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app.py

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+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()

best_bi_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56c69c29aacc53fd27d8378ac966e373204a2b770c02f5d3304b70e2ddaff1b2
+size 16028416

requirements.txt

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+torch>=2.0.0
+transformers>=4.38.0
+gradio>=4.0.0
+nltk>=3.8.1
+pandas>=2.0.0
+scikit-learn>=1.2.0
+tqdm>=4.65.0