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app3.py

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+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)

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+google-genai
+ddgs
+matplotlib
+pandas
+pillow
+numpy