"""Hugging Face Model Ecosystem Collector

Collects data from Hugging Face model hub.
https://huggingface.co/models
https://huggingface.co/datasets
https://huggingface.co/spaces
"""

from typing import Dict, Any, List
from datetime import datetime

from app.services.collectors.base import HTTPCollector


class HuggingFaceModelCollector(HTTPCollector):
    name = "huggingface_models"
    priority = "P1"
    module = "L2"
    frequency_hours = 12
    data_type = "model"
    base_url = "https://huggingface.co/api/models"

    def parse_response(self, response: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Parse Hugging Face models API response"""
        data = []
        models = (
            response
            if isinstance(response, list)
            else response.get("models", response.get("items", []))
        )

        for item in models[:100]:
            try:
                entry = {
                    "source_id": f"hf_model_{item.get('id', '')}",
                    "name": item.get("id", "Unknown"),
                    "description": (item.get("description", "") or "")[:500],
                    "metadata": {
                        "author": item.get("author"),
                        "likes": item.get("likes"),
                        "downloads": item.get("downloads"),
                        "language": item.get("language"),
                        "tags": (item.get("tags", []) or [])[:10],
                        "pipeline_tag": item.get("pipeline_tag"),
                        "library_name": item.get("library_name"),
                        "created_at": item.get("createdAt"),
                    },
                    "reference_date": datetime.utcnow().strftime("%Y-%m-%d"),
                }
                data.append(entry)
            except (ValueError, TypeError, KeyError):
                continue

        return data


class HuggingFaceDatasetCollector(HTTPCollector):
    name = "huggingface_datasets"
    priority = "P1"
    module = "L2"
    frequency_hours = 12
    data_type = "dataset"
    base_url = "https://huggingface.co/api/datasets"

    def parse_response(self, response: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Parse Hugging Face datasets API response"""
        data = []
        datasets = (
            response
            if isinstance(response, list)
            else response.get("datasets", response.get("items", []))
        )

        for item in datasets[:100]:
            try:
                entry = {
                    "source_id": f"hf_dataset_{item.get('id', '')}",
                    "name": item.get("id", "Unknown"),
                    "description": (item.get("description", "") or "")[:500],
                    "metadata": {
                        "author": item.get("author"),
                        "likes": item.get("likes"),
                        "downloads": item.get("downloads"),
                        "size": item.get("size"),
                        "language": item.get("language"),
                        "tags": (item.get("tags", []) or [])[:10],
                        "created_at": item.get("createdAt"),
                    },
                    "reference_date": datetime.utcnow().strftime("%Y-%m-%d"),
                }
                data.append(entry)
            except (ValueError, TypeError, KeyError):
                continue

        return data


class HuggingFaceSpacesCollector(HTTPCollector):
    name = "huggingface_spaces"
    priority = "P2"
    module = "L2"
    frequency_hours = 24
    data_type = "space"
    base_url = "https://huggingface.co/api/spaces"

    def parse_response(self, response: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Parse Hugging Face Spaces API response"""
        data = []
        spaces = (
            response
            if isinstance(response, list)
            else response.get("spaces", response.get("items", []))
        )

        for item in spaces[:100]:
            try:
                entry = {
                    "source_id": f"hf_space_{item.get('id', '')}",
                    "name": item.get("id", "Unknown"),
                    "description": (item.get("description", "") or "")[:500],
                    "metadata": {
                        "author": item.get("author"),
                        "likes": item.get("likes"),
                        "views": item.get("views"),
                        "sdk": item.get("sdk"),
                        "hardware": item.get("hardware"),
                        "tags": (item.get("tags", []) or [])[:10],
                        "created_at": item.get("createdAt"),
                    },
                    "reference_date": datetime.utcnow().strftime("%Y-%m-%d"),
                }
                data.append(entry)
            except (ValueError, TypeError, KeyError):
                continue

        return data