app.py

import streamlit as st 
import torch
import re 
import torch.nn as nn
import json
from tokenizers.models import WordLevel
from tokenizers.pre_tokenizers import Whitespace
from tokenizers import Tokenizer
import torch.nn.functional as F

st.title("Email Classification")

## mopdel 

vocab_size = 473
embedding_dim = 25
hidden_units = 25
num_classes = 2
max_len = 20

class RNNModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_units, num_classes):
        super(RNNModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.RNN(embedding_dim, hidden_units, batch_first=True, dropout=0.2)
        self.fc = nn.Linear(hidden_units, num_classes)

    def forward(self, x):
        x = self.embedding(x)
        output, _ = self.rnn(x)
        x = output[:, -1, :]
        x = self.fc(x)
        return F.softmax(x, dim=1)

model = RNNModel(vocab_size, embedding_dim, hidden_units, num_classes)
## load the weights
model.load_state_dict(torch.load( "email_classfication.pth", map_location=torch.device("cpu")))
model.eval()  

with open("vocab.json", "r") as f:
    vocab = json.load(f)
tokenizer = Tokenizer(WordLevel(vocab, unk_token="<unk>"))
tokenizer.pre_tokenizer = Whitespace()
# tokenizer=joblib.load("tokenizer.pkl")

def preprocess(words):
    normalized = []
    for i in words:
        i = i.lower()
        # get rid of urlss
        i = re.sub('https?://\S+|www\.\S+', '', i)
        # get rid of non words and extra spaces
        i = re.sub('\\W', ' ', i)
        i = re.sub('\n', '', i)
        i = re.sub(' +', ' ', i)
        i = re.sub('^ ', '', i)
        i = re.sub(' $', '', i)

        normalized.append(i)
        text=[tokenizer.encode(text.lower()).ids for text in normalized]
        max_length = 20 
        flattened_text = [token for sublist in text for token in sublist]
        if len(flattened_text) > max_length:
            flattened_text = flattened_text[:max_length] 
        else:
            flattened_text += [0] * (max_length - len(flattened_text))
        text_tensor = torch.tensor(flattened_text, dtype=torch.long)
        text_tensor = text_tensor.unsqueeze(0)       
    return text_tensor
  



text=st.text_input("Enter the Email Text ",value="Happy holidays from our team! Wishing you joy and prosperity this season.")

if st.button("submit"):
    words=text.split()
    v=preprocess(words)
    output=model(v)
    if output.argmax()==1:
        st.write("Its a Spam Mail")
    else:
        st.write("Its not a Spam Mail")