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

# 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.inputs.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()