import gradio as gr
import pandas as pd
import numpy as np
import re
import pickle
import nltk
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from sklearn.preprocessing import LabelEncoder
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import inflect

# Ensure NLTK stopwords and wordnet are downloaded
nltk.download('stopwords')
nltk.download('wordnet')

# Load the tokenizer, label encoder, and model
def load_resources():
    tokenizer = AutoTokenizer.from_pretrained('./transformer_tokenizer')
    with open('./label_encoder_tf.pickle', 'rb') as handle:
        encoder = pickle.load(handle)
    model = TFAutoModelForSequenceClassification.from_pretrained('./transformer_model')
    return tokenizer, encoder, model

tokenizer, encoder, model = load_resources()

# Preprocessing functions
def expand_contractions(text, contractions_dict):
    contractions_pattern = re.compile('({})'.format('|'.join(contractions_dict.keys())), flags=re.IGNORECASE | re.DOTALL)
    def expand_match(contraction):
        match = contraction.group(0)
        first_char = match[0]
        expanded_contraction = contractions_dict.get(match.lower(), match)
        return first_char + expanded_contraction[1:]
    expanded_text = contractions_pattern.sub(expand_match, text)
    return re.sub("'", "", expanded_text)

def convert_numbers_to_words(text):
    p = inflect.engine()
    words = text.split()
    return ' '.join([p.number_to_words(word) if word.isdigit() else word for word in words])

def preprocess_text(text):
    contractions_dict = {
        "ain't": "am not", "aren't": "are not", "can't": "cannot", "can't've": "cannot have", "'cause": "because",
        "could've": "could have", "couldn't": "could not", "couldn't've": "could not have", "didn't": "did not",
        "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hadn't've": "had not have", "hasn't": "has not",
        "haven't": "have not", "he'd": "he had", "he'd've": "he would have", "he'll": "he will", "he'll've": "he will have",
        "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",
        "I'd": "I had", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have", "I'm": "I am", "I've": "I have",
        "isn't": "is not", "it'd": "it had", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have",
        "it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have", "mightn't": "might not",
        "mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have",
        "needn't": "need not", "needn't've": "need not have", "o'clock": "of the clock", "oughtn't": "ought not",
        "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have",
        "she'd": "she had", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is",
        "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have",
        "so's": "so is", "that'd": "that had", "that'd've": "that would have", "that's": "that is", "there'd": "there had",
        "there'd've": "there would have", "there's": "there is", "they'd": "they had", "they'd've": "they would have",
        "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have",
        "wasn't": "was not", "we'd": "we had", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have",
        "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have",
        "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have",
        "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have",
        "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not",
        "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have",
        "y'all": "you all", "y'all'd": "you all would", "y'all'd've": "you all would have", "y'all're": "you all are",
        "y'all've": "you all have", "you'd": "you had", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have",
        "you're": "you are", "you've": "you have"
    }
    text = text.lower()
    text = expand_contractions(text, contractions_dict)
    text = convert_numbers_to_words(text)
    text = re.sub(r'[^\w\s]', '', text)
    stop_words = set(stopwords.words('english'))
    text = ' '.join([word for word in text.split() if word not in stop_words])
    lemmatizer = WordNetLemmatizer()
    text = ' '.join([lemmatizer.lemmatize(word) for word in text.split()])
    return text

# Define the prediction function
def predict_spam(text):
    preprocessed_text = preprocess_text(text)
    encoding = tokenizer(preprocessed_text, return_tensors='tf', truncation=True, padding=True)
    prediction = model(encoding).logits
    predicted_label = np.argmax(prediction, axis=1)
    decoded_label = encoder.inverse_transform(predicted_label)
    return decoded_label[0]

# Create the Gradio interface
iface = gr.Interface(fn=predict_spam, 
                     inputs=gr.Textbox(lines=2, placeholder="Enter SMS message here..."), 
                     outputs="text", 
                     title="SMS Spam Classification with Transformer Model", 
                     description="Enter an SMS message to classify it as spam or ham.")

# Launch the interface
iface.launch(share=True)