import gradio as gr
import os
import json
import numpy as np
from sklearn.feature_extraction.text import (CountVectorizer, TfidfTransformer, HashingVectorizer,
                                             TfidfVectorizer)
from sklearn.linear_model import LogisticRegression
from lr.hyperparameters import SEARCH_SPACE, RandomSearch, HyperparameterSearch

def load_model(serialization_dir):
    with open(os.path.join(serialization_dir, "best_hyperparameters.json"), 'r') as f:
        hyperparameters = json.load(f)
    if hyperparameters.pop('stopwords') == 1:
        stop_words = 'english'
    else:
        stop_words = None
    weight = hyperparameters.pop('weight')
    if weight == 'binary':
        binary = True
    else:
        binary = False
    ngram_range = hyperparameters.pop('ngram_range')
    ngram_range = sorted([int(x) for x in ngram_range.split()])
    if weight == 'tf-idf':
        vect = TfidfVectorizer(stop_words=stop_words,
                               lowercase=True,
                               ngram_range=ngram_range)
    elif weight == 'hash':
        vect = HashingVectorizer(stop_words=stop_words,lowercase=True,ngram_range=ngram_range)
    else:
        vect = CountVectorizer(binary=binary,
                               stop_words=stop_words,
                               lowercase=True,
                               ngram_range=ngram_range)
    if weight != "hash":
        with open(os.path.join(serialization_dir, "vocab.json"), 'r') as f:
            vocab = json.load(f)
        vect.vocabulary_ = vocab
    hyperparameters['C'] = float(hyperparameters['C'])
    hyperparameters['tol'] = float(hyperparameters['tol'])
    classifier = LogisticRegression(**hyperparameters)
    if os.path.exists(os.path.join(serialization_dir, "archive", "idf.npy")):
        vect.idf_ = np.load(os.path.join(serialization_dir,  "archive", "idf.npy"))
    classifier.coef_ = np.load(os.path.join(serialization_dir,  "archive", "coef.npy"))
    classifier.intercept_ = np.load(os.path.join(serialization_dir,  "archive", "intercept.npy"))
    classifier.classes_ = np.load(os.path.join(serialization_dir,  "archive", "classes.npy"))
    return classifier, vect

def score(x, clf, vectorizer):
    # score a single document
    return clf.predict_proba(vectorizer.transform([x]))

clf, vectorizer = load_model("model/")

def start(text):
    k = round(score(text, clf, vectorizer)[0][1], 2)
    return {"GPT-3 Filter Quality Score": k }