app.py

import os
import time
import warnings
import re
import gc
warnings.filterwarnings("ignore", category=UserWarning, module="torch._utils")

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import gradio as gr
import psutil

# Print system resources for debugging
def print_system_resources():
    memory = psutil.virtual_memory()
    cpu_percent = psutil.cpu_percent(interval=1)
    # Get container memory limit (for Docker)
    try:
        with open('/sys/fs/cgroup/memory/memory.limit_in_bytes', 'r') as f:
            mem_limit = min(int(f.read().strip()) / 1e9, 16.0)  # Cap at 16GB for HFS free
    except:
        mem_limit = 16.0  # Fallback for HFS free
    print(f"Total physical memory (psutil): {memory.total/1e9:.2f} GB")
    print(f"Container memory limit: {mem_limit:.2f} GB")
    print(f"CPU usage: {cpu_percent}%")
    print(f"Memory usage: {min(memory.used / (mem_limit * 1e9) * 100, 100):.1f}% ({memory.used/1e9:.2f}/{mem_limit:.2f} GB)")
    print(f"Active processes: {len(psutil.pids())}")

# Print Gradio version for debugging
print(f"Gradio version: {gr.__version__}")

# Load model and tokenizer
model_id = "NlpHUST/gpt2-vietnamese"
try:
    tokenizer = GPT2Tokenizer.from_pretrained(model_id)
    model = GPT2LMHeadModel.from_pretrained(model_id)
except Exception as e:
    print(f"Error loading model: {e}")
    raise e

# Set pad_token_id to eos_token_id if not set
if tokenizer.pad_token_id is None:
    tokenizer.pad_token_id = tokenizer.eos_token_id
    model.config.pad_token_id = tokenizer.eos_token_id

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Apply quantization to reduce memory and speed up
model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)
print(f"Model quantized: {model.__class__.__name__}")

# Print device and memory info for debugging
print(f"Device: {device}")
print(f"Memory allocated: {torch.cuda.memory_allocated(device)/1e9:.2f} GB" if torch.cuda.is_available() else "CPU only")
print_system_resources()

def clean_text(text):
    """Clean generated text by removing non-alphabetic characters and normalizing spaces."""
    text = re.sub(r'[^\w\s.,!?àáâãèéêìíòóôõùúýăđĩũơưạảấầẩẫậắằẳẵặẹẻẽếềểễệỉịọỏốồổỗộớờởỡợụủứừửữựỳỵỷỹ]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def generate_text(prompt, max_length=30, temperature=1.0, max_new_tokens=8, top_k=15):
    try:
        start_time = time.time()
        print_system_resources()
        # Log parameters
        print(f"Parameters: max_length={max_length}, temperature={temperature}, max_new_tokens={max_new_tokens}, top_k={top_k}")
        # Encode input with attention mask
        inputs = tokenizer(
            prompt,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=max_length
        ).to(device)
        print(f"Input tokens: {len(inputs['input_ids'][0])}")
        
        # Generate text
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=int(max_new_tokens),
            min_length=3,
            do_sample=True,
            top_k=int(top_k),
            temperature=temperature,
            pad_token_id=tokenizer.pad_token_id
        )
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(f"Raw output: {generated_text}")
        print(f"Generated token count: {len(outputs[0])}")
        cleaned_text = clean_text(generated_text)
        print(f"Cleaned output: {cleaned_text}")
        elapsed_time = time.time() - start_time
        print(f"Generation time: {elapsed_time:.2f} seconds")
        # Clear memory cache
        gc.collect()
        return cleaned_text
    except Exception as e:
        return f"Error generating text: {e}"

# Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(
            label="Nhập văn bản đầu vào",
            placeholder="Viết gì đó bằng tiếng Việt...",
            value="Hôm nay là một ngày đẹp trời"  # Default text
        ),
        gr.Slider(20, 100, value=30, step=10, label="Độ dài tối đa (max_length)"),
        gr.Slider(0.7, 1.5, value=1.0, step=0.1, label="Nhiệt độ (Temperature)"),
        gr.Slider(1, 20, value=8, step=1, label="Số token mới tối đa (max_new_tokens)"),
        gr.Slider(5, 50, value=15, step=5, label="Top K (top_k)")
    ],
    outputs="text",
    title="Sinh văn bản tiếng Việt",
    description="Dùng mô hình GPT-2 Vietnamese từ NlpHUST để sinh văn bản tiếng Việt. Điều chỉnh các tham số để tối ưu thời gian và chất lượng đầu ra.",
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)