app.py

import os, re, json, traceback
from functools import lru_cache

import gradio as gr
import torch

# NEW: robust control-char sanitizer (requires `regex` package)
import regex as re2  # pip install regex

from settings import SNAPSHOT_PATH, PERSIST_CONTENT
from audit_log import log_event, hash_summary
from privacy import redact_text

# ---------- Environment / cache ----------
os.environ.setdefault("HF_HOME", "/data/.cache/huggingface")
os.environ.setdefault("HF_HUB_CACHE", "/data/.cache/huggingface/hub")
os.environ.setdefault("GRADIO_TEMP_DIR", "/data/gradio")
os.environ.setdefault("GRADIO_CACHE_DIR", "/data/gradio")
os.environ.pop("TRANSFORMERS_CACHE", None)
for p in ["/data/.cache/huggingface/hub", "/data/gradio"]:
    try:
        os.makedirs(p, exist_ok=True)
    except Exception:
        pass

# Optional Cohere
try:
    import cohere
    _HAS_COHERE = True
except Exception:
    _HAS_COHERE = False

from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login

from safety import safety_filter, refusal_reply
from retriever import init_retriever, retrieve_context
from decision_math import compute_operational_numbers
from prompt_templates import build_system_preamble
from upload_ingest import extract_text_from_files
from session_rag import SessionRAG
from mdsi_analysis import capacity_projection, cost_estimate, outcomes_summary

# ---------- Config ----------
MODEL_ID = os.getenv("MODEL_ID", "microsoft/Phi-3-mini-4k-instruct")  # local fallback
HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN") or os.getenv("HF_TOKEN")

COHERE_API_KEY = os.getenv("COHERE_API_KEY")
USE_HOSTED_COHERE = bool(COHERE_API_KEY and _HAS_COHERE)

MAX_NEW_TOKENS = int(os.getenv("MAX_NEW_TOKENS", "512"))

# ---------- Helpers ----------
def pick_dtype_and_map():
    if torch.cuda.is_available():
        return torch.float16, "auto"
    if torch.backends.mps.is_available():
        return torch.float16, {"": "mps"}
    return torch.float32, "cpu"

def is_identity_query(message, history):
    patterns = [
        r"\bwho\s+are\s+you\b",
        r"\bwhat\s+are\s+you\b",
        r"\bwhat\s+is\s+your\s+name\b",
        r"\bwho\s+is\s+this\b",
        r"\bidentify\s+yourself\b",
        r"\btell\s+me\s+about\s+yourself\b",
        r"\bdescribe\s+yourself\b",
        r"\band\s+you\s*\?\b",
        r"\byour\s+name\b",
        r"\bwho\s+am\s+i\s+chatting\s+with\b",
    ]
    def match(t): return any(re.search(p, (t or "").strip().lower()) for p in patterns)
    if match(message): return True
    if history:
        last_user = history[-1][0] if isinstance(history[-1], (list, tuple)) else None
        if match(last_user): return True
    return False

def _iter_user_assistant(history):
    for item in (history or []):
        if isinstance(item, (list, tuple)):
            u = item[0] if len(item) > 0 else ""
            a = item[1] if len(item) > 1 else ""
            yield u, a

def _history_to_prompt(message, history):
    parts = []
    for u, a in _iter_user_assistant(history):
        if u: parts.append(f"User: {u}")
        if a: parts.append(f"Assistant: {a}")
    parts.append(f"User: {message}")
    parts.append("Assistant:")
    return "\n".join(parts)

def _sanitize_text(s: str) -> str:
    """
    Strip control characters (except newline/tab) to avoid garbled UI output.
    """
    if not isinstance(s, str):
        return s
    return re2.sub(r'[\p{C}--[\n\t]]+', '', s)

# ---------- Cohere (default path) ----------
def cohere_chat(message, history):
    if not USE_HOSTED_COHERE:
        return None
    try:
        # Create client on demand to avoid init errors in some environments
        client = cohere.Client(api_key=COHERE_API_KEY)
        prompt = _history_to_prompt(message, history)
        resp = client.chat(
            model="command-r7b-12-2024",
            message=prompt,
            temperature=0.3,
            max_tokens=MAX_NEW_TOKENS,
        )
        if hasattr(resp, "text") and resp.text: return resp.text.strip()
        if hasattr(resp, "reply") and resp.reply: return resp.reply.strip()
        if hasattr(resp, "generations") and resp.generations: return resp.generations[0].text.strip()
        return None
    except Exception:
        return None

# ---------- Local model (accelerate-safe fallback) ----------
@lru_cache(maxsize=1)
def load_local_model():
    if not HF_TOKEN:
        raise RuntimeError("HUGGINGFACE_HUB_TOKEN is not set.")
    login(token=HF_TOKEN, add_to_git_credential=False)
    dtype, device_map = pick_dtype_and_map()
    tok = AutoTokenizer.from_pretrained(
        MODEL_ID, token=HF_TOKEN, use_fast=True, model_max_length=8192,
        padding_side="left", trust_remote_code=True,
    )
    try:
        mdl = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, token=HF_TOKEN, device_map=device_map,
            low_cpu_mem_usage=True, torch_dtype=dtype, trust_remote_code=True,
        )
    except Exception:
        mdl = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, token=HF_TOKEN,
            low_cpu_mem_usage=True, torch_dtype=dtype, trust_remote_code=True,
        )
        mdl.to("cuda" if torch.cuda.is_available() else "cpu")
    if mdl.config.eos_token_id is None and tok.eos_token_id is not None:
        mdl.config.eos_token_id = tok.eos_token_id
    return mdl, tok

def build_inputs(tokenizer, message, history):
    msgs = []
    for u, a in _iter_user_assistant(history):
        if u: msgs.append({"role": "user", "content": u})
        if a: msgs.append({"role": "assistant", "content": a})
    msgs.append({"role": "user", "content": message})
    return tokenizer.apply_chat_template(
        msgs, tokenize=True, add_generation_prompt=True, return_tensors="pt"
    )

def local_generate(model, tokenizer, input_ids, max_new_tokens=MAX_NEW_TOKENS):
    input_ids = input_ids.to(model.device)
    with torch.no_grad():
        out = model.generate(
            input_ids=input_ids, max_new_tokens=max_new_tokens,
            do_sample=True, temperature=0.3, top_p=0.9,
            repetition_penalty=1.15,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    gen_only = out[0, input_ids.shape[-1]:]
    return tokenizer.decode(gen_only, skip_special_tokens=True).strip()

# ---------- Snapshot loader ----------
def _load_snapshot(path=SNAPSHOT_PATH):
    try:
        with open(path, "r", encoding="utf-8") as f:
            return json.load(f)
    except Exception:
        return {
            "timestamp": None, "beds_total": 400, "staffed_ratio": 1.0, "occupied_pct": 0.97,
            "ed_census": 62, "ed_admits_waiting": 19, "avg_ed_wait_hours": 8,
            "discharge_ready_today": 11, "discharge_barriers": {"allied_health": 7, "placement": 4},
            "rn_shortfall": {"med_ward_A": 1, "med_ward_B": 1},
            "forecast_admits_next_24h": {"respiratory": 14, "other": 9},
            "isolation_needs_waiting": {"contact": 3, "airborne": 1}, "telemetry_needed_waiting": 5
        }

# ---------- Init retrieval engines ----------
init_retriever()
_session_rag = SessionRAG()  # in-memory; supports artifacts (CSV columns)

# ---------- Executive pre-compute (MDSi block) ----------
def _mdsi_block():
    base_capacity = capacity_projection(18, 48, 6)
    cons_capacity = capacity_projection(12, 48, 6)
    opt_capacity = capacity_projection(24, 48, 6)
    cost_1200 = cost_estimate(1200, 74.0, 75000.0)
    outcomes = outcomes_summary()
    return json.dumps({
        "capacity_projection": {"conservative": cons_capacity, "base": base_capacity, "optimistic": opt_capacity},
        "cost_for_1200": cost_1200,
        "outcomes_summary": outcomes
    }, indent=2)

# ---------- Core chat logic (Cohere-first with fallback) ----------
def clarityops_reply(user_msg, history, tz, uploaded_files_paths):
    try:
        log_event("user_message", None, {"sizes": {"chars": len(user_msg or "")}})

        # Safety (input)
        safe_in, blocked_in, reason_in = safety_filter(user_msg, mode="input")
        if blocked_in:
            ans = refusal_reply(reason_in)
            return history + [(user_msg, ans)]

        # Identity short-circuit
        if is_identity_query(safe_in, history):
            ans = "I am ClarityOps, your strategic decision making AI partner."
            return history + [(user_msg, ans)]

        # Debug slash command: /diag
        if (safe_in or "").strip().lower().startswith("/diag"):
            try:
                chunk_count = len(getattr(_session_rag, "texts", []) or [])
                cols = _session_rag.get_latest_csv_columns()
                sample = _session_rag.retrieve("the", k=2)
                msg = [
                    f"Chunks in session: {chunk_count}",
                    f"Latest CSV columns: {', '.join(cols) if cols else '<none>'}",
                    "Sample retrieved snippets:",
                    *(sample or ["<no snippets>"])
                ]
                return history + [(user_msg, "\n\n".join(msg))]
            except Exception as e:
                return history + [(user_msg, f"Diag error: {e}")]

        # Ingest uploads: returns chunks + artifacts
        if uploaded_files_paths:
            ing = extract_text_from_files(uploaded_files_paths)
            chunks = ing.get("chunks", []) if isinstance(ing, dict) else (inf or [])
            artifacts = ing.get("artifacts", []) if isinstance(ing, dict) else []
            if chunks:
                _session_rag.add_docs(chunks)
            if artifacts:
                _session_rag.register_artifacts(artifacts)
            log_event("uploads_added", None, {"chunks": len(chunks), "artifacts": len(artifacts)})

        # Deterministic CSV "columns/headers" handler
        if re.search(r"\b(columns?|headers?)\b", (safe_in or "").lower()):
            cols = _session_rag.get_latest_csv_columns()
            if cols:
                return history + [(user_msg, "Here are the column names from your most recent CSV upload:\n\n- " + "\n- ".join(cols))]

        # Heuristic: scenario mode nudge if a long case study was pasted
        plain = (safe_in or "").strip().lower()
        looks_like_case = ("background" in plain and "objective" in plain) or ("case study" in plain)
        if looks_like_case and len(plain) > 600:
            safe_in += (
                "\n\nPlease analyze the scenario above using the Expected Output Format: "
                "produce structured recommendations, estimates and assumptions, include tables and bullet points, "
                "and explicitly state how uploaded files (CSV/docs) influenced your estimates."
            )

        # Retrieve from session uploads (text chunks)
        session_snips = "\n---\n".join(_session_rag.retrieve(
            "diabetes screening Indigenous Métis mobile program cost throughput outcomes logistics bed flow staffing discharge forecast",
            k=6
        ))

        # Load daily snapshot + policies + computed ops numbers
        snapshot = _load_snapshot()
        policy_context = retrieve_context(
            "mobile diabetes screening Indigenous community outreach logistics referral pathways cultural safety data governance cost effectiveness outcomes bed management discharge acceleration ambulance offload"
        )
        computed = compute_operational_numbers(snapshot)

        # Exec scenario detect (MDSi)
        user_lower = (safe_in or "").lower()
        mdsi_extra = _mdsi_block() if ("diabetes" in user_lower or "mdsi" in user_lower or "mobile screening" in user_lower) else ""

        scenario_block = safe_in if len(safe_in) > 400 else ""
        system_preamble = build_system_preamble(
            snapshot=snapshot,
            policy_context=policy_context,
            computed_numbers=computed,
            scenario_text=scenario_block + (f"\n\nExecutive Pre-Computed Blocks:\n{mdsi_extra}" if mdsi_extra else ""),
            session_snips=session_snips
        )

        augmented_user = system_preamble + "\n\nUser question or request:\n" + safe_in

        # Cohere first
        out = cohere_chat(augmented_user, history)

        # Fallback to local HF model if Cohere not set or failed
        if not out:
            model, tokenizer = load_local_model()
            inputs = build_inputs(tokenizer, augmented_user, history)
            out = local_generate(model, tokenizer, inputs, max_new_tokens=MAX_NEW_TOKENS)

        # Tidy echoes and sanitize
        if isinstance(out, str):
            for tag in ("Assistant:", "System:", "User:"):
                if out.startswith(tag):
                    out = out[len(tag):].strip()
        out = _sanitize_text(out)

        # Safety (output)
        safe_out, blocked_out, reason_out = safety_filter(out, mode="output")
        if blocked_out:
            safe_out = refusal_reply(reason_out)

        # Audit (content-free fingerprints)
        log_event("assistant_reply", None, {
            **hash_summary("prompt", augmented_user if not PERSIST_CONTENT else ""),
            **hash_summary("reply", safe_out if not PERSIST_CONTENT else ""),
        })

        return history + [(user_msg, safe_out)]
    except Exception as e:
        err = f"Error: {e}"
        try:
            traceback.print_exc()
        except Exception:
            pass
        return history + [(user_msg, err)]

# ---------- Theme & CSS ----------
theme = gr.themes.Soft(primary_hue="teal", neutral_hue="slate", radius_size=gr.themes.sizes.radius_lg)
custom_css = """
:root { --brand-bg: #e6f7f8; --brand-accent: #0d9488; --brand-text: #0f172a; --brand-text-light: #ffffff; }
.gradio-container { background: var(--brand-bg); }
h1 { color: var(--brand-text); font-weight: 700; font-size: 28px !important; }
.chatbot header, .chatbot .label, .chatbot .label-wrap, .chatbot .top, .chatbot .header, .chatbot > .wrap > header { display: none !important; }
.message.user, .message.bot { background: var(--brand-accent) !important; color: var(--brand-text-light) !important; border-radius: 12px !important; padding: 8px 12px !important; }
textarea, input, .gr-input { border-radius: 12px !important; }
"""

# ---------- UI (single window; uploads at bottom) ----------
with gr.Blocks(theme=theme, css=custom_css, analytics_enabled=False) as demo:
    gr.Markdown("# ClarityOps Augmented Decision AI")

    chat = gr.Chatbot(label="", show_label=False, height=700)

    with gr.Row():
        uploads = gr.Files(
            label="Upload docs/images (PDF, DOCX, CSV, PNG, JPG)",
            file_types=["file"], file_count="multiple", height=68
        )

    with gr.Row():
        msg = gr.Textbox(
            label="",
            show_label=False,
            placeholder="Type a message… (paste scenarios here too; ClarityOps will adapt)",
            scale=10
        )
        send = gr.Button("Send", scale=1)
        clear = gr.Button("Clear chat", scale=1)

    state_history = gr.State(value=[])
    state_uploaded = gr.State(value=[])

    def _store_uploads(files, current):
        paths = []
        for f in (files or []):
            paths.append(getattr(f, "name", None) or f)
        return (current or []) + paths

    uploads.change(fn=_store_uploads, inputs=[uploads, state_uploaded], outputs=state_uploaded)

    def _on_send(user_msg, history, up_paths):
        try:
            if not user_msg or not user_msg.strip():
                return history, "", history
            new_history = clarityops_reply(user_msg.strip(), history or [], None, up_paths or [])
            return new_history, "", new_history
        except Exception as e:
            err = f"Error: {e}"
            try:
                traceback.print_exc()
            except Exception:
                pass
            new_hist = (history or []) + [(user_msg or "", err)]
            return new_hist, "", new_hist

    send.click(_on_send, inputs=[msg, state_history, state_uploaded],
               outputs=[chat, msg, state_history],
               concurrency_limit=2, queue=True)

    msg.submit(_on_send, inputs=[msg, state_history, state_uploaded],
               outputs=[chat, msg, state_history],
               concurrency_limit=2, queue=True)

    clear.click(lambda: ([], "", []), None, [chat, msg, state_history])

if __name__ == "__main__":
    port = int(os.environ.get("PORT", "7860"))
    demo.launch(server_name="0.0.0.0", server_port=port, show_api=False, max_threads=8)