https://console.groq.com" style="color:{ACCENT};">console.groq.com</a>),
          <b>Mistral</b>, or <b>OpenAI</b> key.
          Keys from <code>.env</code> file are loaded automatically.
        </div>
        """)

    # ════════════════════════════════════════════════════════════════════════
    # SECTION 2 — Results
    # ════════════════════════════════════════════════════════════════════════
    with gr.Group(elem_classes="section-card"):
        gr.HTML('<div class="section-title">② Results</div>')

        stats_display = gr.HTML(value=build_stats_html(None))

        with gr.Tabs():

            # ── Tab 1: Review Table (C4) ─────────────────────────────
            with gr.Tab("📊 Review Table (C4)"):
                gr.HTML(f"""
                <p style="font-size:12px;color:{TXT_MUTED};margin:0 0 10px;
                         font-family:'JetBrains Mono',monospace;">
                  All generated topics with IDs, source (title/abstract),
                  keywords, and labels. Target: 98+ rows
                  (50 title topics + 50 abstract topics).
                </p>
                """)
                review_table = gr.Dataframe(
                    value=EMPTY_REVIEW,
                    label="Topic Review Table",
                    interactive=False,
                    wrap=True,
                )

            # ── Tab 2: PAJAIS Mapping (C5) ───────────────────────────
            with gr.Tab("🗺️ PAJAIS Mapping (C5)"):
                gr.HTML(f"""
                <p style="font-size:12px;color:{TXT_MUTED};margin:0 0 10px;
                         font-family:'JetBrains Mono',monospace;">
                  Each topic mapped to the PAJAIS 25-category taxonomy.
                  Status: <b style="color:{ACCENT};">MAPPED</b> (matches taxonomy)
                  or <b style="color:{ACCENT2};">NOVEL</b> (new / unmapped theme).
                </p>
                """)
                mapping_table = gr.Dataframe(
                    value=EMPTY_MAPPING,
                    label="PAJAIS Taxonomy Mapping",
                    interactive=False,
                    wrap=True,
                )

            # ── Tab 3: Narrative (C8) ────────────────────────────────
            with gr.Tab("📝 Narrative (C8)"):
                narrative_output = gr.Textbox(
                    label="Academic Narrative (~500 words)",
                    lines=20,
                    interactive=False,
                    placeholder="Run analysis to generate the narrative...",
                )

            # ── Tab 4: Reflection (C10) ──────────────────────────────
            with gr.Tab("💭 Reflection (C10)"):
                reflection_output = gr.Textbox(
                    label="Research Reflection (~250 words)",
                    lines=14,
                    interactive=False,
                    placeholder="Run analysis to generate the reflection...",
                )

            # ── Tab 5: Pipeline Logs ─────────────────────────────────
            with gr.Tab("📋 Pipeline Logs"):
                logs_output = gr.Textbox(
                    label="Execution Log",
                    lines=20,
                    interactive=False,
                    placeholder="Pipeline logs will appear here after analysis...",
                )

    # ════════════════════════════════════════════════════════════════════════
    # SECTION 3 — Downloads
    # ════════════════════════════════════════════════════════════════════════
    with gr.Group(elem_classes="section-card"):
        gr.HTML('<div class="section-title">③ Download Outputs</div>')

        gr.HTML(f"""
        <p style="font-size:12px;color:{TXT_MUTED};margin:0 0 12px;
                 font-family:'JetBrains Mono',monospace;">
          Generated files: <code>comparison.csv</code> (C6) ·
          <code>taxonomy_map.json</code> (C7) ·
          <code>narrative.txt</code> (C8) ·
          <code>prompts.txt</code> (C9) ·
          <code>reflection.txt</code> (C10)
        </p>
        """)

        download_files = gr.File(
            label="Output Files",
            file_count="multiple",
            interactive=False,
        )

    # ════════════════════════════════════════════════════════════════════════
    # SECTION 4 — Help
    # ════════════════════════════════════════════════════════════════════════
    with gr.Group(elem_classes="section-card"):
        gr.HTML('<div class="section-title">④ Help & Deliverables</div>')

        with gr.Accordion("📋 Assignment Deliverables Checklist", open=False):
            gr.HTML(f"""
            <div style="font-size:12px;color:{TXT};line-height:2;
                        font-family:'JetBrains Mono',monospace;padding:8px;">
              <b style="color:{ACCENT};">C1.</b> HuggingFace-compatible Gradio app ✓<br/>
              <b style="color:{ACCENT};">C2.</b> 3 Python files: app.py, tools.py, agent.py ✓<br/>
              <b style="color:{ACCENT};">C3.</b> requirements.txt with 13 packages ✓<br/>
              <b style="color:{ACCENT};">C4.</b> Review table (98+ topics) → "Review Table" tab ✓<br/>
              <b style="color:{ACCENT};">C5.</b> PAJAIS mapping (MAPPED/NOVEL) → "PAJAIS Mapping" tab ✓<br/>
              <b style="color:{ACCENT};">C6.</b> comparison.csv → Downloads ✓<br/>
              <b style="color:{ACCENT};">C7.</b> taxonomy_map.json → Downloads ✓<br/>
              <b style="color:{ACCENT};">C8.</b> narrative.txt (~500 words) → "Narrative" tab ✓<br/>
              <b style="color:{ACCENT};">C9.</b> prompts.txt → Downloads ✓<br/>
              <b style="color:{ACCENT};">C10.</b> reflection.txt (~250 words) → "Reflection" tab ✓<br/>
            </div>
            """)

        with gr.Accordion("⚠️ Troubleshooting", open=False):
            gr.HTML(f"""
            <div style="font-size:11px;color:{TXT};line-height:1.8;
                        font-family:'JetBrains Mono',monospace;padding:8px;">
              <b style="color:#ff6b6b;">Missing columns error</b><br/>
              Ensure CSV has lowercase 'title' and 'abstract' columns.<br/><br/>
              <b style="color:#ff6b6b;">Rate limit (429 error)</b><br/>
              LLM API rate limit. Wait 1-2 min and retry. App still works
              without LLM (uses heuristic labels).<br/><br/>
              <b style="color:#ff6b6b;">Too few topics</b><br/>
              Dataset may be too small. Minimum ~20 papers recommended
              for meaningful topic modelling.<br/><br/>
              <b style="color:#ff6b6b;">Empty narrative/reflection</b><br/>
              If LLM unavailable, template-based text is used automatically.<br/>
            </div>
            """)

    # ════════════════════════════════════════════════════════════════════════
    # Wire Events
    # ════════════════════════════════════════════════════════════════════════
    run_btn.click(
        fn=run_analysis,
        inputs=[file_input, api_key_input, provider_dropdown],
        outputs=[
            review_table,
            mapping_table,
            stats_display,
            narrative_output,
            reflection_output,
            logs_output,
            download_files,
        ],
    )


# ════════════════════════════════════════════════════════════════════════════
# Launch
# ════════════════════════════════════════════════════════════════════════════
if __name__ == "__main__":
    demo.launch(
        show_error=True,
        theme=gr.themes.Base(
            primary_hue="teal",
            secondary_hue="purple",
            neutral_hue="slate",
            font=gr.themes.GoogleFont("Inter"),
            font_mono=gr.themes.GoogleFont("JetBrains Mono"),
        ),
        css=CSS,
    )