"""app.py — Gradio UI for BERTopic Multi-Agent Research. Zero if/else/for/while/try/except."""
import sys, os; sys.stdout.reconfigure(line_buffering=True)
from dotenv import load_dotenv
load_dotenv()

os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
import json, glob
print(">>> importing gradio...", flush=True)
import gradio as gr
print(">>> importing agents...", flush=True)
from agents import build_agent
from tools import PAPER_CACHE, OUTPUT_DIR, supabase
print(">>> building agent...", flush=True)
agent = build_agent()
_msg_count = 0
print(">>> agent ready!", flush=True)

def _pipeline(phase):
    phases = [("① Load", 1), ("② Codes", 2), ("③ Themes", 3), ("④ Review", 4), ("⑤ Names", 5), ("⑤½ PAJAIS", 5.5), ("⑥ Report", 6)]
    return " → ".join(list(map(lambda p: f"**{p[0]}**" if p[1]==phase else (f"✅ {p[0]}" if p[1]<phase else p[0]), phases)))

def _topic_rows(chat_id=None):
    res = supabase.table("chats").select("topics_json").eq("id", chat_id).execute().data if chat_id else []
    tops = res[0].get("topics_json") if res and res[0].get("topics_json") else []
    return list(map(lambda t: [t["id"], t["label"], "; ".join(t.get("top_sentences",[])[:1])[:100], t["count"], len(t.get("top_papers",[])), "yes", "", ""], tops))

def _history():
    return list(map(lambda r: f"[{r['id']}] {r['title']}", supabase.table("chats").select("id,title").order("created_at", desc=True).limit(20).execute().data))

def _latest_files():
    return sorted(glob.glob(os.path.join(OUTPUT_DIR, "*")), key=os.path.getmtime, reverse=True)[:10] or None

def respond(message, chat_history):
    global _msg_count; _msg_count += 1
    text = (message or "").strip() or "digital social innovation and societal impact"
    chat_id = supabase.table("chats").insert({"title": text[:50], "user_message": text, "bot_message": "Started..."}).execute().data[0]["id"]
    chat_history = chat_history + [{"role":"user","content":text}, {"role":"assistant","content":"🔄 **Dispatching agents...**\n\nSupervisor → OpenAlex → Tavily → Scopus → Validation → BERTopic Analysis\n\n_This may take 30-60 seconds..._"}]
    yield chat_history, "", _pipeline(2), _topic_rows(chat_id), _latest_files(), chat_id
    result = agent.invoke({"messages":[{"role":"user","content":f"Research: {text}. The chat_id is {chat_id}. Search all databases, validate, run BERTopic, upload."}]}, config={"configurable":{"thread_id":f"t{_msg_count}"}})
    chat_history[-1] = {"role":"assistant","content":result["messages"][-1].content}
    yield chat_history, "", _pipeline(6), _topic_rows(chat_id), _latest_files(), chat_id

def submit_review(td):
    edits = list(map(lambda r: f"Topic {r[0]}: {r[5]}, Rename='{r[6]}'", filter(lambda r: str(r[5]).lower()=="no" or str(r[6]).strip()!="", td)))
    return "Review:\n" + "\n".join(edits)

def load_chart(name):
    path = os.path.join(OUTPUT_DIR, str(name or ""))
    fallback = "<div style='text-align:center;color:#64748b;padding:60px;background:#fff;border-radius:8px'>📊 Run a search first to generate BERTopic charts</div>"
    return {True: lambda: "<iframe srcdoc='" + open(path,"r",encoding="utf-8").read().replace("'",'"') + "' width='100%' height='480' frameborder='0'></iframe>", False: lambda: fallback}[os.path.exists(path)]()

print(">>> fetching history...", flush=True)

def show_topic_papers(evt: gr.SelectData, chat_id_state):
    if not chat_id_state: return []
    row = evt.index[0]
    chat = supabase.table("chats").select("topics_json").eq("id", chat_id_state).execute().data[0]
    tops = chat.get("topics_json", []) if chat.get("topics_json") else []
    if row >= len(tops): return []
    papers = supabase.table("papers").select("title,web_link,date_of_publication,journal,no_of_citations,confidence_score").eq("topic_label", tops[row]["label"]).eq("chat_id", chat_id_state).execute().data
    return list(map(lambda p: [p.get("title", ""), p.get("web_link", ""), p.get("date_of_publication", ""), p.get("journal", ""), p.get("no_of_citations", ""), p.get("confidence_score", "")], papers))

hist = _history()
print(f">>> {len(hist)} past sessions", flush=True)

print(">>> building UI...", flush=True)
with gr.Blocks(title="BERTopic — Digital Social Innovation") as demo:

    gr.Markdown("# 🔬 Topic Modelling — Agentic AI\n*Qwen 72B 🧠 · MiniLM Embeddings · Cosine Clustering · 384d · Braun & Clarke Thematic Analysis*")
    
    pipeline_html = gr.Markdown(_pipeline(1))
    gr.Markdown("💡 **Multi-Agent Architecture:** Supervisor Agent orchestrates 4 workers (OpenAlex, Tavily, Scopus, Validation) + 1 analysis worker (BERTopic embedding → clustering → LLM labeling → Supabase upload)")


    chat_state = gr.State(None)
    chatbot = gr.Chatbot(height=320, show_label=False)

    with gr.Row():
        msg = gr.Textbox(placeholder="e.g. 'digital social innovation and societal impact'", show_label=False, scale=9, container=False)
        send = gr.Button("Send", scale=1, min_width=70)

    with gr.Row():
        cmd1 = gr.Button("▶ Digital Social Innovation", size="sm")
        cmd2 = gr.Button("▶ AI in Healthcare", size="sm")
        cmd3 = gr.Button("▶ Sustainable Tourism Tech", size="sm")

    with gr.Tabs():
        with gr.TabItem("📋 Review Table"):
            review_table = gr.Dataframe(headers=["#","Topic Label","Top Evidence","Sents","Papers","Approve","Rename To","Reasoning"], datatype=["number","str","str","number","number","str","str","str"], interactive=True)
            submit_btn = gr.Button("✅ Submit Review to Agent")
            review_out = gr.Textbox(label="Review Status", interactive=False)
            gr.Markdown("🛑 **STOP Gate** — Agent pauses here. Review each BERTopic cluster label. Edit Approve/Rename columns → click Submit.")
            gr.Markdown("📄 **Papers in Selected Topic**")
            paper_table = gr.Dataframe(headers=["Title", "Source", "Year", "Journal", "Citations", "Relevance Score"], interactive=False)
        with gr.TabItem("📊 Charts"):
            chart_dd = gr.Dropdown(choices=["rq4_abstract_bars.html","rq4_abstract_heatmap.html","rq4_abstract_intertopic.html"], value="rq4_abstract_bars.html", label="Select Visualization")
            chart_html = gr.HTML("<div style='text-align:center;color:#64748b;padding:60px;background:#fff;border-radius:8px'>📊 Charts appear here after BERTopic analysis</div>")
        with gr.TabItem("📥 Downloads"):
            download_files = gr.File(label="Output Files (summaries.json, emb.npy, charts, CSV)", file_count="multiple", interactive=False)

    history_dd = gr.Dropdown(choices=hist, label="📚 Past Research Sessions (Supabase)", interactive=True)

    msg.submit(respond, [msg, chatbot], [chatbot, msg, pipeline_html, review_table, download_files, chat_state])
    send.click(respond, [msg, chatbot], [chatbot, msg, pipeline_html, review_table, download_files, chat_state])
    submit_btn.click(submit_review, [review_table], [review_out])
    review_table.select(show_topic_papers, inputs=[chat_state], outputs=[paper_table])
    chart_dd.change(load_chart, [chart_dd], [chart_html])
    cmd1.click(lambda: "digital social innovation and societal impact", outputs=[msg])
    cmd2.click(lambda: "artificial intelligence in healthcare diagnosis", outputs=[msg])
    cmd3.click(lambda: "sustainable tourism technology adoption", outputs=[msg])

print(">>> launching...", flush=True)
demo.launch(server_name="0.0.0.0")