import csv


# Profile field → (csv_column, weight)
FIELD_MAP = [
    ("clinical",      "primary_focus",      "primary_focus",       3),
    ("clinical",      "substances",         "substances",          3),
    ("demographics",  "population",         "age_groups",          2),
    ("demographics",  "identity_factors",   "identity_factors",    2),
    ("logistics",     "insurance",          "insurance",           2),
    ("preferences",   "setting",            "settings",            2),
    ("preferences",   "therapy_approach",   "therapy_approaches",  1),
    ("demographics",  "language",           "languages",           1),
]


def load_resources(csv_path):
    """Load one or more resource CSVs into a list of dicts.

    Accepts a single path (str) or a list of paths. Called once at init.
    """
    if isinstance(csv_path, str):
        csv_path = [csv_path]
    rows = []
    for path in csv_path:
        with open(path, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            rows.extend(reader)
    return rows


def _get_profile_value(profile, category, field):
    """Safely get a profile value, returning None for missing/empty."""
    val = profile.get(category, {}).get(field)
    if val is None:
        return None
    if isinstance(val, list) and len(val) == 0:
        return None
    return val


def _pipe_values(cell):
    """Split a pipe-delimited CSV cell into a set of lowercase values."""
    if not cell or not cell.strip():
        return set()
    return {v.strip().lower() for v in cell.split("|")}


def filter_resources(resources, user_profile):
    """
    Filter the full resource list down to a relevant subset based on
    user profile values. Applies geographic, primary_focus, and substances
    filters. Progressively relaxes filters if fewer than 3 results remain.
    """
    zipcode = _get_profile_value(user_profile, "logistics", "zipcode")
    region = _get_profile_value(user_profile, "logistics", "region")
    primary_focus = _get_profile_value(user_profile, "clinical", "primary_focus")
    substances = _get_profile_value(user_profile, "clinical", "substances")

    # No profile info → no filtering possible, return empty (no recommendations)
    if not zipcode and not region and not primary_focus and not substances:
        return []

    # Build filter functions in order of relaxation priority
    filters = []

    # Geographic filter (relaxed first if too few results)
    if zipcode:
        zip_prefix = zipcode[:3]
        filters.append(("geo", lambda r, zp=zip_prefix: (
            r.get("zip", "")[:3] == zp
        )))
    elif region:
        region_lower = region.lower()
        filters.append(("geo", lambda r, rl=region_lower: (
            rl in r.get("city", "").lower() or rl in r.get("state", "").lower()
        )))

    # Primary focus filter
    if primary_focus:
        focus_lower = primary_focus.lower()
        filters.append(("focus", lambda r, fl=focus_lower: (
            not r.get("primary_focus", "").strip() or
            fl in _pipe_values(r.get("primary_focus", ""))
        )))

    # Substances filter
    if substances:
        if isinstance(substances, str):
            substances = [substances]
        subs_lower = {s.lower() for s in substances}
        filters.append(("substances", lambda r, sl=subs_lower: (
            not r.get("substances", "").strip() or
            bool(sl & _pipe_values(r.get("substances", "")))
        )))

    # Apply all filters, progressively relax if < 3 results
    result = _apply_filters(resources, filters)
    if len(result) >= 3:
        return result
    best = result  # keep the best partial matches found so far

    # Relax geographic filter first
    relaxed = [f for f in filters if f[0] != "geo"]
    if relaxed:
        result = _apply_filters(resources, relaxed)
        if len(result) >= 3:
            return result
        if len(result) > len(best):
            best = result

    # Relax substances filter next
    relaxed = [f for f in relaxed if f[0] != "substances"]
    if relaxed:
        result = _apply_filters(resources, relaxed)
        if len(result) > len(best):
            best = result

    return best


def _apply_filters(resources, filters):
    """Apply a list of filter functions, keeping rows that pass all."""
    if not filters:
        return []
    result = []
    for row in resources:
        if all(fn(row) for _, fn in filters):
            result.append(row)
    return result


def score_resources(filtered, user_profile, top_n=3):
    """
    Score filtered resources by relevance to the user profile.
    Returns the top_n highest-scoring resources as a list of dicts.
    """
    zipcode = _get_profile_value(user_profile, "logistics", "zipcode")
    region = _get_profile_value(user_profile, "logistics", "region")

    scored = []
    for row in filtered:
        score = 0

        # Score each mapped field
        for category, field, csv_col, weight in FIELD_MAP:
            profile_val = _get_profile_value(user_profile, category, field)
            if profile_val is None:
                continue

            cell_values = _pipe_values(row.get(csv_col, ""))
            if not cell_values:
                continue  # empty cell = neutral

            if isinstance(profile_val, list):
                matches = sum(1 for v in profile_val if v.lower() in cell_values)
                if matches > 0:
                    score += weight * (matches / len(profile_val))
            else:
                if profile_val.lower() in cell_values:
                    score += weight

        # Geographic bonus
        row_zip = row.get("zip", "").strip()
        if zipcode and row_zip:
            if row_zip == zipcode:
                score += 5
            elif row_zip[:3] == zipcode[:3]:
                score += 2
        elif region and not zipcode:
            region_lower = region.lower()
            if region_lower in row.get("city", "").lower():
                score += 3

        if score > 0:
            scored.append((score, row))

    # Sort by score descending
    scored.sort(key=lambda x: x[0], reverse=True)
    return [row for _, row in scored[:top_n]]


def format_resources_for_context(results):
    """
    Format a list of resource dicts as a context block for injection into the
    system prompt. The LLM uses this verified data to present recommendations
    naturally in its own voice. Returns empty string if no results.
    """
    if not results:
        return ""

    lines = [
        "[VERIFIED FACILITY DATA — Present these facilities to the user following the "
        "output format in your instructions. Use only the data listed here — do not invent, "
        "alter, or supplement with facilities not in this list.]",
        "",
    ]

    for i, row in enumerate(results, 1):
        name = row.get("name", "Unknown Facility")
        lines.append(f"Facility {i}: {name}")

        parts = [row.get("address", ""), row.get("city", ""),
                 row.get("state", ""), row.get("zip", "")]
        address = ", ".join(p.strip() for p in parts if p.strip())
        if address:
            lines.append(f"  Address: {address}")

        phone = row.get("phone", "").strip()
        if phone:
            lines.append(f"  Phone: {phone}")

        website = row.get("website", "").strip()
        if website:
            lines.append(f"  Website: {website}")

        focus = row.get("primary_focus", "").strip()
        if focus:
            lines.append("  Focus: " + ", ".join(
                v.strip().replace("_", " ").title() for v in focus.split("|")
            ))

        subs = row.get("substances", "").strip()
        if subs:
            lines.append("  Substances: " + ", ".join(
                v.strip().replace("_", " ").title() for v in subs.split("|")
            ))

        settings = row.get("settings", "").strip()
        if settings:
            lines.append("  Settings: " + ", ".join(
                v.strip().replace("_", " ").title() for v in settings.split("|")
            ))

        insurance = row.get("insurance", "").strip()
        if insurance:
            lines.append("  Insurance: " + ", ".join(
                v.strip().replace("_", " ").title() for v in insurance.split("|")
            ))

        lines.append("")

    return "\n".join(lines).rstrip()