app.py

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 necessary NLTK data
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')

# Preprocessing
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)

# Tokenizer
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
max_len = 32
vocab_size = tokenizer.vocab_size

# Model definition
class BiLSTMClassifier(nn.Module):
    def __init__(self, vocab_size, embed_dim, hidden_dim, num_classes):
        super(BiLSTMClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.bilstm = nn.LSTM(embed_dim, hidden_dim, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_dim * 2, num_classes)

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

# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BiLSTMClassifier(vocab_size, embed_dim=128, hidden_dim=64, num_classes=2)
model.load_state_dict(torch.load("best_bi_model.pth", map_location=device))
model.to(device)
model.eval()

# Inference function
def predict_spam(text):
    cleaned = preprocess_text(text)
    encoded = tokenizer(cleaned, truncation=True, padding='max_length', max_length=max_len, return_tensors='pt')
    input_ids = encoded['input_ids'].to(device)

    with torch.no_grad():
        output = model(input_ids)
        prediction = torch.argmax(output, dim=1).item()

    return "Spam 🚫" if prediction == 1 else "Ham ✅"

# Gradio Interface
interface = gr.Interface(
    fn=predict_spam,
    inputs=gr.Textbox(lines=5, label="Enter Email Text"),
    outputs=gr.Label(num_top_classes=2, label="Prediction"),
    title="📧 Spam or Ham Classifier (BiLSTM)",
    description="Enter an email message to predict whether it is Spam or Ham using a trained BiLSTM model."
)

interface.launch()