# app.py
# app.py
import os, traceback, regex as re2
import gradio as gr
import pandas as pd
from typing import List, Tuple, Dict, Any

from settings import HEALTHCARE_SETTINGS, GENERAL_CONVERSATION_PROMPT, USE_SCENARIO_ENGINE
from audit_log import log_event
from privacy import safety_filter, refusal_reply
from data_registry import DataRegistry
from upload_ingest import extract_text_from_files
from healthcare_analysis import HealthcareAnalyzer
from scenario_planner import parse_to_plan
from scenario_engine import ScenarioEngine
from rag import RAGIndex
from llm_router import generate_narrative, cohere_chat, open_fallback_chat

def _sanitize_text(s: str) -> str:
    if not isinstance(s, str): return s
    # strip control chars (keep newlines/tabs)
    return re2.sub(r'[\p{C}--[\n\t]]+', '', s)

def _dataset_catalog(results: Dict[str, Any]) -> Dict[str, List[str]]:
    """Expose available columns per dataset to the planner."""
    cat: Dict[str, List[str]] = {}
    for k, v in results.items():
        if isinstance(v, pd.DataFrame):
            cat[k] = v.columns.tolist()
    return cat

def is_healthcare_scenario(text: str, has_files: bool) -> bool:
    """Heuristic: scenario mode when user provided files + scenario-ish text."""
    t = (text or "").lower()
    kws = HEALTHCARE_SETTINGS["healthcare_keywords"]
    structured = any(s in t for s in ["background", "situation", "tasks", "deliverables"])
    return has_files and (structured or any(k in t for k in kws))

def _append_msg(history_messages: List[Dict[str, str]], role: str, content: str) -> List[Dict[str, str]]:
    """Return a new history list with one message appended."""
    return (history_messages or []) + [{"role": role, "content": content}]

def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -> Tuple[List[Dict[str, str]], str]:
    try:
        safe_in, blocked_in, reason_in = safety_filter(user_msg, mode="input")
        if blocked_in:
            reply = refusal_reply(reason_in)
            new_hist = _append_msg(history_messages, "user", user_msg)
            new_hist = _append_msg(new_hist, "assistant", reply)
            return new_hist, ""

        # Normalize files -> paths (safe when files is None)
        file_paths = [getattr(f, "name", None) or f for f in (files or [])]

        # Register CSVs into the registry
        registry = DataRegistry()
        for p in file_paths:
            try:
                registry.add_path(p)
            except Exception as e:
                log_event("ingest_error", None, {"file": p, "err": str(e)})

        # RAG ingest (best-effort, text only; safe on empty)
        rag = RAGIndex()
        ing = extract_text_from_files(file_paths)
        rag.add(ing.get("chunks", []))

        # Scenario mode: plan -> deterministic execution -> narrative
        if is_healthcare_scenario(safe_in, bool(file_paths)) and USE_SCENARIO_ENGINE:
            analyzer = HealthcareAnalyzer(registry)
            datasets = analyzer.comprehensive_analysis(safe_in)  # expose dataframes by filename
            catalog = _dataset_catalog(datasets)

            # 1) LLM parses scenario into a plan (scenario-agnostic, no hardcoding)
            plan = parse_to_plan(safe_in, catalog)

            # 2) Deterministic execution of the plan (pandas-based)
            structured_md = ScenarioEngine.execute_plan(plan, datasets)

            # 3) Canadian grounding + narrative (Cohere primary, open-model fallback)
            rag_hits = [txt for txt, _ in rag.retrieve(safe_in, k=6)]
            narrative = generate_narrative(safe_in, structured_md, rag_hits)

            final = f"{structured_md}\n\n# Narrative & Recommendations\n\n{narrative}"
            reply = _sanitize_text(final)
        else:
            # General conversation mode (no scenario/files required)
            prompt = f"{GENERAL_CONVERSATION_PROMPT}\n\nUser: {safe_in}\nAssistant:"
            reply = cohere_chat(prompt) or open_fallback_chat(prompt) or "How can I help further?"
            reply = _sanitize_text(reply)

        # Append user then assistant messages to history
        new_hist = _append_msg(history_messages, "user", user_msg)
        new_hist = _append_msg(new_hist, "assistant", reply)
        return new_hist, ""

    except Exception as e:
        tb = traceback.format_exc()
        log_event("app_error", None, {"err": str(e), "tb": tb})
        new_hist = _append_msg(history_messages, "user", user_msg)
        new_hist = _append_msg(new_hist, "assistant", f"Error: {e}\n\n{tb}")
        return new_hist, ""

# -------- UI --------
with gr.Blocks(analytics_enabled=False) as demo:
    gr.Markdown("## Canadian Healthcare AI • Scenario-Agnostic (Cohere primary • Deterministic analytics)")
    # Use the new 'messages' format to avoid deprecation
    chat = gr.Chatbot(type="messages", height=520)
    files = gr.Files(
        file_count="multiple",
        type="filepath",
        file_types=HEALTHCARE_SETTINGS["supported_file_types"]
    )
    msg = gr.Textbox(placeholder="Paste any scenario (Background / Situation / Tasks / Deliverables) or just chat.")
    send = gr.Button("Send")
    clear = gr.Button("Clear")

    def _on_send(m, h, f):
        # h is already a list of {'role','content'} dicts with type="messages"
        h2, _ = handle(m, h or [], f or [])
        return h2, ""

    send.click(_on_send, inputs=[msg, chat, files], outputs=[chat, msg])
    msg.submit(_on_send, inputs=[msg, chat, files], outputs=[chat, msg])
    clear.click(lambda: ([], ""), outputs=[chat, msg])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))