import os
import re
import tempfile
import time

import gradio as gr
import numpy as np
import pandas as pd
from duckduckgo_search import DDGS
from google import genai
from google.genai import types

#  🎨 Responsive Glassmorphism CSS
glassy_css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');

*, *::before, *::after { box-sizing: border-box; }

body, html {
    background: linear-gradient(135deg, #0a0f1a 0%, #111827 40%, #1a2332 100%) !important;
    background-attachment: fixed;
    color: #e0e0e0 !important;
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
}

.gradio-container {
    background: transparent !important;
    max-width: 1500px !important;
    margin: 0 auto !important;
    padding: 12px !important;
}

/* ===== RESPONSIVE STACKING ===== */
@media (max-width: 768px) {
    .gradio-container { padding: 6px !important; }
    .main-row { flex-direction: column !important; }
    .main-row > .gr-column { min-width: 100% !important; max-width: 100% !important; }
    .sidebar-col { display: none !important; }  
    h1 { font-size: 1.4rem !important; }
    h3 { font-size: 1rem !important; }
}
@media (min-width: 769px) and (max-width: 1024px) {
    .main-row { flex-wrap: wrap !important; }
    .main-row > .gr-column { min-width: 48% !important; }
    .sidebar-col { min-width: 100% !important; }
}

/* ===== GLASS PANELS ===== */
div[class*="panel"] {
    background: rgba(255, 255, 255, 0.03) !important;
    border: 1px solid rgba(255, 255, 255, 0.08) !important;
    backdrop-filter: blur(20px) !important;
    -webkit-backdrop-filter: blur(20px) !important;
    border-radius: 16px !important;
    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4) !important;
    padding: 16px !important;
}

/* ===== SIDEBAR ===== */
.sidebar-col { border-right: 1px solid rgba(255,255,255,0.06) !important; }
.sidebar-col .gr-accordion { margin-bottom: 8px !important; }

/* ===== INPUTS ===== */
textarea, input[type="text"], input[type="password"] {
    background: rgba(0, 0, 0, 0.3) !important;
    border: 1px solid rgba(255, 255, 255, 0.12) !important;
    color: #fff !important;
    border-radius: 10px !important;
    transition: border-color 0.2s ease !important;
    font-family: 'Inter', sans-serif !important;
}
textarea:focus, input:focus {
    border-color: rgba(0, 200, 150, 0.5) !important;
    box-shadow: 0 0 12px rgba(0, 200, 150, 0.15) !important;
}

/* ===== PRIMARY BUTTON ===== */
button.primary {
    background: linear-gradient(135deg, #00c896 0%, #00b4d8 100%) !important;
    border: none !important;
    color: #fff !important;
    font-weight: 600 !important;
    border-radius: 10px !important;
    padding: 10px 20px !important;
    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
    box-shadow: 0 4px 15px rgba(0, 200, 150, 0.3) !important;
}
button.primary:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 6px 20px rgba(0, 200, 150, 0.5) !important;
}

/* ===== SECONDARY BUTTON ===== */
button.secondary {
    background: rgba(255,255,255,0.06) !important;
    border: 1px solid rgba(255,255,255,0.15) !important;
    color: #c0c0c0 !important;
    border-radius: 8px !important;
    transition: all 0.2s ease !important;
}
button.secondary:hover {
    background: rgba(255,255,255,0.12) !important;
    color: #fff !important;
}

/* ===== TYPOGRAPHY ===== */
h1 {
    color: #ffffff !important;
    font-weight: 700 !important;
    letter-spacing: -0.5px !important;
    background: linear-gradient(135deg, #00c896, #00b4d8) !important;
    -webkit-background-clip: text !important;
    -webkit-text-fill-color: transparent !important;
    background-clip: text !important;
}
h2, h3, h4 { color: #e8e8e8 !important; font-weight: 600 !important; }
p, span, label { color: #c0c0c0 !important; }

/* ===== SURVEYED LINKS ===== */
.surveyed-links a {
    color: #60efff !important;
    text-decoration: underline !important;
    word-break: break-all !important;
}
.surveyed-links p { margin-bottom: 8px !important; line-height: 1.6 !important; }

/* ===== GALLERY ===== */
.viz-gallery { min-height: 200px; }
.viz-gallery .gallery-item img {
    border-radius: 12px !important;
    border: 1px solid rgba(255,255,255,0.08) !important;
    cursor: pointer !important;
}

/* ===== ACCORDION ===== */
.gr-accordion { border-radius: 12px !important; overflow: hidden !important; }

/* ===== SCROLLABLE MARKDOWN ===== */
.report-body {
    max-height: 70vh;
    overflow-y: auto;
    padding-right: 8px;
}
.report-body::-webkit-scrollbar { width: 6px; }
.report-body::-webkit-scrollbar-thumb {
    background: rgba(255,255,255,0.15);
    border-radius: 3px;
}
"""

#  🎯 Constants
QUICK_MODE = "Quick Research (Direct)"
DEEP_MODE = "Deep Research & Debate"
DEBATE_SKIPPED = "*Debate skipped for Quick mode.*"
VIZ_DIR = tempfile.mkdtemp(prefix="research_viz_")

GEMINI_MODELS = [
    "gemini-2.5-flash",
    "gemini-flash-latest",
    "gemini-flash-lite-latest",
    "gemini-2.5-flash-lite",
    "gemini-2.0-flash",
]

#  🛠️ Core Functions


def make_safe(text):
    """
    STRICT SANITIZATION: Strips out ALL emojis and non-standard characters.
    This guarantees that underlying network libraries on Windows will NEVER
    crash with a 'UnicodeEncodeError'.
    """
    if not text:
        return ""
    return str(text).encode("ascii", "ignore").decode("ascii")


def search_web(
    api_key, query, time_limit, primary_model=GEMINI_MODELS[0], max_results=3
):
    """Hybrid Grounding Engine: Tries Native Google Search first, falls back to DuckDuckGo."""

    # Clean the query so we don't crash building the prompt
    safe_query = make_safe(query)

    # 1. ATTEMPT NATIVE GOOGLE AI SEARCH GROUNDING
    try:
        client = genai.Client(api_key=api_key)
        time_context = (
            f" Focus specifically on recent information from the {time_limit.lower()}."
            if time_limit != "All time"
            else ""
        )
        prompt = f"Conduct detailed, objective research on the following query: '{safe_query}'.{time_context} Provide comprehensive facts and statistics."

        # Strip the prompt of emojis just to be absolutely safe
        safe_prompt = make_safe(prompt)

        config = types.GenerateContentConfig(
            tools=[{"google_search": {}}], temperature=0.2
        )

        response = client.models.generate_content(
            model=primary_model, contents=safe_prompt, config=config
        )

        urls = []
        if response.candidates and response.candidates[0].grounding_metadata:
            gm = response.candidates[0].grounding_metadata
            chunks = getattr(gm, "grounding_chunks", [])
            for chunk in chunks:
                web = getattr(chunk, "web", None)
                if web:
                    uri = getattr(web, "uri", None)
                    title = getattr(web, "title", "Source")
                    if uri:
                        urls.append(f"🔗 **[{title}]({uri})**\n> {uri}")

        unique_urls = list(dict.fromkeys(urls))
        if unique_urls:
            # Make sure the returned text from the API doesn't contain weird characters that might crash the next step
            return make_safe(response.text), "\n\n".join(unique_urls)

    except Exception as e:
        print(f"Native Grounding Info (Falling back to DDG): {e}")

    # 2. FALLBACK TO DUCKDUCKGO SCAPING
    try:
        ddgs = DDGS()
        timelimit_map = {
            "Today": "d",
            "Past week": "w",
            "Past month": "m",
            "Past year": "y",
            "All time": None,
        }
        t = timelimit_map.get(time_limit)
        results = list(ddgs.text(safe_query, timelimit=t, max_results=max_results))

        extracted = []
        urls = []
        for r in results:
            title = make_safe(r.get("title", "Untitled"))
            href = r.get("href", "")
            body = make_safe(r.get("body", ""))

            if href and href.startswith("http"):
                urls.append(f"🔗 **[{title}]({href})**\n> {href}")
                extracted.append(f"Title: {title}\nLink: {href}\nSnippet: {body}")

        url_text = "\n\n".join(urls) if urls else ""
        data_text = "\n\n".join(extracted) if extracted else ""
        return data_text, url_text
    except Exception as e:
        return "", f"⚠️ Search error: {e}"


def call_gemini(api_key, prompt, primary_model=GEMINI_MODELS[0], retries=2):
    """Standard LLM execution with strict sanitization to prevent Windows encoding errors."""
    client = genai.Client(api_key=api_key)
    models_to_try = [primary_model] + [m for m in GEMINI_MODELS if m != primary_model]

    # STIRCTLY strip the prompt to plain ASCII to prevent the httpx library from crashing
    safe_prompt = make_safe(prompt)

    last_error = None
    for model in models_to_try:
        for attempt in range(retries):
            try:
                response = client.models.generate_content(
                    model=model, contents=safe_prompt
                )
                return response.text  # Don't strip the output, Gradio needs to show it. Only the OUTBOUND request causes crashes.
            except Exception as e:
                last_error = str(e)
                if "429" in last_error or "quota" in last_error.lower():
                    break
                if attempt < retries - 1:
                    time.sleep(2 * (attempt + 1))
                    continue
                break
    return f"⚠️ Error connecting to Gemini API. Details: {last_error}"


def execute_chart_code(code_str, output_filename="chart.png"):
    match = re.search(r"```python(.*?)```", code_str, re.DOTALL)
    if match:
        code_str = match.group(1).strip()
    code_str = re.sub(
        r"plt\.savefig\(['\"].*?['\"]", f"plt.savefig('{output_filename}'", code_str
    )
    safe_code = (
        "import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n"
        + code_str
    )
    namespace = {"pd": pd, "np": np}
    try:
        exec(safe_code, namespace)
        if os.path.exists(output_filename):
            return output_filename
    except Exception:
        pass
    return None


def generate_visualizations(
    api_key, topic, research_data, num_charts=1, primary_model=GEMINI_MODELS[0]
):
    chart_types = [
        ("statistical chart (bar, pie, line, or scatter)", "viz_chart"),
        ("comparison table as an image using matplotlib", "viz_table"),
        ("flowchart or process diagram using matplotlib", "viz_flow"),
    ]
    results = []
    for i in range(min(num_charts, 3)):
        chart_desc, prefix = chart_types[i]
        out_path = os.path.join(VIZ_DIR, f"{prefix}_{int(time.time())}_{i}.png")
        chart_prompt = f"""Write a Python script using matplotlib to create a {chart_desc} based on: '{topic}'.
Research context: {research_data[:1500]}
1. Import matplotlib.pyplot as plt
2. Apply a dark theme using plt.style.use('dark_background')
3. MUST save the figure as '{out_path}' using plt.savefig('{out_path}', bbox_inches='tight', dpi=150)
4. Output ONLY valid python code inside ```python ``` blocks."""
        code_response = call_gemini(api_key, chart_prompt, primary_model=primary_model)
        chart_path = execute_chart_code(code_response, output_filename=out_path)
        if chart_path:
            results.append(chart_path)
    return results


def generate_custom_viz(api_key, viz_prompt, primary_model=GEMINI_MODELS[0]):
    """Generate a standalone custom visualization from sidebar prompt."""
    if not api_key or not viz_prompt:
        return []

    out_path = os.path.join(VIZ_DIR, f"custom_{int(time.time())}.png")
    chart_prompt = f"""Write a Python script using matplotlib to create a visualization for: '{viz_prompt}'.
1. Import matplotlib.pyplot as plt
2. Apply a dark theme using plt.style.use('dark_background')
3. Make it visually clear and professional.
4. MUST save the figure as '{out_path}' using plt.savefig('{out_path}', bbox_inches='tight', dpi=150)
5. Output ONLY valid python code inside ```python ``` blocks. No explanations."""

    code_response = call_gemini(api_key, chart_prompt, primary_model=primary_model)
    chart_path = execute_chart_code(code_response, output_filename=out_path)
    if chart_path:
        return [chart_path]
    return []


def export_report(final_text, surveyed_urls, debate_text):
    if not final_text or final_text.startswith("*The final"):
        return None
    report = f"# Research Report\n\n## Final Intelligence Report\n\n{final_text}\n\n\n\n## Surveyed Resources\n\n{surveyed_urls}\n\n\n\n## Debate Transcript\n\n{debate_text}\n"
    out_path = os.path.join(VIZ_DIR, f"report_{int(time.time())}.md")
    with open(out_path, "w", encoding="utf-8") as f:
        f.write(report)
    return out_path


def clear_outputs():
    return (
        "",
        "*Web URLs will appear here...*",
        "*Debate transcript will stream here...*",
        "*The final synthesis will appear here...*",
        [],
        None,
    )


#  🧠 Multi-Agent Orchestration Workflow


def orchestrate_agents(
    topic, mode, time_limit, num_viz, api_key, primary_model, history
):
    if not api_key:
        yield (
            "❌ Error: Please provide a Gemini API Key in the sidebar.",
            "No sites",
            "No debate",
            "Error",
            [],
            history,
            gr.update(),
            "Error",
        )
        return
    if not topic.strip():
        yield (
            "❌ Error: Please enter a research topic.",
            "",
            "",
            "",
            [],
            history,
            gr.update(),
            "Error",
        )
        return

    log, live_debate = [], ""

    def update_log(msg):
        log.append(f"✅ {msg}")
        return "\n".join(log)

    # 1. Determine Routing
    actual_mode = mode
    if mode == "Auto":
        yield (
            update_log("Auto-Routing: Deciding research depth..."),
            "",
            "",
            "Analyzing topic complexity...",
            [],
            history,
            gr.update(),
            "🔄 Routing...",
        )
        decision = (
            call_gemini(
                api_key,
                f"Analyze: '{topic}'. Quick factual question or complex deep research? Reply 'Quick' or 'Deep'.",
                primary_model=primary_model,
            )
            .strip()
            .lower()
        )
        actual_mode = QUICK_MODE if "quick" in decision else DEEP_MODE
        yield (
            update_log(f"Auto-Routing decided: {actual_mode}"),
            "",
            "",
            "Routing chosen...",
            [],
            history,
            gr.update(),
            f"Mode: {actual_mode}",
        )

    # 2. Web Grounding Generation
    yield (
        update_log("Agents brainstorming search strategies..."),
        "💡 Generating queries...",
        "",
        "Optimizing intents...",
        [],
        history,
        gr.update(),
        "🧠 Thinking...",
    )
    queries_raw = (
        call_gemini(
            api_key,
            f"Topic: '{topic}'. Generate exactly 2 highly effective search queries. Return ONLY queries, one per line.",
            primary_model=primary_model,
        )
        .strip()
        .split("\n")
    )
    search_queries = [
        q.strip(' "-*') for q in queries_raw if q.strip() and "Error" not in q
    ][:2] or [topic]

    yield (
        update_log("Triggering Google AI Search Grounding..."),
        "🔎 Extracting context...",
        "",
        "Gathering grounded data...",
        [],
        history,
        gr.update(),
        "🌐 Grounding...",
    )

    all_broad_data, all_surveyed_urls = "", ""
    for q in search_queries:
        b_data, s_urls = search_web(
            api_key, q, time_limit, primary_model, max_results=3
        )
        if b_data:
            all_broad_data += f"\n\nSource [{q}]:\n" + b_data
        if s_urls and "⚠️" not in s_urls:
            all_surveyed_urls += s_urls + "\n\n"

    all_surveyed_urls = all_surveyed_urls.strip() or "⚠️ No valid links retrieved."
    yield (
        update_log("Grounding complete."),
        all_surveyed_urls,
        "",
        "Synthesizing...",
        [],
        history,
        gr.update(),
        "📊 Analyzing...",
    )

    gallery_images, final_answer = [], ""

    # 3. Execution
    if actual_mode == QUICK_MODE:
        yield (
            update_log("Executing Quick Direct Answer..."),
            all_surveyed_urls,
            DEBATE_SKIPPED,
            "Drafting final answer...",
            [],
            history,
            gr.update(),
            "✍️ Writing...",
        )
        prompt = f"You are a pragmatic expert. Based on this grounded data: {all_broad_data}. Answer: '{topic}'. Tone: Layman, simple. Provide verified resources."
        final_answer = call_gemini(api_key, prompt, primary_model=primary_model)
    else:
        yield (
            update_log("Deep Research: Agent 1 analyzing..."),
            all_surveyed_urls,
            live_debate,
            "Analyzing...",
            [],
            history,
            gr.update(),
            "🔬 Agent 1...",
        )
        ra1_findings = call_gemini(
            api_key,
            f"Analyze raw data for '{topic}': {all_broad_data}. Extract core facts.",
            primary_model=primary_model,
        )

        yield (
            update_log("Deep Research: Agent 2 cross-referencing..."),
            all_surveyed_urls,
            live_debate,
            "Cross-referencing...",
            [],
            history,
            gr.update(),
            "🔍 Agent 2...",
        )
        deep_data, deep_urls = search_web(
            api_key,
            f"{topic} critical analysis",
            time_limit,
            primary_model,
            max_results=2,
        )
        if deep_urls and "⚠️" not in deep_urls:
            all_surveyed_urls += "\n\n\n\n**Deep Search Results:**\n\n" + deep_urls
        master_research = call_gemini(
            api_key,
            f"Review Agent 1: {ra1_findings}. Cross-reference with: {deep_data}. Output verified master summary.",
            primary_model=primary_model,
        )

        tone = "Tone: Use simple, layman terms. Be rational and constructive."
        yield (
            update_log("Debate Round 1..."),
            all_surveyed_urls,
            live_debate,
            "Debating...",
            [],
            history,
            gr.update(),
            "⚖️ Debate R1...",
        )
        da1_r1 = call_gemini(
            api_key,
            f"Debate AI 1: Propose an answer to '{topic}' using: {master_research}. Under 100 words. {tone}",
            primary_model=primary_model,
        )
        live_debate += f"**🤖 AI 1 (Proposal):**\n{da1_r1}\n\n"
        da2_r1 = call_gemini(
            api_key,
            f"Debate AI 2: Review AI 1's draft: {da1_r1}. Point out missing context. Under 100 words. {tone}",
            primary_model=primary_model,
        )
        live_debate += f"**🧐 AI 2 (Critique):**\n{da2_r1}\n\n"

        yield (
            update_log("Debate Round 2..."),
            all_surveyed_urls,
            live_debate,
            "Debating...",
            [],
            history,
            gr.update(),
            "⚖️ Debate R2...",
        )
        da1_r2 = call_gemini(
            api_key,
            f"Debate AI 1: Refine based on AI 2's review: {da2_r1}. Under 100 words. {tone}",
            primary_model=primary_model,
        )
        live_debate += f"**🤖 AI 1 (Refinement):**\n{da1_r2}\n\n"
        da2_r2 = call_gemini(
            api_key,
            f"Debate AI 2: Final check on AI 1's revision: {da1_r2}. Under 100 words. {tone}",
            primary_model=primary_model,
        )
        live_debate += f"**🧐 AI 2 (Final Check):**\n{da2_r2}\n\n"

        yield (
            update_log("Master Orchestrator drafting output..."),
            all_surveyed_urls,
            live_debate,
            "Drafting Final Report...",
            [],
            history,
            gr.update(),
            "📝 Synthesizing...",
        )
        final_prompt = f"""You are the Final Orchestrator. Review this debate for topic '{topic}':
        AI 1: {da1_r2}
        AI 2: {da2_r2}
        
        Create the final intelligence report. 
        RULES:
        1. Tone: Simple, layman-friendly. Use examples and analogies.
        2. Formatting: Beautiful Markdown (headers, bullet points, tables if applicable).
        3. End with '### 📚 Verified Resources' with clickable markdown links."""
        final_answer = call_gemini(api_key, final_prompt, primary_model=primary_model)

    debate_display = live_debate if actual_mode != QUICK_MODE else DEBATE_SKIPPED
    yield (
        update_log("Final text generated."),
        all_surveyed_urls,
        debate_display,
        final_answer,
        [],
        history,
        gr.update(),
        "✅ Report ready",
    )

    # 4. Visualizations
    if num_viz > 0:
        yield (
            update_log(f"Generating {num_viz} visualization(s)..."),
            all_surveyed_urls,
            debate_display,
            final_answer,
            [],
            history,
            gr.update(),
            "📊 Generating charts...",
        )
        gallery_images = generate_visualizations(
            api_key,
            topic,
            all_broad_data,
            num_charts=num_viz,
            primary_model=primary_model,
        )
        yield (
            update_log(f"{len(gallery_images)} visualization(s) generated!"),
            all_surveyed_urls,
            debate_display,
            final_answer,
            gallery_images,
            history,
            gr.update(),
            "✅ Charts ready",
        )

    # 5. Complete
    yield (
        update_log("All Operations Completed Successfully!"),
        all_surveyed_urls,
        debate_display,
        final_answer,
        gallery_images,
        history,
        gr.update(),
        "✅ Done!",
    )

    history.append(
        {
            "topic": topic,
            "log": "\n".join(log),
            "urls": all_surveyed_urls,
            "debate": debate_display,
            "final": final_answer,
            "charts": gallery_images,
        }
    )
    yield (
        "\n".join(log),
        all_surveyed_urls,
        debate_display,
        final_answer,
        gallery_images,
        history,
        gr.update(choices=[h["topic"] for h in history]),
        "✅ Done!",
    )


def load_from_history(selected_topic, history):
    for item in history:
        if item["topic"] == selected_topic:
            return (
                item["log"],
                item["urls"],
                item["debate"],
                item["final"],
                item.get("charts", []),
            )
    return "", "", "", "No history found.", []


#  🖥️ Responsive Dashboard UI
with gr.Blocks(title="AI Research Hub") as app:
    history_state = gr.State([])

    gr.Markdown("# 🔍 Multi-Agent Research Hub")
    gr.Markdown(
        "*Native Google AI Grounding · Auto-Routing · Live Debates · Multi-Viz Analytics*"
    )

    with gr.Row(elem_classes=["main-row"]):
        with gr.Column(scale=1, min_width=220, elem_classes=["sidebar-col"]):
            gr.Markdown("### 🧭 Sidebar")
            with gr.Accordion("🔑 API Key", open=True):
                api_key = gr.Textbox(
                    label="Gemini API Key",
                    type="password",
                    placeholder="AIzaSy...",
                    show_label=False,
                )
            with gr.Accordion("📋 Quick Actions", open=True):
                export_btn = gr.Button(
                    "📥 Export Report", variant="secondary", size="sm"
                )
                export_file = gr.File(label="Download", visible=True, interactive=False)
                clear_btn = gr.Button("🗑️ Clear Outputs", variant="secondary", size="sm")

            with gr.Accordion("🎨 Custom Visualization", open=False):
                custom_viz_prompt = gr.Textbox(
                    label="Describe your chart",
                    placeholder="e.g. Pie chart of global energy sources",
                    lines=2,
                )
                custom_viz_btn = gr.Button("📊 Generate", variant="primary", size="sm")
                custom_viz_gallery = gr.Gallery(
                    label="Custom Charts",
                    columns=1,
                    height=200,
                    object_fit="contain",
                    interactive=False,
                )

            with gr.Accordion("🕰️ History", open=False):
                history_dropdown = gr.Dropdown(label="Past Queries", choices=[])
                load_history_btn = gr.Button("📂 Load", variant="secondary", size="sm")

        with gr.Column(scale=5, min_width=400):
            with gr.Row():
                topic = gr.Textbox(
                    label="🔍 Research Topic",
                    placeholder="Enter any topic to research...",
                    lines=2,
                    scale=3,
                )
                with gr.Column(scale=1, min_width=180):
                    model_select = gr.Dropdown(
                        choices=GEMINI_MODELS,
                        value=GEMINI_MODELS[0],
                        label="🤖 Primary Model",
                    )
                    mode = gr.Radio(
                        ["Auto", QUICK_MODE, DEEP_MODE], value="Auto", label="🧠 Mode"
                    )

            with gr.Row():
                time_limit = gr.Dropdown(
                    ["All time", "Past year", "Past month", "Past week", "Today"],
                    value="All time",
                    label="📅 Time Cutoff",
                    scale=1,
                )
                num_viz = gr.Slider(
                    minimum=0,
                    maximum=3,
                    step=1,
                    value=1,
                    label="📊 Visualizations",
                    scale=1,
                )
                submit_btn = gr.Button(
                    "🚀 Start Research", variant="primary", size="lg", scale=1
                )

            status_bar = gr.Textbox(
                show_label=False,
                interactive=False,
                lines=1,
                placeholder="Ready to research...",
            )

            with gr.Row(elem_classes=["main-row"]):
                with gr.Column(scale=1, min_width=280):
                    with gr.Accordion("🤖 Workflow Logs", open=True):
                        progress_box = gr.Textbox(
                            show_label=False, lines=8, interactive=False
                        )
                with gr.Column(scale=1, min_width=280):
                    with gr.Accordion("🌐 Grounded Resources", open=True):
                        surveyed_sites = gr.Markdown(
                            "*Web URLs will appear here...*",
                            elem_classes=["surveyed-links"],
                        )

            with gr.Accordion("⚖️ Live AI Debate", open=False):
                live_debate = gr.Markdown("*Debate transcript will stream here...*")

            gr.Markdown("")
            gr.Markdown("### 📑 Final Intelligence Report")
            final_output = gr.Markdown(
                "*The final synthesis will appear here...*",
                elem_classes=["report-body"],
            )

            gr.Markdown("")
            gr.Markdown("### 📊 Data Visualizations")
            viz_gallery = gr.Gallery(
                label="Generated Visualizations",
                columns=3,
                height=350,
                object_fit="contain",
                interactive=False,
                elem_classes=["viz-gallery"],
            )

    submit_btn.click(
        orchestrate_agents,
        inputs=[topic, mode, time_limit, num_viz, api_key, model_select, history_state],
        outputs=[
            progress_box,
            surveyed_sites,
            live_debate,
            final_output,
            viz_gallery,
            history_state,
            history_dropdown,
            status_bar,
        ],
    )
    load_history_btn.click(
        load_from_history,
        inputs=[history_dropdown, history_state],
        outputs=[progress_box, surveyed_sites, live_debate, final_output, viz_gallery],
    )
    export_btn.click(
        export_report,
        inputs=[final_output, surveyed_sites, live_debate],
        outputs=[export_file],
    )
    clear_btn.click(
        clear_outputs,
        outputs=[
            progress_box,
            surveyed_sites,
            live_debate,
            final_output,
            viz_gallery,
            export_file,
        ],
    )

    custom_viz_btn.click(
        generate_custom_viz,
        inputs=[api_key, custom_viz_prompt, model_select],
        outputs=[custom_viz_gallery],
    )

if __name__ == "__main__":
    app.launch(theme=gr.themes.Soft(), css=glassy_css)