"""
AlphaFold Database and UniProt API clients.
All data fetched live from official public APIs.
"""
from __future__ import annotations
import httpx
from typing import Any

ALPHAFOLD_BASE = "https://alphafold.ebi.ac.uk/api"
UNIPROT_BASE = "https://rest.uniprot.org/uniprotkb"
TIMEOUT = 20.0


async def fetch_alphafold_summary(uniprot_id: str) -> dict[str, Any]:
    """Fetch AlphaFold structure summary for a UniProt ID."""
    uid = uniprot_id.strip().upper()
    url = f"{ALPHAFOLD_BASE}/prediction/{uid}"
    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        r = await client.get(url)
        r.raise_for_status()
        data = r.json()
        if isinstance(data, list) and data:
            return data[0]
        return data


async def fetch_uniprot_entry(uniprot_id: str) -> dict[str, Any]:
    """Fetch UniProt entry metadata."""
    uid = uniprot_id.strip().upper()
    url = f"{UNIPROT_BASE}/{uid}.json"
    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        r = await client.get(url)
        r.raise_for_status()
        return r.json()


async def fetch_pae_image_url(uniprot_id: str) -> str | None:
    """Return URL of the predicted aligned error (PAE) image."""
    try:
        summary = await fetch_alphafold_summary(uniprot_id)
        return summary.get("paeImageUrl") or summary.get("pae_image_url")
    except Exception:
        return None


def extract_protein_metadata(uniprot_data: dict) -> dict[str, Any]:
    """Parse UniProt JSON into clean metadata dict."""
    entry = uniprot_data

    # Protein name
    pn = entry.get("proteinDescription", {})
    recommended = pn.get("recommendedName", {})
    full_name = (
        recommended.get("fullName", {}).get("value", "")
        or pn.get("submissionNames", [{}])[0].get("fullName", {}).get("value", "")
    )

    # Gene names
    genes = [g.get("geneName", {}).get("value", "") for g in entry.get("genes", [])]
    gene_str = ", ".join(g for g in genes if g)

    # Organism
    organism = entry.get("organism", {}).get("scientificName", "")

    # Sequence
    seq_data = entry.get("sequence", {})
    sequence = seq_data.get("value", "")
    length = seq_data.get("length", len(sequence))

    # Function annotation
    comments = entry.get("comments", [])
    function_text = ""
    for c in comments:
        if c.get("commentType") == "FUNCTION":
            texts = c.get("texts", [])
            if texts:
                function_text = texts[0].get("value", "")[:400]
                break

    # Keywords for domain classification
    keywords = [kw.get("name", "") for kw in entry.get("keywords", [])]

    # Subcellular location
    location = ""
    for c in comments:
        if c.get("commentType") == "SUBCELLULAR LOCATION":
            locs = c.get("subcellularLocations", [])
            if locs:
                location = locs[0].get("location", {}).get("value", "")
            break

    # Disease associations
    diseases = []
    for c in comments:
        if c.get("commentType") == "DISEASE":
            d = c.get("disease", {})
            if d.get("diseaseId"):
                diseases.append(d["diseaseId"])

    # Infer functional category from keywords
    kw_lower = " ".join(keywords).lower()
    if any(t in kw_lower for t in ["transferase", "hydrolase", "lyase", "kinase", "enzyme"]):
        function_category = "enzyme"
    elif any(t in kw_lower for t in ["receptor", "ligand-bind"]):
        function_category = "receptor"
    elif any(t in kw_lower for t in ["transport", "channel", "carrier"]):
        function_category = "transporter"
    elif any(t in kw_lower for t in ["structural", "cytoskeleton", "collagen"]):
        function_category = "structural"
    elif any(t in kw_lower for t in ["transcription", "dna-binding", "chromatin"]):
        function_category = "transcription"
    elif any(t in kw_lower for t in ["signal", "hormone", "cytokine"]):
        function_category = "signaling"
    else:
        function_category = "unknown"

    return {
        "uniprot_id": entry.get("primaryAccession", ""),
        "protein_name": full_name,
        "gene_name": gene_str,
        "organism": organism,
        "sequence": sequence,
        "length": length,
        "function_text": function_text,
        "function_category": function_category,
        "keywords": keywords[:12],
        "subcellular_location": location,
        "disease_associations": diseases[:6],
    }


async def fetch_plddt_scores(alphafold_data: dict) -> list[float]:
    """
    Fetch per-residue pLDDT scores from AlphaFold confidence JSON file.
    The summary endpoint doesn't include scores inline — they're in a separate file.
    Falls back gracefully if unavailable.
    """
    # Try the confidence JSON URL directly
    conf_url = alphafold_data.get("confidenceUrl") or alphafold_data.get("confidence_url")
    if not conf_url:
        # Construct from entry ID and version
        entry_id = alphafold_data.get("entryId", "")
        version = alphafold_data.get("latestVersion", 4)
        if entry_id:
            conf_url = f"https://alphafold.ebi.ac.uk/files/{entry_id}-confidence_v{version}.json"

    if conf_url:
        try:
            async with httpx.AsyncClient(timeout=TIMEOUT) as client:
                r = await client.get(conf_url)
                r.raise_for_status()
                data = r.json()
                scores = data.get("confidenceScore") or data if isinstance(data, list) else []
                if scores:
                    return [float(s) for s in scores]
        except Exception:
            pass

    # Final fallback: single average value from summary
    avg = (alphafold_data.get("confidenceAvgLocalScore") or
           alphafold_data.get("plddt") or 75.0)
    return [float(avg)]


def extract_plddt_scores(alphafold_data: dict) -> list[float]:
    """Synchronous fallback — use fetch_plddt_scores() for accurate per-residue data."""
    avg = (alphafold_data.get("confidenceAvgLocalScore") or
           alphafold_data.get("plddt") or 75.0)
    return [float(avg)]