Spaces:
Sleeping
Sleeping
File size: 2,983 Bytes
ef404e7 b9421c6 ef404e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
# app.py
import streamlit as st
import torch
from src.model import TransformerModel
from src.utils import load_vocab, tokenize
import time
import random
import os
# Configuration
MODEL_PATH = 'models/3ed0k4_model_epoch10.pth' # Update this path based on the latest model
VOCAB_PATH = 'vocab.json'
EMBED_SIZE = 256
NUM_HEADS = 8
HIDDEN_DIM = 512
NUM_LAYERS = 4
DROPOUT = 0.1
MAX_LENGTH = 100 # Maximum tokens to generate
# Title and Description
st.title("3ed0k4 NLP Text Generation Model 🚀")
st.write("Enter a prompt, and the model will generate text based on your input. It will take 1 to 10 seconds to respond to simulate 'thinking'.")
# Load vocabulary
@st.cache_resource
def load_resources():
vocab = load_vocab(VOCAB_PATH)
return vocab
vocab = load_resources()
vocab_size = len(vocab)
# Initialize model
@st.cache_resource
def load_model():
model = TransformerModel(
vocab_size=vocab_size,
embed_size=EMBED_SIZE,
num_heads=NUM_HEADS,
hidden_dim=HIDDEN_DIM,
num_layers=NUM_LAYERS,
dropout=DROPOUT
)
if not os.path.exists(MODEL_PATH):
st.error(f"Model file not found at {MODEL_PATH}. Please ensure the model is trained and the path is correct.")
return None
model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu')))
model.eval()
return model
model = load_model()
def generate_text(prompt, max_length=MAX_LENGTH):
tokens = tokenize(prompt)
numericalized = [vocab.get(token, vocab['<UNK>']) for token in tokens]
input_seq = torch.tensor(numericalized, dtype=torch.long).unsqueeze(0) # Batch size 1
generated = numericalized.copy()
with torch.no_grad():
for _ in range(max_length):
src_mask = model.generate_square_subsequent_mask(input_seq.size(1)).to(input_seq.device)
outputs = model(input_seq, src_mask)
next_token_logits = outputs[0, -1, :]
next_token = torch.argmax(next_token_logits).item()
if next_token == vocab.get('<PAD>', 0):
break
generated.append(next_token)
input_seq = torch.tensor(generated, dtype=torch.long).unsqueeze(0)
# Convert numerical tokens back to words
inv_vocab = {idx: word for word, idx in vocab.items()}
generated_tokens = [inv_vocab.get(tok, '<UNK>') for tok in generated]
return ' '.join(generated_tokens)
# User Inputs
prompt = st.text_input("Enter your prompt:", "")
delay = st.slider("Select thinking delay (seconds):", min_value=1, max_value=10, value=3)
if st.button("Generate"):
if not model:
st.error("Model is not loaded. Please check the model path.")
elif prompt.strip() == "":
st.warning("Please enter a prompt to generate text.")
else:
with st.spinner("Thinking..."):
time.sleep(delay)
response = generate_text(prompt)
st.success("Here's the generated text:")
st.write(response)
|