import os
import time
import warnings
import re
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)
    print(f"Total physical memory: {memory.total/1e9:.2f} GB")
    print(f"CPU usage: {cpu_percent}%")
    print(f"Memory usage: {memory.percent}% ({memory.used/1e9:.2f}/{memory.total/1e9:.2f} GB)")

# 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 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 extra spaces."""
    text = re.sub(r'[^\w\s.,!?àáâãèéêìíòóôõùúýăđĩũơưạảấầẩẫậắằẳẵặẹẻẽếềểễệỉịọỏốồổỗộớờởỡợụủứừửữựỳỵỷỹ]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def generate_text(prompt, max_length=50, temperature=0.9):
    try:
        start_time = time.time()
        print_system_resources()
        # Encode input with attention mask
        inputs = tokenizer(
            prompt,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=max_length
        ).to(device)
        
        # Generate text
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=25,  # Slightly increase for more content
            min_length=10,
            do_sample=True,  # Enable sampling for diversity
            top_k=50,  # Limit to top 50 tokens
            top_p=0.9,  # Nucleus sampling
            no_repeat_ngram_size=2,
            pad_token_id=tokenizer.pad_token_id
        )
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(f"Raw output: {generated_text}")
        cleaned_text = clean_text(generated_text)
        elapsed_time = time.time() - start_time
        print_system_resources()
        print(f"Generation time: {elapsed_time:.2f} seconds")
        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=50, step=10, label="Độ dài tối đa"),
        gr.Slider(0.7, 1.0, value=0.9, step=0.1, label="Nhiệt độ (Temperature)")
    ],
    outputs="text",
    title="Sinh văn bản tiếng Việt",
    description="Dùng mô hình GPT-2 Vietnamese từ NlpHUST để sinh văn bản tiếng Việt.",
    allow_flagging="never"
)

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