import numpy as np
import pandas as pd
import streamlit as st
import uncurl
from calibration import overconfidence
from firebase_requests import get_forecasts, get_resolutions
from gjo_requests import get_resolved_questions
from plotting import plotly_calibration, plotly_calibration_odds

if __name__ == "__main__":

    st.title("Learn how calibrated are you?")

    # ---

    # if st.checkbox('I am new! Show me instructions.'):
    #     st.write("""
    #         Hey!
    #     """)

    # ---

    platform = st.selectbox(
        "Which platform are you using?",
        ["Good Judgement Open", "CSET Foretell"],
    )
    platform_url = {
        "Good Judgement Open": "https://www.gjopen.com",
        "CSET Foretell": "https://www.cset-foretell.com",
    }[platform]

    uid = st.number_input("What is your user ID?", min_value=1, value=28899)
    uid = str(uid)

    curl_value = ""

    curl_command = st.text_area(
        "Ugh... Gimme your cURL info...", value=curl_value.strip()
    )
    curl_content = uncurl.parse_context(curl_command)
    headers, cookies = curl_content.headers, curl_content.cookies

    # ---

    questions = get_resolved_questions(uid, platform_url, headers, cookies)

    st.write(f"{len(questions)} questions you forecasted on have resolved.")

    # ---
    # TODO: Make a progress bar..?

    forecasts = get_forecasts(uid, questions, platform_url, headers, cookies)
    resolutions = get_resolutions(questions, platform_url, headers, cookies)

    # ---

    num_forecasts = sum(len(f) for f in forecasts.values())
    st.write(
        f"On these {len(questions)} questions you've made {num_forecasts} forecasts."
    )

    flatten = lambda t: [item for sublist in t for item in sublist]
    y_true = flatten(resolutions[q]["y_true"] for q in questions for _ in forecasts[q])
    y_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q])

    # Note that I am "double counting" each prediction.
    if st.checkbox("Drop last"):
        y_true = flatten(
            resolutions[q]["y_true"][:-1] for q in questions for _ in forecasts[q]
        )
        y_pred = flatten(f["y_pred"][:-1] for q in questions for f in forecasts[q])

    y_true, y_pred = np.array(y_true), np.array(y_pred)

    st.write(f"Which gives us {len(y_pred)} datapoints to work with.")

    # ---

    strategy = st.selectbox(
        "Which binning stranegy do you prefer?",
        ["uniform", "quantile"],
    )

    recommended_n_bins = int(np.sqrt(len(y_pred))) if strategy == "quantile" else 20 + 1
    n_bins = st.number_input(
        "How many bins do you want me to display?",
        min_value=1,
        value=recommended_n_bins,
    )

    fig = plotly_calibration(y_true, y_pred, n_bins=n_bins, strategy=strategy)
    st.plotly_chart(fig, use_container_width=True)

    overconf = overconfidence(y_true, y_pred)
    st.write(f"Your over/under- confidence score is {overconf:.2f}.")

    # ---

    fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy)
    st.plotly_chart(fig, use_container_width=True)