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import gradio as gr
import torch
from mingru_lm import MinGRU_LM
model = MinGRU_LM(dim=512, num_tokens=256, num_layers=6)
pt_model = "model/best_model.pt"
checkpoint = torch.load(pt_model)
model.load_state_dict(checkpoint['model_state_dict'])
# Move model to GPU if available
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
def decode_tokens(tokens):
return ''.join([chr(token) for token in tokens if token >= 32 and token < 256]) # ASCII-safe decoding
def tokenize_text(text):
return [ord(char) for char in text if ord(char) < 256] # ASCII-safe tokenization
def generate_text(start_text, max_length, temperature):
model.eval()
tokens = tokenize_text(start_text)
input_tensor = torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(device) # Ensure long tensor
generated_tokens = tokens.copy()
with torch.no_grad():
for _ in range(max_length):
_, logits = model(input_tensor, labels=None)
last_token_logits = logits[0, -1, :] / temperature
probs = torch.softmax(last_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1).item()
# Only append if it's within the 256-character ASCII range
if next_token < 256:
generated_tokens.append(next_token)
input_tensor = torch.cat([input_tensor, torch.tensor([[next_token]], device=device)], dim=1)
else:
continue # Skip tokens outside ASCII range
return decode_tokens(generated_tokens)
# Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(lines=3, label="Enter your prompt", value="Once upon a time"),
gr.Slider(minimum=10, maximum=500, value=200, step=1, label="Max Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
],
outputs=gr.Textbox(lines=10, label="Generated Text"),
title="Text Generation with MinGRU_LM",
description="Enter a prompt and adjust parameters to generate text using the MinGRU_LM model."
)
if __name__ == "__main__":
iface.launch()
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