Helidon là gì ? Hướng dẫn chuyển đổi các dự án Spring Boot sang Helidon bằng AI

import os

def classify(path):
    text = open(path).read()

    if path.endswith("pom.xml"):
        return "pom"

    if "@RestController" in text:
        return "controller"

    if "JpaRepository" in text or "CrudRepository" in text:
        return "repo"

    if "@Service" in text or "@Component" in text:
        return "service"

    if "@Entity" in text:
        return "entity"

    return "java"

if __name__ == "__main__":
    base_dir = "../spring-petclinic"
    
    for root, dirs, files in os.walk(base_dir):
        for file in files:
            if file.endswith(".java") or file.endswith("pom.xml"):
                path = os.path.join(root, file)
                ftype = classify(path)
                print(f"{path}:{ftype}")

import os
from openai import OpenAI
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Lock
import time
from datetime import datetime

# Support multiple API providers via environment variables
# Default to Ollama if available, otherwise use DeepSeek
api_key = (os.getenv("OPENAI_API_KEY") or "").strip()
base_url = (os.getenv("OPENAI_BASE_URL") or "").strip()
model_name = os.getenv("MODEL_NAME", "").strip()

# Auto-detect Ollama if not explicitly set
if not base_url:
    # Check if Ollama is running
    import socket
    try:
        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        sock.settimeout(1)
        result = sock.connect_ex(('localhost', 11434))
        sock.close()
        if result == 0:
            # Ollama is running, use it as default
            base_url = "http://localhost:11434/v1"
            model_name = model_name or "llama3"
            api_key = api_key or "ollama"
            print("ℹ Auto-detected Ollama, using it as default")
    except:
        pass
    
    # Fallback to DeepSeek if Ollama not available
    if not base_url:
        base_url = "https://api.deepseek.com"
        model_name = model_name or "deepseek-chat"
        if not api_key:
            print("⚠ Warning: Using DeepSeek default. Set OPENAI_API_KEY for Ollama or other providers.")

if not api_key:
    raise ValueError("OPENAI_API_KEY environment variable is not set")

client = OpenAI(
    api_key=api_key,
    base_url=base_url
)

PROMPTS = {
    "controller": "prompts/controller.txt",
    "repo": "prompts/repo.txt",
    "service": "prompts/service.txt",
    "entity": "prompts/entity.txt",
    "pom": "prompts/pom.txt",
    "java": "prompts/entity.txt"  # fallback
}

# Thread-safe counters for progress tracking
success_lock = Lock()
error_lock = Lock()
progress_lock = Lock()
success_count = 0
error_count = 0
completed_count = 0
start_time = None

def print_progress(completed, total, current_file=None):
    """Print progress with percentage and time estimate"""
    global start_time
    if total == 0:
        return
    
    percentage = (completed / total) * 100
    elapsed = time.time() - start_time if start_time else 0
    
    if completed > 0 and elapsed > 0:
        avg_time = elapsed / completed
        remaining = total - completed
        eta_seconds = avg_time * remaining
        eta_str = f"ETA: {int(eta_seconds//60)}m {int(eta_seconds%60)}s"
    else:
        eta_str = "Calculating..."
    
    elapsed_str = f"{int(elapsed//60)}m {int(elapsed%60)}s"
    
    bar_length = 30
    filled = int(bar_length * completed / total)
    bar = "█" * filled + "░" * (bar_length - filled)
    
    status = f"[{bar}] {completed}/{total} ({percentage:.1f}%) | {elapsed_str} elapsed | {eta_str}"
    if current_file:
        filename = os.path.basename(current_file)
        status += f" | Processing: {filename}"
    
    print(f"\r{status}", end="", flush=True)


def convert(path, ftype, max_retries=3):
    file_start_time = time.time()
    prompt = open(PROMPTS[ftype]).read()
    code = open(path).read()
    
    # Calculate dynamic timeout based on file size
    # Base timeout: 60s, add 2s per KB of code
    code_size_kb = len(code) / 1024
    base_timeout = 60
    dynamic_timeout = int(base_timeout + (code_size_kb * 2))
    # Cap at 10 minutes for very large files
    timeout = min(dynamic_timeout, 600)

    messages = [
        {"role": "system", "content": prompt},
        {"role": "user", "content": code},
    ]

    last_error = None
    for attempt in range(max_retries):
        try:
            # Add small delay between retries
            if attempt > 0:
                wait_time = min(2 ** attempt, 10)  # Exponential backoff, max 10s
                time.sleep(wait_time)
                print(f"  ↻ Retry {attempt}/{max_retries-1} for {os.path.basename(path)}...")
            
            # Call API (supports multiple providers)
            result = client.chat.completions.create(
                model=model_name,
                temperature=0,
                messages=messages,
                timeout=timeout
            )

            output = result.choices[0].message.content

            # Tạo file output theo cấu trúc map
            out_path = "output/" + path.replace("../spring-petclinic/", "")
            os.makedirs(os.path.dirname(out_path), exist_ok=True)
            with open(out_path, "w") as f:
                f.write(output)

            elapsed = time.time() - file_start_time
            return True, elapsed
        except Exception as e:
            last_error = e
            error_msg = str(e)
            # If it's not a timeout, don't retry
            if "timeout" not in error_msg.lower() and "timed out" not in error_msg.lower():
                elapsed = time.time() - file_start_time
                return False, elapsed, error_msg
            # For timeout, continue to retry
    
    # All retries failed
    elapsed = time.time() - file_start_time
    return False, elapsed, str(last_error)


def convert_with_counter(path, ftype, total, skip_existing=True):
    global success_count, error_count, completed_count
    
    # Check if output file already exists
    if skip_existing:
        out_path = "output/" + path.replace("../spring-petclinic/", "")
        if os.path.exists(out_path) and os.path.getsize(out_path) > 0:
            with progress_lock:
                completed_count += 1
                success_count += 1
                filename = os.path.basename(path)
                print(f"\n⊘ [{completed_count}/{total}] {filename} (skipped - already exists)")
                print_progress(completed_count, total)
            return True
    
    result_data = convert(path, ftype)
    
    if len(result_data) == 2:
        success, elapsed = result_data
        error_msg = None
    else:
        success, elapsed, error_msg = result_data
    
    with progress_lock:
        completed_count += 1
        
        if success:
            success_count += 1
            status = "✔"
        else:
            with error_lock:
                error_count += 1
            status = "✗"
        
        # Print detailed result on new line
        filename = os.path.basename(path)
        if success:
            print(f"\n{status} [{completed_count}/{total}] {filename} ({elapsed:.1f}s)")
        else:
            if error_msg:
                if "402" in error_msg or "Insufficient Balance" in error_msg:
                    print(f"\n{status} [{completed_count}/{total}] {filename} - Insufficient Balance")
                elif "401" in error_msg or "Unauthorized" in error_msg:
                    print(f"\n{status} [{completed_count}/{total}] {filename} - Invalid API key")
                elif "timeout" in error_msg.lower():
                    print(f"\n{status} [{completed_count}/{total}] {filename} - Timeout ({elapsed:.1f}s)")
                else:
                    print(f"\n{status} [{completed_count}/{total}] {filename} - Error: {error_msg[:50]}")
            else:
                print(f"\n{status} [{completed_count}/{total}] {filename} - Failed")
        
        # Update progress bar
        print_progress(completed_count, total)
    
    return success


# Cho phép chạy convert thủ công
if __name__ == "__main__":
    import sys
    
    print(f"Using API: {base_url}")
    print(f"Using model: {model_name}")
    print()
    
    if len(sys.argv) == 3:
        result_data = convert(sys.argv[1], sys.argv[2])
        if len(result_data) == 2:
            success, elapsed = result_data
            if success:
                print(f"✔ Converted in {elapsed:.1f}s")
            else:
                print(f"✗ Failed in {elapsed:.1f}s")
        else:
            success, elapsed, error_msg = result_data
            print(f"✗ Error: {error_msg}")
    else:
        success_count = 0
        error_count = 0
        completed_count = 0
        start_time = time.time()
        
        # Parse file list
        tasks = []
        with open("file-list.txt", "r") as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                # Support both formats: "path:type" and "path type"
                if ":" in line:
                    parts = line.split(":", 1)
                else:
                    parts = line.rsplit(None, 1)  # Split on last whitespace
                
                if len(parts) == 2:
                    path, ftype = parts
                    path = path.strip()
                    ftype = ftype.strip()
                    tasks.append((path, ftype))
                else:
                    print(f"⚠ Skipped invalid line: {line[:50]}...")
        
        total = len(tasks)
        # Reduce default workers to avoid overwhelming Ollama
        # Ollama can be slow with too many concurrent requests
        max_workers = int(os.getenv("MAX_WORKERS", "2"))
        
        print(f"Found {total} files to convert")
        print(f"Using {max_workers} parallel workers (reduce if getting timeouts)")
        print(f"Timeout: 60s base + 2s per KB (max 10min)")
        print(f"Retries: 3 attempts per file")
        print(f"Started at {datetime.now().strftime('%H:%M:%S')}\n")
        
        # Initialize progress bar
        print_progress(0, total)
        
        # Use ThreadPoolExecutor for parallel processing
        # Add small delay between submissions to avoid overwhelming the API
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = []
            for idx, (path, ftype) in enumerate(tasks):
                future = executor.submit(convert_with_counter, path, ftype, total)
                futures.append(future)
                # Small delay to avoid overwhelming Ollama
                if idx < len(tasks) - 1:
                    time.sleep(0.5)
            
            # Wait for all tasks to complete
            for future in as_completed(futures):
                try:
                    future.result()
                except Exception as e:
                    with progress_lock:
                        completed_count += 1
                        error_count += 1
                        print(f"\n✗ Unexpected error: {e}")
                        print_progress(completed_count, total)
        
        # Final summary
        total_time = time.time() - start_time
        print(f"\n\n{'='*60}")
        print(f"Summary:")
        print(f"  ✓ Succeeded: {success_count}")
        print(f"  ✗ Failed:    {error_count}")
        print(f"  ⏱ Total time: {int(total_time//60)}m {int(total_time%60)}s")
        if success_count > 0:
            avg_time = total_time / success_count
            print(f"  📊 Avg time/file: {avg_time:.1f}s")
        print(f"{'='*60}")