import joblib
import gradio as gr
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings, string
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.neighbors import KNeighborsClassifier
# from sklearn.svm import SVC
# from sklearn.linear_model import LogisticRegression
from nltk.corpus import stopwords
import string, nltk
from nltk import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer

nltk.download('wordnet')
nltk.download('omw-1.4')

lemmatizer = WordNetLemmatizer()
stemmer = PorterStemmer()

def clean_text(text):
    nopunc = [w for w in text if w not in string.punctuation]
    nopunc = ''.join(nopunc)
    return  ' '.join([word for word in nopunc.split() if word.lower() not in stopwords.words('english')])
def preprocess(text):
    return ' '.join([word for word in word_tokenize(text) if word not in stopwords.words('english') and not word.isdigit() and word not in string.punctuation])

def stem_words(text):
    return ' '.join([stemmer.stem(word) for word in text.split()])

def lemmatize_words(text):
    return ' '.join([lemmatizer.lemmatize(word) for word in text.split()])

def text_process(review):
    nopunc = [char for char in review if char not in string.punctuation]
    nopunc = ''.join(nopunc)
    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]


### IMPORT ML MODEL; best use for multiple reviews involving the same reviewer or user
print("Running prototype")
loaded_pipeline = joblib.load("trained_fake_review_detector.pkl")
print(loaded_pipeline)
iter_test = ["Hello World"]
print(loaded_pipeline.predict(iter_test))

initialise_gradio = True

if initialise_gradio:
    def classify_review(x):
        iter_x = list(x)
        result = loaded_pipeline.predict(iter_x)

        return result[0]
    iface = gr.Interface(fn = classify_review, 
                        inputs = 'textbox', 
                        outputs = 'label', 
                        title = 'Fake Review Detection',
                        description = 'Input any text that you believe to be fake, this will return an output',
                        article = 
                            '''<div>
                                <p> Hit submit after putting your fake review text to see the results.</p>
                            </div>''',
                        share = True)

    #gr.Markdown("## Text Examples")

    # Adding of examples
    #gr.Examples([["hi", "Adam"], ["hello", "Eve"]], test_fn, cache_examples=True)

    iface.launch(share = True)