app.py

import torch
import torch.nn as nn
import os
import pickle
from torch.functional import F
import numpy as np
import gradio as gr
import torchtext

#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device('cpu')
VOCAB_SIZE = 10000
MAX_LEN = 200
EMBEDDING_DIM = 100
N_UNITS = 128
VALIDATION_SPLIT = 0.2
SEED = 42
LOAD_MODEL = False
BATCH_SIZE = 128
EPOCHS = 25
# loading model from checkpoint
class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)
        self.log_softmax = nn.LogSoftmax(dim=2)

    def forward(self, x):
        x = self.embedding(x)
        x, _ = self.lstm(x)
        x = self.fc(x)
        return self.log_softmax(x)

# loading model from checkpoint
model = LSTMModel(VOCAB_SIZE, EMBEDDING_DIM, N_UNITS).to(device)

device = 'cpu'

checkpoint_path = 'recipe_generator_LSTM.pth'
checkpoint = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(checkpoint)

print('Loaded model from checkpoint')

def load_vocab(file_path):
    file_path = os.path.join(file_path)
    with open(file_path, 'rb') as input:
        vocab = pickle.load(input)
    print(f"Vocabulary loaded from {file_path}")
    return vocab

vocab = load_vocab('vocab.pkl')




class TextGenerator:
    def __init__(self, vocab, top_k=10):
        self.vocab = vocab
        self.top_k = top_k

    def sample_from(self, logits, temperature):
        probs = F.softmax(logits / temperature, dim=-1).cpu().numpy()
        return np.random.choice(len(probs), p=probs)

    def generate(self, model, device, start_prompt, max_tokens, temperature):
        model.eval()

        tokens = [self.vocab.get_stoi()[token] for token in start_prompt.split()]
        tokens = torch.LongTensor(tokens).unsqueeze(0).to(device)

        with torch.no_grad():
            for _ in range(max_tokens):
                output = model(tokens)
                next_token_logits = output[0, -1, :]
                next_token = self.sample_from(next_token_logits, temperature)
                tokens = torch.cat([tokens, torch.LongTensor([[next_token]]).to(device)], dim=1)

        generated_tokens = [token for token in tokens[0] if self.vocab.get_itos()[token] != '<pad>']
        generated_text = ' '.join(self.vocab.get_itos()[token] for token in generated_tokens)
        return generated_text
    
text_generator = TextGenerator(vocab=vocab, top_k=10)
generated_text = text_generator.generate(model=model, device=device, start_prompt="recipe for", max_tokens=100, temperature=0.5)

print(f"\nGenerated Text: {generated_text}")



def generate_recipe():
    return text_generator.generate(model=model, device=device, start_prompt="recipe for", max_tokens=100, temperature=0.5)

iface = gr.Interface(
    fn=generate_recipe, 
    inputs=[], 
    outputs="text",
    title="Recipe Generator",
    description="This is a LSTM based Recurrent Neural Network trained to generate recipes. Press submit to generate a new recipe that can sometimes provide humor!",
)

iface.launch()