"""Save session state to local disk and upload JSON + flattened CSV to HuggingFace."""
import csv
import json
import os
import tempfile
import uuid
from datetime import datetime
from pathlib import Path

import streamlit as st
from huggingface_hub import HfApi
from src.data import release_reservation, record_completion


@st.cache_resource
def _get_hf_api(hf_token: str, output_repo: str) -> HfApi:
    """Initialise HF API client and ensure the output repo exists."""
    api = HfApi(token=hf_token) if hf_token else HfApi()
    if hf_token:
        try:
            api.repo_info(repo_id=output_repo, repo_type="dataset")
        except Exception as e:
            if "404" in str(e) or "not found" in str(e).lower():
                api.create_repo(repo_id=output_repo, repo_type="dataset", private=True)
                print(f"[HF] Created output repo: {output_repo}")
            else:
                print(f"[HF] Warning checking repo existence: {e}")
    return api


def _hf_study_identity_snapshot(state: dict, cfg: dict) -> dict:
    """
    Compact, non-secret metadata duplicated into every HF JSON export so files
    are self-describing (especially which YAML model entry each participant saw).
    """
    snap: dict = {
        "study_type":          cfg.get("study_type"),
        "pair_selection_seed": cfg.get("pair_selection_seed"),
        "categories":          cfg.get("categories"),
        "pairs_per_user":      cfg.get("pairs_per_user"),
        "min_turns":           cfg.get("min_turns"),
        "max_turns":           cfg.get("max_turns"),
        "prolific_study_id":   cfg.get("prolific_study_id"),
        "output_dataset_repo": cfg.get("output_dataset_repo"),
    }
    if cfg.get("study_type") != "model_comparison":
        return snap
    snap.update(
        {
            "submission_id": state.get("submission_id"),
            "session_user_id": state.get("user_id"),
            "comparison_models_canonical": state.get("comparison_models_canonical"),
            "model_presentation_order_config_indices": state.get(
                "model_presentation_order_config_indices"
            ),
            "model_identity_timeline": state.get("model_identity_timeline"),
            "comparison_final": state.get("comparison_final"),
        }
    )
    return snap


def save_and_upload(state: dict, cfg: dict) -> None:
    """Write the full JSON to disk, then upload JSON + flattened CSV to HuggingFace."""
    output_repo   = cfg["output_dataset_repo"]
    hf_token      = cfg.get("hf_token", "")
    hf_api        = _get_hf_api(hf_token, output_repo)

    worker_id     = state.get("prolific_pid") or state.get("user_id", "anonymous")
    submission_id = state.get("submission_id", str(uuid.uuid4()))
    safe_worker   = "".join(c if c.isalnum() else "_" for c in str(worker_id))

    print(f"[SAVE] starting save_and_upload")
    print(f"[SAVE] output_repo={output_repo}")
    print(f"[SAVE] hf_token set={bool(hf_token)}")

    # ── Write JSON ────────────────────────────────────────────────────────────
    ann_dir   = Path(cfg["annotations_dir"]) / safe_worker
    ann_dir.mkdir(parents=True, exist_ok=True)
    json_path = ann_dir / f"{submission_id}.json"

    export_state = dict(state)
    export_state["hf_study_identity"] = _hf_study_identity_snapshot(state, cfg)

    with open(json_path, "w") as f:
        json.dump(export_state, f, indent=2)
    print(f"[SAVE] JSON written: {json_path}")

    uploaded = False
    if hf_token:
        try:
            hf_api.upload_file(
                path_or_fileobj=str(json_path),
                path_in_repo=f"json/{safe_worker}/{submission_id}.json",
                repo_id=output_repo,
                repo_type="dataset",
            )
            print("[HF] JSON uploaded.")
            uploaded = True
        except Exception as e:
            print(f"[HF] JSON upload error: {e}")

    if uploaded:
        # Release reservations so items are immediately available for re-assignment
        release_reservation(state.get("user_id", ""), cfg)
        # Record completion locally — updates counts immediately without waiting
        # for an HF re-scan. Also invalidates the HF cache.
        record_completion(state.get("user_id", ""), state.get("items", []), cfg)
        # Auto-pause Prolific study if all items are now covered
        try:
            from src.data import all_items_covered, pause_prolific_study
            if all_items_covered(cfg):
                pause_prolific_study(cfg)
        except Exception as e:
            print(f"[SAVE] Auto-pause check failed: {e}")

    # ── Write + upload CSV ────────────────────────────────────────────────────
    _save_and_upload_csv(state, cfg, hf_api, safe_worker, submission_id)


# ── CSV schema ────────────────────────────────────────────────────────────────

_COMMON_HEADER = [
    "submission_id", "prolific_pid", "study_id", "session_id",
    "submission_time", "duration_seconds",
    "study_type", "model_name",
    "prompt_personalization", "prompt_detailed_instruction",
    "pair_selection_seed", "category",
    # Demographics
    "age", "gender", "geographic_region", "education_level", "race",
    "us_citizen", "marital_status", "religion", "religious_attendance",
    "political_affiliation", "income", "political_views",
    "household_size", "employment_status",
    # Background
    "movies_criteria", "movies_enjoy", "movies_avoid",
    "groceries_criteria", "groceries_enjoy", "groceries_avoid",
    # Ratings
    "pre_rating", "post_rating", "rating_delta",
    # Conversation
    "num_turns", "conversation_json",
    # Reflection
    "standout_moment", "thinking_change",
]

_PREFERENCE_EXTRA_HEADER = [
    "pair_index", "pair_id",
    "product_a_id", "product_a_title", "product_a_price",
    "product_b_id", "product_b_title", "product_b_price",
    "familiarity_a", "familiarity_b",
]

_LIKELIHOOD_EXTRA_HEADER = [
    "item_index", "item_id",
    "product_title", "product_price",
    "familiarity",
]

_MODEL_COMPARISON_HEADER = [
    "submission_id", "prolific_pid", "study_id", "session_id",
    "submission_time", "duration_seconds",
    "study_type", "pair_selection_seed",
    "comparison_models_canonical_json",
    "model_presentation_order_config_indices_json",
    "model_identity_timeline_json",
    "comparison_round_index", "user_model_label", "variant_key",
    "config_index", "config_variant_name",
    "model_name", "sampler_path",
    "use_demographics", "use_background", "prompt_personalization", "prompt_detailed_instruction",
    "category",
    "age", "gender", "geographic_region", "education_level", "race",
    "us_citizen", "marital_status", "religion", "religious_attendance",
    "political_affiliation", "income", "political_views",
    "household_size", "employment_status",
    "movies_criteria", "movies_enjoy", "movies_avoid",
    "groceries_criteria", "groceries_enjoy", "groceries_avoid",
    "pair_id",
    "product_a_id", "product_a_title", "product_a_price",
    "product_b_id", "product_b_title", "product_b_price",
    "familiarity_a", "familiarity_b", "pre_rating",
    "post_convincing", "post_more_likely_buy_target", "post_trustworthy_natural",
    "post_stood_out",
    "num_turns", "conversation_json",
    "final_preferred_user_label",
    "final_preferred_config_index", "final_preferred_variant_key",
    "final_rank_convincing_labels_json",
    "final_rank_buy_target_labels_json",
    "final_rank_convincing_config_indices_json",
    "final_rank_buy_target_config_indices_json",
    "user_label_to_config_index_json",
]


def _save_and_upload_csv(
    state: dict, cfg: dict, hf_api: HfApi, safe_worker: str, submission_id: str
) -> None:
    study_type   = cfg["study_type"]
    demographics = state.get("demographics", {})
    background   = state.get("background",   {})
    items        = state.get("items", [])

    if study_type == "model_comparison":
        item = items[0] if items else {}
        fin  = state.get("comparison_final") or {}
        pref = fin.get("preferred_user_label", "")
        pref_ci = fin.get("preferred_config_index", "")
        pref_vk = fin.get("preferred_variant_key", "")
        rank_c_labels = json.dumps(fin.get("rank_convincing_labels", []))
        rank_b_labels = json.dumps(fin.get("rank_buy_target_labels", []))
        rank_c_ci = json.dumps(fin.get("rank_convincing_config_indices", []))
        rank_b_ci = json.dumps(fin.get("rank_buy_target_config_indices", []))
        label_map = json.dumps(fin.get("user_label_to_config_index", {}))

        canon_j = json.dumps(state.get("comparison_models_canonical", []))
        pres_j  = json.dumps(state.get("model_presentation_order_config_indices", []))
        time_j  = json.dumps(state.get("model_identity_timeline", []))

        pa, pb = item.get("product_a", {}), item.get("product_b", {})

        rows: list[list] = []
        for ridx, mr in enumerate(state.get("model_rounds", [])):
            mconf = mr.get("config", {})
            conv  = mr.get("conversation", {})
            pr    = mr.get("post_chat_ratings") or {}
            pv_eff = {
                "personalization": mconf.get(
                    "personalization",
                    mconf.get("use_demographics", False) or mconf.get("use_background", False),
                ),
                # Not passed to lsp get_seller_system_prompt; optional YAML audit only.
                "detailed_instruction": mconf.get("detailed_instruction", ""),
            }
            rows.append([
                submission_id,
                state.get("prolific_pid", ""),
                state.get("study_id", ""),
                state.get("session_id", ""),
                state.get("meta", {}).get("submission_time", ""),
                state.get("meta", {}).get("duration_seconds", ""),
                study_type,
                cfg.get("pair_selection_seed", 42),
                canon_j,
                pres_j,
                time_j,
                ridx,
                mr.get("user_label", ""),
                mr.get("variant_key", ""),
                mr.get("config_index", ""),
                mconf.get("name", ""),
                mconf.get("model_name", ""),
                mconf.get("sampler_path", ""),
                mconf.get("use_demographics", False),
                mconf.get("use_background", False),
                pv_eff["personalization"],
                pv_eff["detailed_instruction"],
                item.get("category", ""),
                demographics.get("age", ""),
                demographics.get("gender", ""),
                demographics.get("geographic_region", ""),
                demographics.get("education_level", ""),
                demographics.get("race", ""),
                demographics.get("us_citizen", ""),
                demographics.get("marital_status", ""),
                demographics.get("religion", ""),
                demographics.get("religious_attendance", ""),
                demographics.get("political_affiliation", ""),
                demographics.get("income", ""),
                demographics.get("political_views", ""),
                demographics.get("household_size", ""),
                demographics.get("employment_status", ""),
                background.get("movies_criteria", ""),
                background.get("movies_enjoy", ""),
                background.get("movies_avoid", ""),
                background.get("groceries_criteria", ""),
                background.get("groceries_enjoy", ""),
                background.get("groceries_avoid", ""),
                item.get("pair_id", ""),
                pa.get("id", ""), pa.get("title", ""), pa.get("price", ""),
                pb.get("id", ""), pb.get("title", ""), pb.get("price", ""),
                item.get("familiarity_a", ""),
                item.get("familiarity_b", ""),
                item.get("pre_rating", ""),
                pr.get("convincing", ""),
                pr.get("more_likely_buy_target", ""),
                pr.get("trustworthy_natural", ""),
                pr.get("stood_out", ""),
                conv.get("num_turns", 0),
                json.dumps(conv.get("turns", [])),
                pref,
                pref_ci,
                pref_vk,
                rank_c_labels,
                rank_b_labels,
                rank_c_ci,
                rank_b_ci,
                label_map,
            ])

        header = _MODEL_COMPARISON_HEADER
    else:
        header = _COMMON_HEADER + (
            _PREFERENCE_EXTRA_HEADER if study_type == "preference"
            else _LIKELIHOOD_EXTRA_HEADER
        )
        rows = []

        for i, item in enumerate(items):
            conv  = item.get("conversation", {})
            refl  = item.get("reflection",   {})
            pre   = item.get("pre_rating",   "")
            post  = item.get("post_rating",  "")
            delta = (post - pre) if isinstance(pre, int) and isinstance(post, int) else ""
            pv    = item.get("prompt_variant", {})

            common = [
                submission_id,
                state.get("prolific_pid", ""),
                state.get("study_id",     ""),
                state.get("session_id",   ""),
                state.get("meta", {}).get("submission_time",  ""),
                state.get("meta", {}).get("duration_seconds", ""),
                study_type,
                item.get("model_name", ""),
                pv.get("personalization",      False),
                pv.get("detailed_instruction", True),
                cfg.get("pair_selection_seed", 42),
                item.get("category", ""),
                demographics.get("age",                  ""),
                demographics.get("gender",               ""),
                demographics.get("geographic_region",    ""),
                demographics.get("education_level",      ""),
                demographics.get("race",                 ""),
                demographics.get("us_citizen",           ""),
                demographics.get("marital_status",       ""),
                demographics.get("religion",             ""),
                demographics.get("religious_attendance", ""),
                demographics.get("political_affiliation",""),
                demographics.get("income",               ""),
                demographics.get("political_views",      ""),
                demographics.get("household_size",       ""),
                demographics.get("employment_status",    ""),
                background.get("movies_criteria",    ""),
                background.get("movies_enjoy",        ""),
                background.get("movies_avoid",        ""),
                background.get("groceries_criteria",  ""),
                background.get("groceries_enjoy",     ""),
                background.get("groceries_avoid",     ""),
                pre, post, delta,
                conv.get("num_turns", 0),
                json.dumps(conv.get("turns", [])),
                refl.get("standout_moment", ""),
                refl.get("thinking_change",  ""),
            ]

            if study_type == "preference":
                pa, pb = item.get("product_a", {}), item.get("product_b", {})
                extra = [
                    i + 1,
                    item.get("pair_id", ""),
                    pa.get("id",    ""), pa.get("title", ""), pa.get("price", ""),
                    pb.get("id",    ""), pb.get("title", ""), pb.get("price", ""),
                    item.get("familiarity_a", ""),
                    item.get("familiarity_b", ""),
                ]
            else:
                prod = item.get("product", {})
                extra = [
                    i + 1,
                    item.get("item_id", ""),
                    prod.get("title", ""), prod.get("price", ""),
                    item.get("familiarity", ""),
                ]

            rows.append(common + extra)

    timestamp  = datetime.now().strftime("%Y%m%d_%H%M%S")
    unique_tag = uuid.uuid4().hex[:8]
    repo_path  = f"csv/{timestamp}_{safe_worker}_{unique_tag}.csv"

    with tempfile.NamedTemporaryFile(
        mode="w", suffix=".csv", delete=False, newline="", encoding="utf-8"
    ) as tmp:
        tmp_path = tmp.name
        writer   = csv.writer(tmp)
        writer.writerow(header)
        writer.writerows(rows)

    if cfg.get("hf_token"):
        try:
            hf_api.upload_file(
                path_or_fileobj=tmp_path,
                path_in_repo=repo_path,
                repo_id=cfg["output_dataset_repo"],
                repo_type="dataset",
            )
            print("[HF] CSV uploaded.")
        except Exception as e:
            print(f"[HF] CSV upload error: {e}")

    os.unlink(tmp_path)