import torch
import torch.nn as nn
from transformers import T5Tokenizer, T5EncoderModel
import gradio as gr
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# === NLTK Downloads ===
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')

# === Preprocessing Function ===
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_text(text):
    text = re.sub(r'[^A-Za-z\s]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    text = text.lower()
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words]
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    return ' '.join(tokens)

# === Model Definition ===
class T5Classifier(nn.Module):
    def __init__(self, model_name='t5-small', num_labels=2):
        super(T5Classifier, self).__init__()
        self.encoder = T5EncoderModel.from_pretrained(model_name)
        self.classifier = nn.Linear(self.encoder.config.d_model, num_labels)

    def forward(self, input_ids, attention_mask):
        encoder_output = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
        cls_representation = encoder_output.last_hidden_state[:, 0, :]
        logits = self.classifier(cls_representation)
        return logits

# === Load Model & Tokenizer ===
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = T5Tokenizer.from_pretrained("t5-small")

model = T5Classifier(model_name='t5-small', num_labels=2)
model.load_state_dict(torch.load("best_model.pth", map_location=device))
model.to(device)
model.eval()

# === Prediction Function ===
label_map = {0: "Negative", 1: "Positive"}

def predict_sentiment(text):
    cleaned = preprocess_text(text)
    inputs = tokenizer(cleaned, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
    input_ids = inputs["input_ids"].to(device)
    attention_mask = inputs["attention_mask"].to(device)

    with torch.no_grad():
        logits = model(input_ids=input_ids, attention_mask=attention_mask)
        pred = torch.argmax(logits, dim=1).item()
    
    return label_map[pred]

# === Gradio Interface ===
demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Enter Movie Review"),
    outputs=gr.Text(label="Predicted Sentiment"),
    title="🎬 T5 Movie Review Classifier",
    description="Enter a movie review, and the model will predict whether the sentiment is Positive or Negative."
)

demo.launch()