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Gradio web app for the ai-words model.
Runs on HuggingFace Spaces with GPU support.
"""
import os
import subprocess
import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import time
import torch
MODEL_DIR = "./trained_model"
TOKENIZER_NAME = "Qwen/Qwen3-8B"
# Detect device
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
# ─── Load or train model ─────────────────────────────────────────────
def get_model():
"""Load existing trained model, or train from scratch if not available."""
if os.path.isdir(MODEL_DIR) and os.path.isfile(os.path.join(MODEL_DIR, "config.json")):
print(f"Loading trained model from {MODEL_DIR}...")
else:
print("No trained model found. Training from scratch...")
# Run the full pipeline (tokenize + train)
result = subprocess.run(
["python", "tokenize_data.py"],
capture_output=False
)
if result.returncode != 0:
raise RuntimeError("Dataset loading/tokenization failed")
result = subprocess.run(
["python", "train.py"],
capture_output=False
)
if result.returncode != 0:
raise RuntimeError("Training failed")
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
tokenizer.pad_token = tokenizer.eos_token
generator = pipeline(
"text-generation",
model=MODEL_DIR,
tokenizer=tokenizer,
device=device,
clean_up_tokenization_spaces=False,
)
return tokenizer, generator
print(f"Using device: {device}")
tokenizer, generator = get_model()
print("Model ready!")
# ─── Generation function ─────────────────────────────────────────────
def generate(prompt, max_tokens, temperature, do_sample):
if not prompt.strip():
return "Please enter a prompt.", ""
config = GenerationConfig(
max_new_tokens=int(max_tokens),
do_sample=do_sample,
temperature=float(temperature) if do_sample else 1.0,
)
input_ids = tokenizer.encode(prompt)
tokens_in = len(input_ids)
start_time = time.time()
result = generator(prompt, generation_config=config)
elapsed = time.time() - start_time
generated_text = result[0]["generated_text"]
output_ids = tokenizer.encode(generated_text)
tokens_out = len(output_ids)
new_tokens = tokens_out - tokens_in
speed = new_tokens / elapsed if elapsed > 0 else 0
stats = (
f"Prompt tokens: {tokens_in}\n"
f"New tokens: {new_tokens}\n"
f"Total output tokens: {tokens_out}\n"
f"Time: {elapsed:.2f}s\n"
f"Speed: {speed:.1f} tokens/sec\n"
f"Device: {device}"
)
return generated_text, stats
# ─── Gradio UI ────────────────────────────────────────────────────────
with gr.Blocks(title="ai-words", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🧠 ai-words")
gr.Markdown(
"GPT-2 fine-tuned on 13 datasets (~2.9M examples): "
"WikiText, dictionaries, news, reviews, slang, and more."
)
with gr.Row():
with gr.Column(scale=3):
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Type your prompt here...",
lines=3,
value="The meaning of life is"
)
with gr.Row():
max_tokens = gr.Slider(10, 500, value=100, step=10, label="Max new tokens")
temperature = gr.Slider(0.1, 2.0, value=0.8, step=0.1, label="Temperature")
do_sample = gr.Checkbox(value=True, label="Sample")
generate_btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=2):
stats_output = gr.Textbox(label="Stats", lines=6, interactive=False)
output_text = gr.Textbox(label="Generated Text", lines=8, interactive=False)
generate_btn.click(
fn=generate,
inputs=[prompt_input, max_tokens, temperature, do_sample],
outputs=[output_text, stats_output],
)
gr.Markdown(
"### Training Data\n"
"WikiText-103 · WikiText-2 · English Dictionary · WordNet · "
"AG News · IMDB · Rotten Tomatoes · CNN/DailyMail · Yelp Reviews · "
"Urban Dictionary · Slang · Gen Z Slang"
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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