RETRAN

This is an extremely tiny model that summarizes text into 4 words

you will need to download the config, tokenizer and model and use this pytho ncode as a starting point:

import torch
import tkinter as tk
import json
import torch.nn as nn
import math

class ThemeExtractor(nn.Module):
    def __init__(self, vocab_size, d_model=64, nhead=4, num_layers=1, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.embedding = nn.Embedding(vocab_size, d_model)
        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward=128, dropout=dropout)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.fc = nn.Linear(d_model, 1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        emb = self.embedding(x) * math.sqrt(self.d_model)
        emb = emb.transpose(0, 1)
        enc_out = self.encoder(emb)
        enc_out = enc_out.transpose(0, 1)
        enc_out = self.dropout(enc_out)
        logits = self.fc(enc_out).squeeze(-1)
        return logits

def load_model_and_tokenizer():
    with open('config.json', 'r') as f:
        config = json.load(f)
    with open('tokenizer.json', 'r') as f:
        vocab = json.load(f)
    vocab_size = config["vocab_size"]
    model = ThemeExtractor(vocab_size, d_model=64, nhead=4, num_layers=1, dropout=0.2)
    model.load_state_dict(torch.load("theme_extractor.pth"))
    return model, vocab, config

def generate_text(model, vocab, config, input_text):
    inv_vocab = {v: k for k, v in vocab.items()}
    max_len = config["max_len"]
    tokens = input_text.lower().split()
    token_ids = [vocab.get(token, vocab["<unk>"]) for token in tokens]
    if len(token_ids) < max_len:
        token_ids += [vocab["<pad>"]] * (max_len - len(token_ids))
    else:
        token_ids = token_ids[:max_len]
    input_tensor = torch.tensor([token_ids], dtype=torch.long).to(device)
    model.eval()
    with torch.no_grad():
        logits = model(input_tensor)
        probs = torch.sigmoid(logits).squeeze(0)
    topk = torch.topk(probs, 4)
    indices = topk.indices.cpu().numpy()
    selected = sorted(indices, key=lambda i: i)
    theme_words = [tokens[i] for i in selected if i < len(tokens)]
    return ' '.join(theme_words)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, vocab, config = load_model_and_tokenizer()

def on_generate():
    input_text = entry_input.get()
    generated = generate_text(model, vocab, config, input_text)
    label_output.config(text="Generated Themes: " + generated)

root = tk.Tk()
root.title("Theme Extractor")

entry_input = tk.Entry(root, width=50)
entry_input.pack(pady=10)

button_generate = tk.Button(root, text="Generate Themes", command=on_generate)
button_generate.pack(pady=10)

label_output = tk.Label(root, text="Generated Themes: ", wraplength=400)
label_output.pack(pady=10)

root.mainloop()