import inspect
import logging
import statistics
from dataclasses import asdict
from datetime import timedelta
from typing import List

import requests_cache
from flask import Flask, jsonify

from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour
from .util import Context

logger = logging.getLogger(__name__)
app = Flask(__name__)

requests_cache.install_cache("requests_cache", expire_after=timedelta(minutes=10))


strategies = {
    # name: (weight, probability function)
    "tv2news": miloStrats.tv2newsStrat,
    "australia": miloStrats.australiaStrat,
    "camera": miloStrats.camImgStrat,
    "iss": iss.night_on_iss,
    "cars_in_traffic": cars_in_traffic.cars_in_traffic,
    "tide": tide_strat.is_tide,
    "upstairs_neighbour": upstairs_neighbour.check_games,
}


@app.route("/", methods=["GET", "POST"])
def probabilities():
    phone_data = {}  # TODO: get from POST request
    context = Context(**phone_data)

    predictions: List[dict] = []
    for name, strategy in strategies.items():
        try:
            prediction = strategy(context)
        except Exception as e:
            logger.warning("Strategy %s failed: %s", name, e)
            logger.exception(e)
            continue
        predictions.append({
            "name": name,
            "description": inspect.getdoc(strategy),
            "weight": prediction.weight,
            "weighted_probability": prediction.probability * prediction.weight,
            "night": prediction.probability > 0.5,
            **asdict(prediction),
        })

    mean = statistics.mean(p["weighted_probability"] for p in predictions)
    median = statistics.median(p["weighted_probability"] for p in predictions)
    night = mean > 0.5

    # Invert if we're in Australia
    if context.in_australia:
        night = not night
        for prediction in predictions:
            prediction["night"] = not prediction["night"]

    # Calculate contributions of predictions
    consensus_weight_sum = sum(p["weight"] for p in predictions if p["night"] == night)
    for prediction in predictions:
        # If this prediction agrees with the consensus it contributed
        if prediction["night"] == night:
            prediction["contribution"] = prediction["weight"] / consensus_weight_sum
        else:
            prediction["contribution"] = 0.0

    return jsonify({
        "predictions": predictions,
        "weighted_probabilities_mean": mean,
        "weighted_probabilities_median": median,
        "night": night,
    })


def main():
    app.run(host='0.0.0.0')


if __name__ == '__main__':
    main()