app.py

# app.py
import os, re, json, traceback, pathlib
from functools import lru_cache
from typing import List, Dict, Any, Tuple

import gradio as gr
import torch
import regex as re2  # robust control-char sanitizer

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

# ---------- Writable caches (HF Spaces-safe) ----------
HOME = pathlib.Path.home()
HF_HOME = str(HOME / ".cache" / "huggingface")
HF_HUB_CACHE = str(HOME / ".cache" / "huggingface" / "hub")
HF_TRANSFORMERS = str(HOME / ".cache" / "huggingface" / "transformers")
ST_HOME = str(HOME / ".cache" / "sentence-transformers")
GRADIO_TMP = str(HOME / "app" / "gradio")
GRADIO_CACHE = GRADIO_TMP

os.environ.setdefault("HF_HOME", HF_HOME)
os.environ.setdefault("HF_HUB_CACHE", HF_HUB_CACHE)
os.environ.setdefault("TRANSFORMERS_CACHE", HF_TRANSFORMERS)
os.environ.setdefault("SENTENCE_TRANSFORMERS_HOME", ST_HOME)
os.environ.setdefault("GRADIO_TEMP_DIR", GRADIO_TMP)
os.environ.setdefault("GRADIO_CACHE_DIR", GRADIO_CACHE)
os.environ.setdefault("HF_HUB_ENABLE_XET", "0")
os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")

for p in [HF_HOME, HF_HUB_CACHE, HF_TRANSFORMERS, ST_HOME, GRADIO_TMP, GRADIO_CACHE]:
    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

# NEW: dynamic data analysis framework
from data_registry import DataRegistry
from schema_mapper import map_concepts, build_phase1_questions
from auto_metrics import build_data_findings_markdown

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

# Larger output budget for Phase 2
MAX_NEW_TOKENS = int(os.getenv("MAX_NEW_TOKENS", "2048"))

# ---------- Generic System Prompt ----------
SYSTEM_MASTER = """
SYSTEM ROLE
You are an AI analytical system that provides data-driven insights for any scenario.
Absolute rules:
- Use ONLY information provided in this conversation (scenario text + uploaded files + user answers).
- Never invent data. If something required is missing after clarifications, write the literal token: INSUFFICIENT_DATA.
- Provide clear analysis with calculations, evidence, and reasoning.
- Maintain privacy safeguards (aggregate data; suppress small cohorts <10).
- Adapt your analysis approach to the specific scenario and data provided.

Formatting rules for structured analysis:
- Start with the header: "Structured Analysis"
- Organize analysis into logical sections based on the scenario requirements
- End with concrete recommendations and a brief "Provenance" mapping outputs to scenario text, uploaded files, and answers.
""".strip()

# ---------- 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 _sanitize_text(s: str) -> str:
    if not isinstance(s, str):
        return s
    return re2.sub(r'[\p{C}--[\n\t]]+', '', s)

def is_scenario_triggered(text: str, uploaded_files_paths) -> bool:
    """Detect if this should be treated as a scenario analysis request."""
    t = (text or "").lower()
    
    # Scenario keywords
    scenario_keywords = [
        "scenario", "analysis", "analyze", "assess", "evaluate", "recommendation",
        "strategy", "plan", "solution", "decision", "priority", "allocate", "resource"
    ]
    
    has_keyword = any(keyword in t for keyword in scenario_keywords)
    has_files = bool(uploaded_files_paths)
    
    # If files are uploaded, assume scenario mode
    # If certain analytical keywords are present, assume scenario mode
    return has_files or has_keyword

# ---------- Cohere first ----------
def cohere_chat(message, history):
    if not USE_HOSTED_COHERE:
        return None
    try:
        client = cohere.Client(api_key=COHERE_API_KEY)
        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}")
        prompt = "\n".join(parts) + "\nAssistant:"
        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 (HF) ----------
@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,
        cache_dir=os.environ.get("TRANSFORMERS_CACHE")
    )
    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,
            cache_dir=os.environ.get("TRANSFORMERS_CACHE")
        )
    except Exception:
        mdl = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, token=HF_TOKEN,
            low_cpu_mem_usage=True, torch_dtype=dtype, trust_remote_code=True,
            cache_dir=os.environ.get("TRANSFORMERS_CACHE")
        )
        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 = [{"role": "system", "content": SYSTEM_MASTER}]
    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 & retrieval ----------
def _load_snapshot(path=SNAPSHOT_PATH):
    """Load operational snapshot if available."""
    try:
        with open(path, "r", encoding="utf-8") as f:
            return json.load(f)
    except Exception:
        return {}  # Return empty dict if no snapshot available

init_retriever()
_session_rag = SessionRAG()

# NEW: session-scoped data registry
_data_registry = DataRegistry()

# ---------- Core chat logic (generic scenario handling) ----------
def clarityops_reply(user_msg, history, tz, uploaded_files_paths, awaiting_answers=False):
    try:
        log_event("user_message", None, {"sizes": {"chars": len(user_msg or "")}})

        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)], awaiting_answers

        if is_identity_query(safe_in, history):
            ans = "I am an AI analytical system designed to help you analyze scenarios and make data-driven decisions."
            return history + [(user_msg, ans)], awaiting_answers

        # 1) Ingest uploads into RAG AND DataRegistry
        artifacts = []
        if uploaded_files_paths:
            ing = extract_text_from_files(uploaded_files_paths)
            chunks = ing.get("chunks", []) if isinstance(ing, dict) else (ing or [])
            artifacts = ing.get("artifacts", []) if isinstance(ing, dict) else []
            if chunks:
                _session_rag.add_docs(chunks)
            if artifacts:
                _session_rag.register_artifacts(artifacts)
            # register parsable tables into DataRegistry
            for p in uploaded_files_paths:
                _data_registry.add_path(p)
            log_event("uploads_added", None, {
                "chunks": len(chunks), "artifacts": len(artifacts), "tables": len(_data_registry.names())
            })

        # Quick helper for column inspection
        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))], awaiting_answers

        # 2) Decide mode
        scenario_mode = is_scenario_triggered(safe_in, uploaded_files_paths)

        if not scenario_mode:
            # ---------- Normal conversational chat ----------
            out = cohere_chat(safe_in, history) if USE_HOSTED_COHERE else None
            if not out:
                model, tokenizer = load_local_model()
                tiny = [{"role": "system", "content": "You are a helpful assistant."}]
                for u, a in _iter_user_assistant(history):
                    if u: tiny.append({"role": "user", "content": u})
                    if a: tiny.append({"role": "assistant", "content": a})
                tiny.append({"role": "user", "content": safe_in})
                inputs = tokenizer.apply_chat_template(tiny, tokenize=True, add_generation_prompt=True, return_tensors="pt")
                out = local_generate(model, tokenizer, inputs, max_new_tokens=MAX_NEW_TOKENS)

            out = _sanitize_text(out or "")
            safe_out, blocked_out, reason_out = safety_filter(out, mode="output")
            if blocked_out:
                safe_out = refusal_reply(reason_out)
            log_event("assistant_reply", None, {
                **hash_summary("prompt", safe_in if not PERSIST_CONTENT else ""),
                **hash_summary("reply", safe_out if not PERSIST_CONTENT else ""),
                "mode": "normal_chat",
            })
            return history + [(user_msg, safe_out)], awaiting_answers

        # ---------- Generic Scenario Analysis Mode ----------
        # 3) Build dynamic concept mapping from scenario + data
        mapping = map_concepts(safe_in, _data_registry)

        if not awaiting_answers:
            # PHASE 1: ask for missing/ambiguous information
            phase1 = build_phase1_questions(scenario_text=safe_in, registry=_data_registry, mapping=mapping)
            phase1 = _sanitize_text(phase1)
            log_event("assistant_reply", None, {
                **hash_summary("prompt", safe_in if not PERSIST_CONTENT else ""),
                **hash_summary("reply", phase1 if not PERSIST_CONTENT else ""),
                "mode": "scenario_phase1",
                "awaiting_next_phase": True
            })
            return history + [(user_msg, phase1)], True

        # PHASE 2: compute data analysis and generate structured response
        data_findings_md, missing_keys = build_data_findings_markdown(_data_registry, mapping)

        # Build context for analysis
        insufficient_data_note = ""
        if missing_keys:
            insufficient_data_note = (
                "\n\nData limitations: Missing or uncomputable: "
                + ", ".join(sorted(set(missing_keys)))
                + ". Where these are essential to analysis, write INSUFFICIENT_DATA."
            )

        # Get relevant context from uploaded documents
        # Extract key terms from scenario to improve retrieval
        scenario_terms = _extract_key_terms_from_scenario(safe_in)
        session_snips = "\n---\n".join(_session_rag.retrieve(scenario_terms, k=6))
        
        # Load any available operational data
        snapshot = _load_snapshot()
        computed_numbers = compute_operational_numbers(snapshot) if snapshot else {}
        
        # Get general policy/context if available
        policy_context = retrieve_context(scenario_terms)

        # Build comprehensive data summary for analysis
        registry_summary = _data_registry.summarize_for_prompt()
        artifact_block = "Uploaded Data Files:\n" + registry_summary if registry_summary else "No data files uploaded."

        scenario_block = safe_in if len((safe_in or "")) > 0 else ""
        system_preamble = build_system_preamble(
            snapshot=snapshot,
            policy_context=policy_context,
            computed_numbers=computed_numbers,
            scenario_text=scenario_block + f"\n\n{artifact_block}\n\n{data_findings_md}" + insufficient_data_note,
            session_snips=session_snips
        )

        directive = (
            "\n\n[ANALYSIS INSTRUCTION]\n"
            "Provide a structured analysis appropriate to this scenario. Begin with 'Structured Analysis' and "
            "organize your response into logical sections based on what the scenario requires. Use the data "
            "provided as ground truth. When information is missing, write INSUFFICIENT_DATA. Show your reasoning "
            "and calculations. End with concrete recommendations and a brief Provenance section.\n"
        )

        augmented_user = SYSTEM_MASTER + "\n\n" + system_preamble + "\n\nScenario and context:\n" + safe_in + directive

        out = cohere_chat(augmented_user, history)
        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)

        if isinstance(out, str):
            for tag in ("Assistant:", "System:", "User:"):
                if out.startswith(tag):
                    out = out[len(tag):].strip()
        out = _sanitize_text(out or "")

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

        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 ""),
            "mode": "scenario_phase2",
            "awaiting_next_phase": False
        })

        return history + [(user_msg, safe_out)], False

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

def _extract_key_terms_from_scenario(scenario_text: str) -> str:
    """Extract key terms from scenario text for better context retrieval."""
    if not scenario_text:
        return ""
    
    # Simple extraction of important words (remove common stop words)
    stop_words = {
        'the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by',
        'is', 'are', 'was', 'were', 'be', 'been', 'have', 'has', 'had', 'do', 'does', 'did',
        'a', 'an', 'this', 'that', 'these', 'those', 'i', 'you', 'he', 'she', 'it', 'we', 'they'
    }
    
    words = re.findall(r'\b[a-zA-Z]{3,}\b', scenario_text.lower())
    key_terms = [word for word in words if word not in stop_words]
    
    # Return first 10-15 key terms
    return ' '.join(key_terms[:15])

# ---------- Theme & CSS ----------
theme = gr.themes.Soft(primary_hue="teal", neutral_hue="slate", radius_size=gr.themes.sizes.radius_lg)
custom_css = """
:root { --brand-bg: #0f172a; --brand-accent: #0d9488; --brand-text: #0f172a; --brand-text-light: #ffffff; }
html, body, .gradio-container { height: 100vh; }
.gradio-container { background: var(--brand-bg); display: flex; flex-direction: column; }

/* HERO (landing) */
#hero-wrap { height: 70vh; display: grid; place-items: center; }
#hero { text-align: center; }
#hero h2 { color: #0f172a; font-weight: 800; font-size: 32px; margin-bottom: 22px; }
#hero .search-row { width: min(860px, 92vw); margin: 0 auto; display: flex; gap: 8px; align-items: stretch; }
#hero .search-row .hero-box { flex: 1 1 auto; }
#hero .search-row .hero-box textarea { height: 52px !important; }
#hero-send > button { height: 52px !important; padding: 0 18px !important; border-radius: 12px !important; }
#hero .hint { color: #334155; margin-top: 10px; font-size: 13px; opacity: 0.9; }

/* CHAT */
#chat-container { position: relative; }
.chatbot header, .chatbot .label, .chatbot .label-wrap { 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; }

/* Chat input row equal heights */
#chat-input-row { align-items: stretch; }
#chat-msg textarea { height: 52px !important; }
#chat-send > button, #chat-clear > button { height: 52px !important; padding: 0 18px !important; border-radius: 12px !important; }
"""

# ---------- UI ----------
with gr.Blocks(theme=theme, css=custom_css, analytics_enabled=False) as demo:
    # --- HERO (initial screen) ---
    with gr.Column(elem_id="hero-wrap", visible=True) as hero_wrap:
        with gr.Column(elem_id="hero"):
            gr.HTML("<h2>What scenario can I help you analyze?</h2>")
            with gr.Row(elem_classes="search-row"):
                hero_msg = gr.Textbox(
                    placeholder="Describe your scenario or ask any question (upload files for data analysis)…",
                    show_label=False,
                    lines=1,
                    elem_classes="hero-box"
                )
                hero_send = gr.Button("➤", scale=0, elem_id="hero-send")
            gr.Markdown('<div class="hint">Upload files and describe your scenario for comprehensive analysis. The system will ask clarifying questions, then provide structured insights.</div>')

    # --- MAIN APP (hidden until first message) ---
    with gr.Column(elem_id="chat-container", visible=False) as app_wrap:
        chat = gr.Chatbot(label="", show_label=False, height="80vh")
        with gr.Row():
            uploads = gr.Files(
                label="Upload data files (PDF, DOCX, CSV, PNG, JPG)",
                file_types=["file"], file_count="multiple", height=68
            )
        with gr.Row(elem_id="chat-input-row"):
            msg = gr.Textbox(
                label="",
                show_label=False,
                placeholder="Continue the conversation. Provide additional details or answer clarifying questions.",
                scale=10,
                elem_id="chat-msg",
                lines=1,
            )
            send = gr.Button("Send", scale=1, elem_id="chat-send")
            clear = gr.Button("Clear chat", scale=1, elem_id="chat-clear")

    # ---- State
    state_history = gr.State(value=[])
    state_uploaded = gr.State(value=[])
    state_awaiting = gr.State(value=False)

    # ---- Uploads
    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)

    # ---- Core send (used by both hero input and chat input)
    def _on_send(user_msg, history, up_paths, awaiting):
        try:
            if not user_msg or not user_msg.strip():
                return history, "", history, awaiting
            new_history, new_awaiting = clarityops_reply(
                user_msg.strip(), history or [], None, up_paths or [], awaiting_answers=awaiting
            )
            return new_history, "", new_history, new_awaiting
        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, awaiting

    # ---- Hero -> App transition + first send
    def _hero_start(user_msg, history, up_paths, awaiting):
        chat_o, msg_o, hist_o, await_o = _on_send(user_msg, history, up_paths, awaiting)
        return (
            chat_o, msg_o, hist_o, await_o,
            gr.update(visible=False),
            gr.update(visible=True),
            ""
        )

    hero_send.click(
        _hero_start,
        inputs=[hero_msg, state_history, state_uploaded, state_awaiting],
        outputs=[chat, msg, state_history, state_awaiting, hero_wrap, app_wrap, hero_msg],
        concurrency_limit=2, queue=True
    )
    hero_msg.submit(
        _hero_start,
        inputs=[hero_msg, state_history, state_uploaded, state_awaiting],
        outputs=[chat, msg, state_history, state_awaiting, hero_wrap, app_wrap, hero_msg],
        concurrency_limit=2, queue=True
    )

    # ---- Normal chat interactions after hero is gone
    send.click(_on_send, inputs=[msg, state_history, state_uploaded, state_awaiting],
               outputs=[chat, msg, state_history, state_awaiting],
               concurrency_limit=2, queue=True)
    msg.submit(_on_send, inputs=[msg, state_history, state_uploaded, state_awaiting],
               outputs=[chat, msg, state_history, state_awaiting],
               concurrency_limit=2, queue=True)

    def _on_clear():
        # Clear the in-memory data registry for a fresh scenario
        _data_registry.clear()
        _session_rag.clear()  # Also clear RAG session if available
        return (
            [], "", [], False,
            gr.update(visible=True),
            gr.update(visible=False),
            ""
        )

    clear.click(_on_clear, None, [chat, msg, state_history, state_awaiting, hero_wrap, app_wrap, hero_msg])

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=40)