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import os
from typing import Literal, Optional, Tuple
import logging
import gradio as gr
from omegaconf import OmegaConf
from dacite import Config as DaciteConfig, from_dict
from transformers import GPT2Config, GPT2LMHeadModel
from huggingface_hub import PyTorchModelHubMixin, login
from llm_trainer import LLMTrainer
from xlstm import xLSTMLMModel, xLSTMLMModelConfig
login(token=os.getenv('token'))
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class xLSTMWrapper(xLSTMLMModel, PyTorchModelHubMixin):
pass
GPT2_CONFIG = GPT2Config(
vocab_size=50304,
n_positions=256,
n_embd=768,
n_layer=12,
n_head=12,
activation_function="gelu"
)
XLSTM_CONFIG = OmegaConf.load("xlstm_config.yaml")
XLSTM_CONFIG = from_dict(data_class=xLSTMLMModelConfig, data=OmegaConf.to_container(XLSTM_CONFIG), config=DaciteConfig(strict=True))
UI_CONFIG = {
"title": "HSEAI",
"description": "Enter your text below and the AI will continue it.",
"port": 7860,
"host": "0.0.0.0",
"default_model": "xLSTM",
"max_sequences": 3,
"default_length": 64,
"min_length": 16,
"max_length": 128,
"length_step": 16
}
xLSTM = xLSTMWrapper(XLSTM_CONFIG).from_pretrained("AlekMan/HSE_AI_Vanilla_XLSTM", config=XLSTM_CONFIG)
xLSTM_ft = xLSTMWrapper(XLSTM_CONFIG).from_pretrained("AlekMan/HSE_AI_Vanilla_XLSTM_FT", config=XLSTM_CONFIG)
gpt2 = GPT2LMHeadModel(GPT2_CONFIG).from_pretrained("AlekMan/HSE_AI_GPT2")
gpt2_lora = GPT2LMHeadModel(GPT2_CONFIG).from_pretrained("AlekMan/HSE_AI_GPT2")
gpt2_lora.load_adapter("AlekMan/HSE_AI_GPT2_LoRA")
class ModelManager:
"""Manages model initialization and caching"""
def __init__(self):
self._current_trainer: Optional[LLMTrainer] = None
self._current_model: Optional[str] = None
def get_trainer(self, model_name: Literal["xLSTM", "GPT2", "xLSTM_FT", "GPT2_FT"]):
"""Get trainer instance, creating if necessary"""
if self._current_trainer is None or self._current_model != model_name:
logger.info(f"Loading model: {model_name}")
self._current_trainer = self._load_model(model_name)
self._current_model = model_name
logger.info(f"Model {model_name} loaded successfully")
return self._current_trainer
def _load_model(self, model_name: Literal["xLSTM", "GPT2"]) -> LLMTrainer:
"""Load and initialize model"""
try:
if model_name == "GPT2":
trainer = LLMTrainer(model=gpt2, model_returns_logits=False)
elif model_name == "xLSTM":
trainer = LLMTrainer(model=xLSTM, model_returns_logits=True)
elif model_name == "GPT2_FT":
trainer = LLMTrainer(model=gpt2_lora, model_returns_logits=False)
elif model_name == "xLSTM_FT":
trainer = LLMTrainer(model=xLSTM_ft, model_returns_logits=True)
else:
raise ValueError(f"Unsupported model: {model_name}")
return trainer
except Exception as e:
logger.error(f"Failed to load model {model_name}: {e}")
raise RuntimeError(f"Failed to load model {model_name}: {e}")
model_manager = ModelManager()
def generate_text(
user_input: str,
model_choice: str = UI_CONFIG["default_model"],
n_sequences: int = UI_CONFIG["max_sequences"],
length: int = UI_CONFIG["default_length"]
) -> Tuple[str, str, str]:
"""Generate text continuations using the selected model"""
if not user_input.strip():
return "Please enter some text first.", "", ""
try:
logger.info(f"Generating text with {model_choice}, length: {length}")
trainer = model_manager.get_trainer(model_choice)
continuations = trainer.generate_text(
prompt=user_input,
n_return_sequences=n_sequences,
length=length
)
results = []
for i, continuation in enumerate(continuations[:n_sequences]):
clean_continuation = continuation[len(user_input):].strip()
if clean_continuation:
results.append(clean_continuation + "...")
else:
results.append("(No continuation generated)")
while len(results) < 3:
results.append("")
logger.info("Text generation completed successfully")
return results[0], results[1], results[2]
except Exception as e:
error_msg = f"Error during generation: {str(e)}"
logger.error(error_msg)
return error_msg, "", ""
def create_input_section() -> Tuple[gr.Textbox, gr.Dropdown, gr.Slider, gr.Button]:
"""Create the input section of the interface"""
with gr.Column():
user_input = gr.Textbox(
label="Enter your text:",
placeholder="Type your text here...",
lines=3,
max_lines=10
)
with gr.Row():
model_choice = gr.Dropdown(
choices=["GPT2", "GPT2_FT", "xLSTM", "xLSTM_FT"],
value=UI_CONFIG["default_model"],
label="Model",
interactive=True
)
length = gr.Slider(
minimum=UI_CONFIG["min_length"],
maximum=UI_CONFIG["max_length"],
value=UI_CONFIG["default_length"],
step=UI_CONFIG["length_step"],
label="Generation Length"
)
generate_btn = gr.Button("Generate Continuation", variant="primary")
return user_input, model_choice, length, generate_btn
def create_output_section() -> Tuple[gr.Textbox, gr.Textbox, gr.Textbox]:
"""Create the output section of the interface"""
gr.Markdown("### Generated Continuations:")
with gr.Row():
output1 = gr.Textbox(
label="Continuation 1",
lines=8,
max_lines=15,
interactive=False
)
output2 = gr.Textbox(
label="Continuation 2",
lines=8,
max_lines=15,
interactive=False
)
output3 = gr.Textbox(
label="Continuation 3",
lines=8,
max_lines=15,
interactive=False
)
return output1, output2, output3
def setup_event_handlers(
user_input: gr.Textbox,
model_choice: gr.Dropdown,
length: gr.Slider,
generate_btn: gr.Button,
outputs: Tuple[gr.Textbox, gr.Textbox, gr.Textbox]
) -> None:
"""Setup event handlers for the interface"""
inputs = [
user_input,
model_choice,
gr.Number(value=UI_CONFIG["max_sequences"], visible=False),
length
]
generate_btn.click(
fn=generate_text,
inputs=inputs,
outputs=list(outputs)
)
user_input.submit(
fn=generate_text,
inputs=inputs,
outputs=list(outputs)
)
def create_interface() -> gr.Blocks:
"""Create and return the Gradio interface"""
with gr.Blocks(title=UI_CONFIG["title"], theme=gr.themes.Soft()) as demo:
gr.Markdown(f"# {UI_CONFIG['title']}")
gr.Markdown(UI_CONFIG["description"])
with gr.Row():
user_input, model_choice, length, generate_btn = create_input_section()
outputs = create_output_section()
setup_event_handlers(user_input, model_choice, length, generate_btn, outputs)
return demo
def initialize_model_on_startup() -> None:
"""Initialize the default model on startup"""
try:
logger.info(f"Initializing {UI_CONFIG['default_model']} model on startup...")
model_manager.get_trainer(UI_CONFIG["default_model"])
logger.info(f"{UI_CONFIG['default_model']} model initialized successfully!")
except Exception as e:
logger.warning(f"Could not initialize model on startup: {e}")
logger.info("Model will be initialized when first used.")
def main() -> None:
"""Main function to launch the Gradio app"""
logger.info(f"Starting {UI_CONFIG['title']} application...")
initialize_model_on_startup()
demo = create_interface()
logger.info(f"Launching interface on {UI_CONFIG['host']}:{UI_CONFIG['port']}")
demo.launch(
server_name=UI_CONFIG["host"],
server_port=UI_CONFIG["port"],
share=False,
show_error=True
)
if __name__ == "__main__":
main()
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