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

# Download NLTK resources
nltk.download("stopwords")
nltk.download("punkt_tab")
nltk.download("wordnet")

# Preprocessing setup
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'https?://\S+|www\.\S+', '', text)
    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)

# GRU Classifier
class GRUClassifier(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, num_classes):
        super(GRUClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
        self.gru = nn.GRU(embed_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, num_classes)

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        out, _ = self.gru(x)
        out = out[:, -1, :]
        return self.fc(out)

# Load tokenizer and model
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = GRUClassifier(
    vocab_size=tokenizer.vocab_size,
    embed_dim=128,
    hidden_dim=64,
    num_classes=2
).to(device)

model.load_state_dict(torch.load("best_gru_model.pth", map_location=device))
model.eval()

# Prediction function
def predict_clickbait(title):
    preprocessed = preprocess_text(title)
    encoding = tokenizer(preprocessed, truncation=True, padding='max_length', max_length=32, return_tensors='pt')
    input_ids = encoding['input_ids'].to(device)

    with torch.no_grad():
        output = model(input_ids)
        pred = torch.argmax(output, dim=1).item()
        confidence = torch.softmax(output, dim=1).squeeze()[pred].item()

    label = "📢 Spam (Clickbait)" if pred == 1 else "✅ Ham (Non-Clickbait)"
    return f"{label} (Confidence: {confidence:.2f})"

# Gradio Interface
interface = gr.Interface(
    fn=predict_clickbait,
    inputs=gr.Textbox(lines=2, placeholder="Enter a news title or headline..."),
    outputs="text",
    title="📰 Clickbait Detector (Ham vs Spam)",
    description="Enter a headline to detect whether it's ham (non-clickbait) or spam (clickbait) using a GRU-based model."
)

if __name__ == "__main__":
    interface.launch()