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# async_api_processor.py
"""
Asynchronous API Processing for Glossarion
Implements batch API processing with 50% discount from supported providers.
This is SEPARATE from the existing batch processing (parallel API calls).
Supported Providers with Async/Batch APIs (50% discount):
- Gemini (Batch API)
- Anthropic (Message Batches API)
- OpenAI (Batch API)
- Mistral (Batch API)
- Amazon Bedrock (Batch Inference)
- Groq (Batch API)
Providers without Async APIs:
- DeepSeek (no batch API)
- Cohere (only batch embeddings, not completions)
"""
import os
import sys
import re
from bs4 import BeautifulSoup
import ebooklib
from ebooklib import epub
import json
import time
import threading
import logging
import hashlib
import traceback
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Any
import tkinter as tk
from tkinter import ttk, messagebox
import ttkbootstrap as tb
from dataclasses import dataclass, asdict
from enum import Enum
import requests
import uuid
from pathlib import Path
try:
import tiktoken
except ImportError:
tiktoken = None
# For TXT file processing
try:
from txt_processor import TextFileProcessor
except ImportError:
TextFileProcessor = None
print("txt_processor not available - TXT file support disabled")
# For provider-specific implementations
try:
import google.generativeai as genai
HAS_GEMINI = True
except ImportError:
HAS_GEMINI = False
try:
import anthropic
HAS_ANTHROPIC = True
except ImportError:
HAS_ANTHROPIC = False
try:
import openai
HAS_OPENAI = True
except ImportError:
HAS_OPENAI = False
logger = logging.getLogger(__name__)
class AsyncAPIStatus(Enum):
"""Status states for async API jobs"""
PENDING = "pending"
PROCESSING = "processing"
COMPLETED = "completed"
FAILED = "failed"
CANCELLED = "cancelled"
EXPIRED = "expired"
@dataclass
class AsyncJobInfo:
"""Information about an async API job"""
job_id: str
provider: str
model: str
status: AsyncAPIStatus
created_at: datetime
updated_at: datetime
total_requests: int
completed_requests: int = 0
failed_requests: int = 0
cost_estimate: float = 0.0
input_file: Optional[str] = None
output_file: Optional[str] = None
error_message: Optional[str] = None
metadata: Dict[str, Any] = None
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization"""
data = asdict(self)
data['status'] = self.status.value
data['created_at'] = self.created_at.isoformat()
data['updated_at'] = self.updated_at.isoformat()
return data
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'AsyncJobInfo':
"""Create from dictionary"""
data['status'] = AsyncAPIStatus(data['status'])
data['created_at'] = datetime.fromisoformat(data['created_at'])
data['updated_at'] = datetime.fromisoformat(data['updated_at'])
if data.get('metadata') is None:
data['metadata'] = {}
return cls(**data)
class AsyncAPIProcessor:
"""Handles asynchronous batch API processing for supported providers"""
# Provider configurations
PROVIDER_CONFIGS = {
'gemini': {
'batch_endpoint': 'native_sdk', # Uses native SDK instead of REST
'status_endpoint': 'native_sdk',
'max_requests_per_batch': 10000,
'supports_chunking': False,
'discount': 0.5,
'available': True # Now available!
},
'anthropic': {
'batch_endpoint': 'https://api.anthropic.com/v1/messages/batches',
'status_endpoint': 'https://api.anthropic.com/v1/messages/batches/{job_id}',
'max_requests_per_batch': 10000,
'supports_chunking': False,
'discount': 0.5
},
'openai': {
'batch_endpoint': 'https://api.openai.com/v1/batches',
'status_endpoint': 'https://api.openai.com/v1/batches/{job_id}',
'cancel_endpoint': 'https://api.openai.com/v1/batches/{job_id}/cancel',
'max_requests_per_batch': 50000,
'supports_chunking': False,
'discount': 0.5
},
'mistral': {
'batch_endpoint': 'https://api.mistral.ai/v1/batch/jobs',
'status_endpoint': 'https://api.mistral.ai/v1/batch/jobs/{job_id}',
'max_requests_per_batch': 10000,
'supports_chunking': False,
'discount': 0.5
},
'bedrock': {
'batch_endpoint': 'batch-inference', # AWS SDK specific
'max_requests_per_batch': 10000,
'supports_chunking': False,
'discount': 0.5
},
'groq': {
'batch_endpoint': 'https://api.groq.com/openai/v1/batch',
'status_endpoint': 'https://api.groq.com/openai/v1/batch/{job_id}',
'max_requests_per_batch': 1000,
'supports_chunking': False,
'discount': 0.5
}
}
def __init__(self, gui_instance):
"""Initialize the async processor
Args:
gui_instance: Reference to TranslatorGUI instance
"""
self.gui = gui_instance
self.jobs_file = os.path.join(os.path.dirname(__file__), 'async_jobs.json')
self.jobs: Dict[str, AsyncJobInfo] = {}
self.stop_flag = threading.Event()
self.processing_thread = None
self._load_jobs()
def _load_jobs(self):
"""Load saved async jobs from file"""
try:
if os.path.exists(self.jobs_file):
with open(self.jobs_file, 'r', encoding='utf-8') as f:
data = json.load(f)
for job_id, job_data in data.items():
try:
self.jobs[job_id] = AsyncJobInfo.from_dict(job_data)
except Exception as e:
print(f"Failed to load job {job_id}: {e}")
except Exception as e:
print(f"Failed to load async jobs: {e}")
def _save_jobs(self):
"""Save async jobs to file"""
try:
data = {job_id: job.to_dict() for job_id, job in self.jobs.items()}
with open(self.jobs_file, 'w', encoding='utf-8') as f:
json.dump(data, f, indent=2)
except Exception as e:
print(f"Failed to save async jobs: {e}")
def get_provider_from_model(self, model: str) -> Optional[str]:
"""Determine provider from model name"""
model_lower = model.lower()
# Check prefixes
if model_lower.startswith(('gpt', 'o1', 'o3', 'o4')):
return 'openai'
elif model_lower.startswith('gemini'):
return 'gemini'
elif model_lower.startswith(('claude', 'sonnet', 'opus', 'haiku')):
return 'anthropic'
elif model_lower.startswith(('mistral', 'mixtral', 'codestral')):
return 'mistral'
elif model_lower.startswith('groq'):
return 'groq'
elif model_lower.startswith('bedrock'):
return 'bedrock'
# Check for aggregator prefixes that might support async
if model_lower.startswith(('eh/', 'electronhub/', 'electron/')):
# Extract actual model after prefix
actual_model = model.split('/', 1)[1] if '/' in model else model
return self.get_provider_from_model(actual_model)
return None
def supports_async(self, model: str) -> bool:
"""Check if model supports async processing"""
provider = self.get_provider_from_model(model)
return provider in self.PROVIDER_CONFIGS
def estimate_cost(self, num_chapters: int, avg_tokens_per_chapter: int, model: str, compression_factor: float = 1.0) -> Tuple[float, float]:
"""Estimate costs for async vs regular processing
Returns:
Tuple of (async_cost, regular_cost)
"""
provider = self.get_provider_from_model(model)
if not provider:
return (0.0, 0.0)
# UPDATED PRICING AS OF JULY 2025
# Prices are (input_price, output_price) per 1M tokens
token_prices = {
'openai': {
# GPT-4.1 Series (Latest - June 2024 knowledge)
'gpt-4.1': (2.0, 8.0),
'gpt-4.1-mini': (0.4, 1.6),
'gpt-4.1-nano': (0.1, 0.4),
# GPT-4.5 Preview
'gpt-4.5-preview': (75.0, 150.0),
# GPT-4o Series
'gpt-4o': (2.5, 10.0),
'gpt-4o-mini': (0.15, 0.6),
'gpt-4o-audio': (2.5, 10.0),
'gpt-4o-audio-preview': (2.5, 10.0),
'gpt-4o-realtime': (5.0, 20.0),
'gpt-4o-realtime-preview': (5.0, 20.0),
'gpt-4o-mini-audio': (0.15, 0.6),
'gpt-4o-mini-audio-preview': (0.15, 0.6),
'gpt-4o-mini-realtime': (0.6, 2.4),
'gpt-4o-mini-realtime-preview': (0.6, 2.4),
# GPT-4 Legacy
'gpt-4': (30.0, 60.0),
'gpt-4-turbo': (10.0, 30.0),
'gpt-4-32k': (60.0, 120.0),
'gpt-4-0613': (30.0, 60.0),
'gpt-4-0314': (30.0, 60.0),
# GPT-3.5
'gpt-3.5-turbo': (0.5, 1.5),
'gpt-3.5-turbo-instruct': (1.5, 2.0),
'gpt-3.5-turbo-16k': (3.0, 4.0),
'gpt-3.5-turbo-0125': (0.5, 1.5),
# O-series Reasoning Models (NOT batch compatible usually)
'o1': (15.0, 60.0),
'o1-pro': (150.0, 600.0),
'o1-mini': (1.1, 4.4),
'o3': (1.0, 4.0),
'o3-pro': (20.0, 80.0),
'o3-deep-research': (10.0, 40.0),
'o3-mini': (1.1, 4.4),
'o4-mini': (1.1, 4.4),
'o4-mini-deep-research': (2.0, 8.0),
# Special models
'chatgpt-4o-latest': (5.0, 15.0),
'computer-use-preview': (3.0, 12.0),
'gpt-4o-search-preview': (2.5, 10.0),
'gpt-4o-mini-search-preview': (0.15, 0.6),
'codex-mini-latest': (1.5, 6.0),
# Small models
'davinci-002': (2.0, 2.0),
'babbage-002': (0.4, 0.4),
'default': (2.5, 10.0)
},
'anthropic': {
# Claude 4 Series (Latest)
'claude-4-opus': (3.0, 15.0),
'claude-opus-4': (3.0, 15.0),
'claude-4-sonnet': (3.0, 15.0),
'claude-sonnet-4': (3.0, 15.0),
# Claude 3.5 Series
'claude-3.5-sonnet': (3.0, 15.0),
'claude-3.5-opus': (15.0, 75.0),
'claude-3.5-haiku': (0.25, 1.25),
# Claude 3 Series
'claude-3-opus': (15.0, 75.0),
'claude-3-sonnet': (3.0, 15.0),
'claude-3-haiku': (0.25, 1.25),
# Legacy
'claude-2.1': (8.0, 24.0),
'claude-2': (8.0, 24.0),
'claude-instant': (0.8, 2.4),
'default': (3.0, 15.0)
},
'gemini': {
# Gemini 2.5 Series (Latest)
'gemini-2.5-pro': (1.25, 10.0), # β€200k tokens
'gemini-2.5-flash': (0.3, 2.5),
'gemini-2.5-flash-lite': (0.1, 0.4),
'gemini-2.5-flash-lite-preview': (0.1, 0.4),
'gemini-2.5-flash-lite-preview-06-17': (0.1, 0.4),
'gemini-2.5-flash-native-audio': (0.5, 12.0), # Audio output
'gemini-2.5-flash-preview-native-audio-dialog': (0.5, 12.0),
'gemini-2.5-flash-exp-native-audio-thinking-dialog': (0.5, 12.0),
'gemini-2.5-flash-preview-tts': (0.5, 10.0),
'gemini-2.5-pro-preview-tts': (1.0, 20.0),
# Gemini 2.0 Series
'gemini-2.0-flash': (0.1, 0.4),
'gemini-2.0-flash-lite': (0.075, 0.3),
'gemini-2.0-flash-live': (0.35, 1.5),
'gemini-2.0-flash-live-001': (0.35, 1.5),
'gemini-live-2.5-flash-preview': (0.35, 1.5),
# Gemini 1.5 Series
'gemini-1.5-flash': (0.075, 0.3), # β€128k tokens
'gemini-1.5-flash-8b': (0.0375, 0.15),
'gemini-1.5-pro': (1.25, 5.0),
# Legacy/Deprecated
'gemini-1.0-pro': (0.5, 1.5),
'gemini-pro': (0.5, 1.5),
# Experimental
'gemini-exp': (1.25, 5.0),
'default': (0.3, 2.5)
},
'mistral': {
'mistral-large': (3.0, 9.0),
'mistral-large-2': (3.0, 9.0),
'mistral-medium': (0.4, 2.0),
'mistral-medium-3': (0.4, 2.0),
'mistral-small': (1.0, 3.0),
'mistral-small-v24.09': (1.0, 3.0),
'mistral-nemo': (0.3, 0.3),
'mixtral-8x7b': (0.24, 0.24),
'mixtral-8x22b': (1.0, 3.0),
'codestral': (0.1, 0.3),
'ministral': (0.1, 0.3),
'default': (0.4, 2.0)
},
'groq': {
'llama-4-scout': (0.11, 0.34), # Official pricing
'llama-4-maverick': (0.5, 0.77), # Official pricing
'llama-3.1-405b': (2.5, 2.5),
'llama-3.1-70b': (0.59, 0.79),
'llama-3.1-8b': (0.05, 0.1),
'llama-3-70b': (0.59, 0.79),
'llama-3-8b': (0.05, 0.1),
'mixtral-8x7b': (0.24, 0.24),
'gemma-7b': (0.07, 0.07),
'gemma2-9b': (0.1, 0.1),
'default': (0.3, 0.3)
},
'deepseek': {
'deepseek-v3': (0.27, 1.09), # Regular price
'deepseek-v3-promo': (0.14, 0.27), # Promo until Feb 8
'deepseek-chat': (0.27, 1.09),
'deepseek-r1': (0.27, 1.09),
'deepseek-reasoner': (0.27, 1.09),
'deepseek-coder': (0.14, 0.14),
'default': (0.27, 1.09)
},
'cohere': {
'command-a': (2.5, 10.0),
'command-r-plus': (2.5, 10.0),
'command-r+': (2.5, 10.0),
'command-r': (0.15, 0.6),
'command-r7b': (0.0375, 0.15),
'command': (1.0, 3.0),
'default': (0.5, 2.0)
}
}
provider_prices = token_prices.get(provider, {'default': (2.5, 10.0)})
# Find the right price for this model
price_tuple = provider_prices.get('default', (2.5, 10.0))
model_lower = model.lower()
# Try exact match first
if model_lower in provider_prices:
price_tuple = provider_prices[model_lower]
else:
# Try prefix matching
for model_key, price in provider_prices.items():
if model_key == 'default':
continue
# Remove version numbers for matching
model_key_clean = model_key.replace('-', '').replace('.', '')
model_lower_clean = model_lower.replace('-', '').replace('.', '')
if (model_lower.startswith(model_key) or
model_lower_clean.startswith(model_key_clean) or
model_key in model_lower):
price_tuple = price
break
# Calculate weighted average price based on compression_factor
input_price, output_price = price_tuple
input_ratio = 1 / (1 + compression_factor)
output_ratio = compression_factor / (1 + compression_factor)
price_per_million = (input_ratio * input_price) + (output_ratio * output_price)
# Calculate total tokens
# For translation: output is typically 1.2-1.5x input length
output_multiplier = compression_factor # Conservative estimate
total_tokens_per_chapter = avg_tokens_per_chapter * (1 + output_multiplier)
total_tokens = num_chapters * total_tokens_per_chapter
# Convert to cost
regular_cost = (total_tokens / 1_000_000) * price_per_million
# Batch API discount (50% off)
discount = self.PROVIDER_CONFIGS.get(provider, {}).get('discount', 0.5)
async_cost = regular_cost * discount
# Log for debugging
logger.info(f"Cost calculation for {model}:")
logger.info(f" Provider: {provider}")
logger.info(f" Input price: ${input_price:.4f}/1M tokens")
logger.info(f" Output price: ${output_price:.4f}/1M tokens")
logger.info(f" Compression factor: {compression_factor}")
logger.info(f" Weighted avg price: ${price_per_million:.4f}/1M tokens")
logger.info(f" Chapters: {num_chapters}")
logger.info(f" Avg input tokens/chapter: {avg_tokens_per_chapter:,}")
logger.info(f" Total tokens (input+output): {total_tokens:,}")
logger.info(f" Regular cost: ${regular_cost:.4f}")
logger.info(f" Async cost (50% off): ${async_cost:.4f}")
return (async_cost, regular_cost)
def prepare_batch_request(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare batch request for provider
Args:
chapters: List of chapter data with prompts
model: Model name
Returns:
Provider-specific batch request format
"""
provider = self.get_provider_from_model(model)
if provider == 'openai':
return self._prepare_openai_batch(chapters, model)
elif provider == 'anthropic':
return self._prepare_anthropic_batch(chapters, model)
elif provider == 'gemini':
return self._prepare_gemini_batch(chapters, model)
elif provider == 'mistral':
return self._prepare_mistral_batch(chapters, model)
elif provider == 'groq':
return self._prepare_groq_batch(chapters, model)
else:
raise ValueError(f"Unsupported provider for async: {provider}")
def _prepare_openai_batch(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare OpenAI batch format"""
# CRITICAL: Map to exact supported model names
supported_batch_models = {
# Current models (as of July 2025)
'gpt-4o': 'gpt-4o',
'gpt-4o-mini': 'gpt-4o-mini',
'gpt-4-turbo': 'gpt-4-turbo',
'gpt-4-turbo-preview': 'gpt-4-turbo',
'gpt-3.5-turbo': 'gpt-3.5-turbo',
'gpt-3.5': 'gpt-3.5-turbo',
# New GPT-4.1 models (if available in your region)
'gpt-4.1': 'gpt-4.1',
'gpt-4.1-mini': 'gpt-4.1-mini',
'gpt-4o-nano': 'gpt-4o-nano',
# Legacy models (may still work)
'gpt-4': 'gpt-4',
'gpt-4-0613': 'gpt-4-0613',
'gpt-4-0314': 'gpt-4-0314',
}
# Check if model is supported
model_lower = model.lower()
actual_model = None
for key, value in supported_batch_models.items():
if model_lower == key.lower() or model_lower.startswith(key.lower()):
actual_model = value
break
if not actual_model:
print(f"Model '{model}' is not supported for batch processing!")
print(f"Supported models: {list(supported_batch_models.values())}")
raise ValueError(f"Model '{model}' is not supported for OpenAI Batch API")
logger.info(f"Using batch-supported model: '{actual_model}' (from '{model}')")
requests = []
for chapter in chapters:
# Validate messages
messages = chapter.get('messages', [])
if not messages:
print(f"Chapter {chapter['id']} has no messages!")
continue
# Ensure all messages have required fields
valid_messages = []
for msg in messages:
if not msg.get('role') or not msg.get('content'):
print(f"Skipping invalid message: {msg}")
continue
# Ensure content is string and not empty
content = str(msg['content']).strip()
if not content:
print(f"Skipping message with empty content")
continue
valid_messages.append({
'role': msg['role'],
'content': content
})
if not valid_messages:
print(f"No valid messages for chapter {chapter['id']}")
continue
request = {
"custom_id": chapter['id'],
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": actual_model,
"messages": valid_messages,
"temperature": float(chapter.get('temperature', 0.3)),
"max_tokens": int(chapter.get('max_tokens', 8192))
}
}
# LOG THE FIRST REQUEST COMPLETELY
if len(requests) == 0:
print(f"=== FIRST REQUEST ===")
print(json.dumps(request, indent=2))
print(f"=== END FIRST REQUEST ===")
requests.append(request)
return {"requests": requests}
def _prepare_anthropic_batch(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare Anthropic batch format"""
requests = []
for chapter in chapters:
# Extract system message if present
system = None
messages = []
for msg in chapter['messages']:
if msg['role'] == 'system':
system = msg['content']
else:
messages.append(msg)
request = {
"custom_id": chapter['id'],
"params": {
"model": model,
"messages": messages,
"max_tokens": chapter.get('max_tokens', 8192),
"temperature": chapter.get('temperature', 0.3)
}
}
if system:
request["params"]["system"] = system
requests.append(request)
return {"requests": requests}
def _prepare_gemini_batch(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare Gemini batch format"""
requests = []
for chapter in chapters:
# Format messages for Gemini
prompt = self._format_messages_for_gemini(chapter['messages'])
request = {
"custom_id": chapter['id'],
"generateContentRequest": {
"model": f"models/{model}",
"contents": [{"parts": [{"text": prompt}]}],
"generationConfig": {
"temperature": chapter.get('temperature', 0.3),
"maxOutputTokens": chapter.get('max_tokens', 8192)
}
}
}
# Add safety settings if disabled
if os.getenv("DISABLE_GEMINI_SAFETY", "false").lower() == "true":
request["generateContentRequest"]["safetySettings"] = [
{"category": cat, "threshold": "BLOCK_NONE"}
for cat in ["HARM_CATEGORY_HARASSMENT", "HARM_CATEGORY_HATE_SPEECH",
"HARM_CATEGORY_SEXUALLY_EXPLICIT", "HARM_CATEGORY_DANGEROUS_CONTENT",
"HARM_CATEGORY_CIVIC_INTEGRITY"]
]
requests.append(request)
return {"requests": requests}
def _prepare_mistral_batch(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare Mistral batch format"""
requests = []
for chapter in chapters:
request = {
"custom_id": chapter['id'],
"model": model,
"messages": chapter['messages'],
"temperature": chapter.get('temperature', 0.3),
"max_tokens": chapter.get('max_tokens', 8192)
}
requests.append(request)
return {"requests": requests}
def _prepare_groq_batch(self, chapters: List[Dict[str, Any]], model: str) -> Dict[str, Any]:
"""Prepare Groq batch format (OpenAI-compatible)"""
return self._prepare_openai_batch(chapters, model)
def _format_messages_for_gemini(self, messages: List[Dict[str, str]]) -> str:
"""Format messages for Gemini prompt"""
formatted_parts = []
for msg in messages:
role = msg.get('role', 'user').upper()
content = msg['content']
if role == 'SYSTEM':
formatted_parts.append(f"INSTRUCTIONS: {content}")
else:
formatted_parts.append(f"{role}: {content}")
return "\n\n".join(formatted_parts)
async def submit_batch(self, batch_data: Dict[str, Any], model: str, api_key: str) -> AsyncJobInfo:
"""Submit batch to provider and create job entry"""
provider = self.get_provider_from_model(model)
if provider == 'openai':
return await self._submit_openai_batch(batch_data, model, api_key)
elif provider == 'anthropic':
return await self._submit_anthropic_batch(batch_data, model, api_key)
elif provider == 'gemini':
return await self._submit_gemini_batch(batch_data, model, api_key)
elif provider == 'mistral':
return await self._submit_mistral_batch(batch_data, model, api_key)
elif provider == 'groq':
return await self._submit_groq_batch(batch_data, model, api_key)
else:
raise ValueError(f"Unsupported provider: {provider}")
def _submit_openai_batch_sync(self, batch_data, model, api_key):
"""Submit OpenAI batch synchronously"""
try:
# Remove aiofiles import - not needed for sync operations
import tempfile
import json
# Create temporary file for batch data
with tempfile.NamedTemporaryFile(mode='w', suffix='.jsonl', delete=False) as f:
# Write each request as JSONL
for request in batch_data['requests']:
json.dump(request, f)
f.write('\n')
temp_path = f.name
try:
# Upload file to OpenAI
headers = {'Authorization': f'Bearer {api_key}'}
with open(temp_path, 'rb') as f:
files = {'file': ('batch.jsonl', f, 'application/jsonl')}
data = {'purpose': 'batch'}
response = requests.post(
'https://api.openai.com/v1/files',
headers=headers,
files=files,
data=data
)
if response.status_code != 200:
raise Exception(f"File upload failed: {response.text}")
file_id = response.json()['id']
# Create batch job
batch_request = {
'input_file_id': file_id,
'endpoint': '/v1/chat/completions',
'completion_window': '24h'
}
response = requests.post(
'https://api.openai.com/v1/batches',
headers={**headers, 'Content-Type': 'application/json'},
json=batch_request
)
if response.status_code != 200:
raise Exception(f"Batch creation failed: {response.text}")
batch_info = response.json()
# Calculate cost estimate
total_tokens = sum(r.get('token_count', 15000) for r in batch_data['requests'])
async_cost, _ = self.estimate_cost(
len(batch_data['requests']),
total_tokens // len(batch_data['requests']),
model
)
job = AsyncJobInfo(
job_id=batch_info['id'],
provider='openai',
model=model,
status=AsyncAPIStatus.PENDING,
created_at=datetime.now(),
updated_at=datetime.now(),
total_requests=len(batch_data['requests']),
cost_estimate=async_cost,
metadata={'file_id': file_id, 'batch_info': batch_info}
)
return job
finally:
# Clean up temp file
if os.path.exists(temp_path):
os.unlink(temp_path)
except Exception as e:
print(f"OpenAI batch submission failed: {e}")
raise
def _submit_anthropic_batch_sync(self, batch_data: Dict[str, Any], model: str, api_key: str) -> AsyncJobInfo:
"""Submit Anthropic batch (synchronous version)"""
try:
headers = {
'X-API-Key': api_key,
'Content-Type': 'application/json',
'anthropic-version': '2023-06-01',
'anthropic-beta': 'message-batches-2024-09-24'
}
response = requests.post(
'https://api.anthropic.com/v1/messages/batches',
headers=headers,
json=batch_data
)
if response.status_code != 200:
raise Exception(f"Batch creation failed: {response.text}")
batch_info = response.json()
job = AsyncJobInfo(
job_id=batch_info['id'],
provider='anthropic',
model=model,
status=AsyncAPIStatus.PENDING,
created_at=datetime.now(),
updated_at=datetime.now(),
total_requests=len(batch_data['requests']),
metadata={'batch_info': batch_info}
)
return job
except Exception as e:
print(f"Anthropic batch submission failed: {e}")
raise
def check_job_status(self, job_id: str) -> AsyncJobInfo:
"""Check the status of a batch job"""
job = self.jobs.get(job_id)
if not job:
raise ValueError(f"Job {job_id} not found")
try:
provider = job.provider
if provider == 'openai':
self._check_openai_status(job)
elif provider == 'gemini':
self._check_gemini_status(job)
elif provider == 'anthropic':
self._check_anthropic_status(job)
else:
print(f"Unknown provider: {provider}")
# Update timestamp
job.updated_at = datetime.now()
self._save_jobs()
except Exception as e:
print(f"Error checking job status: {e}")
job.metadata['last_error'] = str(e)
return job
def _check_gemini_status(self, job: AsyncJobInfo):
"""Check Gemini batch status"""
try:
# First try the Python SDK approach
try:
from google import genai
api_key = self._get_api_key()
client = genai.Client(api_key=api_key)
# Get batch job status
batch_job = client.batches.get(name=job.job_id)
# Log the actual response for debugging
logger.info(f"Gemini batch job state: {batch_job.state.name if hasattr(batch_job, 'state') else 'Unknown'}")
# Map Gemini states to our status
state_map = {
'JOB_STATE_PENDING': AsyncAPIStatus.PENDING,
'JOB_STATE_RUNNING': AsyncAPIStatus.PROCESSING,
'JOB_STATE_SUCCEEDED': AsyncAPIStatus.COMPLETED,
'JOB_STATE_FAILED': AsyncAPIStatus.FAILED,
'JOB_STATE_CANCELLED': AsyncAPIStatus.CANCELLED,
'JOB_STATE_CANCELLING': AsyncAPIStatus.PROCESSING
}
job.status = state_map.get(batch_job.state.name, AsyncAPIStatus.PENDING)
# Update metadata
if not job.metadata:
job.metadata = {}
if 'batch_info' not in job.metadata:
job.metadata['batch_info'] = {}
job.metadata['batch_info']['state'] = batch_job.state.name
job.metadata['raw_state'] = batch_job.state.name
job.metadata['last_check'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# Try to get progress information
if hasattr(batch_job, 'completed_count'):
job.completed_requests = batch_job.completed_count
elif job.status == AsyncAPIStatus.PROCESSING:
# If processing but no progress info, show as 1 to indicate it started
job.completed_requests = 1
elif job.status == AsyncAPIStatus.COMPLETED:
# If completed, all requests are done
job.completed_requests = job.total_requests
# If completed, store the result file info
if batch_job.state.name == 'JOB_STATE_SUCCEEDED' and hasattr(batch_job, 'dest'):
job.output_file = batch_job.dest.file_name if hasattr(batch_job.dest, 'file_name') else None
except Exception as sdk_error:
# Fallback to REST API if SDK fails
print(f"Gemini SDK failed, trying REST API: {sdk_error}")
api_key = self._get_api_key()
headers = {'x-goog-api-key': api_key}
batch_name = job.job_id if job.job_id.startswith('batches/') else f'batches/{job.job_id}'
response = requests.get(
f'https://generativelanguage.googleapis.com/v1beta/{batch_name}',
headers=headers
)
if response.status_code == 200:
data = response.json()
# Update job status
state = data.get('metadata', {}).get('state', 'JOB_STATE_PENDING')
# Map states
state_map = {
'JOB_STATE_PENDING': AsyncAPIStatus.PENDING,
'JOB_STATE_RUNNING': AsyncAPIStatus.PROCESSING,
'JOB_STATE_SUCCEEDED': AsyncAPIStatus.COMPLETED,
'JOB_STATE_FAILED': AsyncAPIStatus.FAILED,
'JOB_STATE_CANCELLED': AsyncAPIStatus.CANCELLED,
}
job.status = state_map.get(state, AsyncAPIStatus.PENDING)
# Extract progress from metadata
metadata = data.get('metadata', {})
# Gemini might provide progress info
if 'completedRequestCount' in metadata:
job.completed_requests = metadata['completedRequestCount']
if 'failedRequestCount' in metadata:
job.failed_requests = metadata['failedRequestCount']
if 'totalRequestCount' in metadata:
job.total_requests = metadata['totalRequestCount']
# Store raw state
if not job.metadata:
job.metadata = {}
job.metadata['raw_state'] = state
job.metadata['last_check'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# Check if completed
if state == 'JOB_STATE_SUCCEEDED' and 'response' in data:
job.status = AsyncAPIStatus.COMPLETED
if 'responsesFile' in data.get('response', {}):
job.output_file = data['response']['responsesFile']
else:
print(f"Gemini status check failed: {response.status_code} - {response.text}")
except Exception as e:
print(f"Gemini status check failed: {e}")
if not job.metadata:
job.metadata = {}
job.metadata['last_error'] = str(e)
def _check_openai_status(self, job: AsyncJobInfo):
"""Check OpenAI batch status"""
try:
api_key = self._get_api_key()
headers = {'Authorization': f'Bearer {api_key}'}
response = requests.get(
f'https://api.openai.com/v1/batches/{job.job_id}',
headers=headers
)
if response.status_code != 200:
print(f"Status check failed: {response.text}")
return
data = response.json()
# Log the full response for debugging
logger.debug(f"OpenAI batch status response: {json.dumps(data, indent=2)}")
# Check for high failure rate while in progress
request_counts = data.get('request_counts', {})
total = request_counts.get('total', 0)
failed = request_counts.get('failed', 0)
completed = request_counts.get('completed', 0)
# Map OpenAI status to our status
status_map = {
'validating': AsyncAPIStatus.PENDING,
'in_progress': AsyncAPIStatus.PROCESSING,
'finalizing': AsyncAPIStatus.PROCESSING,
'completed': AsyncAPIStatus.COMPLETED,
'failed': AsyncAPIStatus.FAILED,
'expired': AsyncAPIStatus.EXPIRED,
'cancelled': AsyncAPIStatus.CANCELLED,
'cancelling': AsyncAPIStatus.CANCELLED,
}
job.status = status_map.get(data['status'], AsyncAPIStatus.PENDING)
# Update progress
request_counts = data.get('request_counts', {})
job.completed_requests = request_counts.get('completed', 0)
job.failed_requests = request_counts.get('failed', 0)
job.total_requests = request_counts.get('total', job.total_requests)
# Store metadata
if not job.metadata:
job.metadata = {}
job.metadata['raw_state'] = data['status']
job.metadata['last_check'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# Handle completion
if data['status'] == 'completed':
# Check if all requests failed
if job.failed_requests > 0 and job.completed_requests == 0:
print(f"OpenAI job completed but all {job.failed_requests} requests failed")
job.status = AsyncAPIStatus.FAILED
job.metadata['all_failed'] = True
# Store error file if available
if data.get('error_file_id'):
job.metadata['error_file_id'] = data['error_file_id']
logger.info(f"Error file available: {data['error_file_id']}")
else:
# Normal completion with some successes
if 'output_file_id' in data and data['output_file_id']:
job.output_file = data['output_file_id']
logger.info(f"OpenAI job completed with output file: {job.output_file}")
# If there were also failures, note that
if job.failed_requests > 0:
job.metadata['partial_failure'] = True
print(f"Job completed with {job.failed_requests} failed requests out of {job.total_requests}")
else:
print(f"OpenAI job marked as completed but no output_file_id found: {data}")
# Always store error file if present
if data.get('error_file_id'):
job.metadata['error_file_id'] = data['error_file_id']
except Exception as e:
print(f"OpenAI status check failed: {e}")
if not job.metadata:
job.metadata = {}
job.metadata['last_error'] = str(e)
def _check_anthropic_status(self, job: AsyncJobInfo):
"""Check Anthropic batch status"""
try:
api_key = self._get_api_key()
headers = {
'X-API-Key': api_key,
'anthropic-version': '2023-06-01',
'anthropic-beta': 'message-batches-2024-09-24'
}
response = requests.get(
f'https://api.anthropic.com/v1/messages/batches/{job.job_id}',
headers=headers
)
if response.status_code != 200:
print(f"Status check failed: {response.text}")
return
data = response.json()
# Map Anthropic status
status_map = {
'created': AsyncAPIStatus.PENDING,
'processing': AsyncAPIStatus.PROCESSING,
'ended': AsyncAPIStatus.COMPLETED,
'failed': AsyncAPIStatus.FAILED,
'expired': AsyncAPIStatus.EXPIRED,
'canceled': AsyncAPIStatus.CANCELLED,
}
job.status = status_map.get(data['processing_status'], AsyncAPIStatus.PENDING)
# Update progress
results_summary = data.get('results_summary', {})
job.completed_requests = results_summary.get('succeeded', 0)
job.failed_requests = results_summary.get('failed', 0)
job.total_requests = results_summary.get('total', job.total_requests)
# Store metadata
if not job.metadata:
job.metadata = {}
job.metadata['raw_state'] = data['processing_status']
job.metadata['last_check'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if data.get('results_url'):
job.output_file = data['results_url']
except Exception as e:
print(f"Anthropic status check failed: {e}")
if not job.metadata:
job.metadata = {}
job.metadata['last_error'] = str(e)
def _get_api_key(self) -> str:
"""Get API key from GUI settings"""
if hasattr(self.gui, 'api_key_entry'):
return self.gui.api_key_entry.get().strip()
elif hasattr(self.gui, 'api_key_var'):
return self.gui.api_key_var.get().strip()
else:
# Fallback to environment variable
return os.getenv('API_KEY', '') or os.getenv('GEMINI_API_KEY', '') or os.getenv('GOOGLE_API_KEY', '')
def retrieve_results(self, job_id: str) -> List[Dict[str, Any]]:
"""Retrieve results from a completed batch job"""
job = self.jobs.get(job_id)
if not job:
raise ValueError(f"Job {job_id} not found")
if job.status != AsyncAPIStatus.COMPLETED:
raise ValueError(f"Job is not completed. Current status: {job.status.value}")
# If output file is missing, try to refresh status first
if not job.output_file:
print(f"No output file for completed job {job_id}, refreshing status...")
self.check_job_status(job_id)
# Re-check after status update
if not job.output_file:
# Log the job details for debugging
print(f"Job details: {json.dumps(job.to_dict(), indent=2)}")
raise ValueError(f"No output file available for job {job_id} even after status refresh")
provider = job.provider
if provider == 'openai':
return self._retrieve_openai_results(job)
elif provider == 'gemini':
return self._retrieve_gemini_results(job)
elif provider == 'anthropic':
return self._retrieve_anthropic_results(job)
else:
raise ValueError(f"Unknown provider: {provider}")
def _retrieve_gemini_results(self, job: AsyncJobInfo) -> List[Dict[str, Any]]:
"""Retrieve Gemini batch results"""
try:
from google import genai
api_key = self._get_api_key()
# Create client with API key
client = genai.Client(api_key=api_key)
# Get the batch job
batch_job = client.batches.get(name=job.job_id)
if batch_job.state != 'JOB_STATE_SUCCEEDED':
raise ValueError(f"Batch job not completed: {batch_job.state}")
# Download results
if hasattr(batch_job, 'dest') and batch_job.dest:
# Extract the file name from the destination object
if hasattr(batch_job.dest, 'output_uri'):
# For BigQuery or Cloud Storage destinations
file_name = batch_job.dest.output_uri
elif hasattr(batch_job.dest, 'file_name'):
# For file-based destinations
file_name = batch_job.dest.file_name
else:
# Try to get any file reference from the dest object
# Log the object to understand its structure
logger.info(f"BatchJobDestination object: {batch_job.dest}")
logger.info(f"BatchJobDestination attributes: {dir(batch_job.dest)}")
raise ValueError(f"Cannot extract file name from destination: {batch_job.dest}")
# Download the results file
results_content_bytes = client.files.download(file=file_name)
results_content = results_content_bytes.decode('utf-8')
results = []
# Parse JSONL results
for line in results_content.splitlines():
if line.strip():
result_data = json.loads(line)
# Extract the response content
text_content = ""
# Handle different response formats
if 'response' in result_data:
response = result_data['response']
# Check for different content structures
if isinstance(response, dict):
if 'candidates' in response and response['candidates']:
candidate = response['candidates'][0]
if 'content' in candidate and 'parts' in candidate['content']:
for part in candidate['content']['parts']:
if 'text' in part:
text_content += part['text']
elif 'text' in candidate:
text_content = candidate['text']
elif 'text' in response:
text_content = response['text']
elif 'content' in response:
text_content = response['content']
elif isinstance(response, str):
text_content = response
results.append({
'custom_id': result_data.get('key', ''),
'content': text_content,
'finish_reason': 'stop'
})
return results
else:
raise ValueError("No output file available for completed job")
except ImportError:
raise ImportError(
"google-genai package not installed. "
"Run: pip install google-genai"
)
except Exception as e:
print(f"Failed to retrieve Gemini results: {e}")
raise
def _retrieve_openai_results(self, job: AsyncJobInfo) -> List[Dict[str, Any]]:
"""Retrieve OpenAI batch results"""
if not job.output_file:
# Try one more status check
self._check_openai_status(job)
if not job.output_file:
raise ValueError(f"No output file available for OpenAI job {job.job_id}")
try:
api_key = self._get_api_key()
headers = {'Authorization': f'Bearer {api_key}'}
# Download results file
response = requests.get(
f'https://api.openai.com/v1/files/{job.output_file}/content',
headers=headers
)
if response.status_code != 200:
raise Exception(f"Failed to download results: {response.status_code} - {response.text}")
# Parse JSONL results
results = []
for line in response.text.strip().split('\n'):
if line:
try:
result = json.loads(line)
# Extract the actual response content
if 'response' in result and 'body' in result['response']:
results.append({
'custom_id': result.get('custom_id', ''),
'content': result['response']['body']['choices'][0]['message']['content'],
'finish_reason': result['response']['body']['choices'][0].get('finish_reason', 'stop')
})
else:
print(f"Unexpected result format: {result}")
except json.JSONDecodeError as e:
print(f"Failed to parse result line: {line} - {e}")
return results
except Exception as e:
print(f"Failed to retrieve OpenAI results: {e}")
print(f"Job details: {json.dumps(job.to_dict(), indent=2)}")
raise
def _retrieve_anthropic_results(self, job: AsyncJobInfo) -> List[Dict[str, Any]]:
"""Retrieve Anthropic batch results"""
if not job.output_file:
raise ValueError("No output file available")
api_key = self._get_api_key()
headers = {
'X-API-Key': api_key,
'anthropic-version': '2023-06-01'
}
# Download results
response = requests.get(job.output_file, headers=headers)
if response.status_code != 200:
raise Exception(f"Failed to download results: {response.text}")
# Parse JSONL results
results = []
for line in response.text.strip().split('\n'):
if line:
result = json.loads(line)
if result['result']['type'] == 'succeeded':
message = result['result']['message']
results.append({
'custom_id': result['custom_id'],
'content': message['content'][0]['text'],
'finish_reason': message.get('stop_reason', 'stop')
})
return results
class AsyncProcessingDialog:
"""GUI dialog for async processing"""
def __init__(self, parent, translator_gui):
"""Initialize dialog
Args:
parent: Parent window
translator_gui: Reference to main TranslatorGUI instance
"""
self.parent = parent
self.gui = translator_gui
# Fix for PyInstaller - ensure processor uses correct directory
self.processor = AsyncAPIProcessor(translator_gui)
# If running as exe, update the jobs file path
if getattr(sys, 'frozen', False):
# Running as compiled exe
application_path = os.path.dirname(sys.executable)
self.processor.jobs_file = os.path.join(application_path, 'async_jobs.json')
# Reload jobs from the correct location
self.processor._load_jobs()
self.selected_job_id = None
self.polling_jobs = set() # Track which jobs are being polled
# Use the correct attribute name 'wm' instead of 'window_manager'
self.window_manager = translator_gui.wm # WindowManager is stored as 'wm'
self._create_dialog()
self._refresh_jobs_list()
def _create_dialog(self):
"""Create the async processing dialog"""
# Create scrollable dialog (stays hidden)
self.dialog, scrollable_frame, canvas = self.window_manager.setup_scrollable(
self.parent,
"Async Batch Processing (50% Discount)",
width=0, # Will be auto-sized
height=None,
max_width_ratio=0.9,
max_height_ratio=1.00
)
# Store references
self.scrollable_frame = scrollable_frame
self.canvas = canvas
# Main container in scrollable_frame
main_frame = ttk.Frame(scrollable_frame)
main_frame.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)
# Top section - Information and controls
self._create_info_section(main_frame)
# Middle section - Configuration
self._create_config_section(main_frame)
# Bottom section - Active jobs
self._create_jobs_section(main_frame)
# Button frame goes in the DIALOG, not scrollable_frame
button_frame = ttk.Frame(self.dialog)
button_frame.pack(fill=tk.X, padx=10, pady=10)
self._create_button_frame(button_frame)
# Load active jobs
self._refresh_jobs_list()
# Auto-resize and show - THIS is what applies the height ratio!
self.window_manager.auto_resize_dialog(
self.dialog,
canvas,
max_width_ratio=0.9,
max_height_ratio=0.92 # Can override to any value like 1.43
)
# Handle window close
self.dialog.protocol("WM_DELETE_WINDOW",
lambda: [self.dialog._cleanup_scrolling(), self.dialog.destroy()])
self._start_auto_refresh(30)
def _create_info_section(self, parent):
"""Create information section"""
info_frame = ttk.LabelFrame(parent, text="Async Processing Information", padding=10)
info_frame.pack(fill=tk.X, pady=(0, 10))
# Model and provider info
model_frame = ttk.Frame(info_frame)
model_frame.pack(fill=tk.X)
ttk.Label(model_frame, text="Current Model:").pack(side=tk.LEFT, padx=(0, 5))
model_name = self.gui.model_var.get() if hasattr(self.gui, 'model_var') else "Not selected"
self.model_label = ttk.Label(model_frame, text=model_name, style="info.TLabel")
self.model_label.pack(side=tk.LEFT, padx=(0, 20))
# Check if model supports async
provider = self.processor.get_provider_from_model(model_name)
if provider and provider in self.processor.PROVIDER_CONFIGS:
status_text = f"β Supported ({provider.upper()})"
status_style = "success.TLabel"
else:
status_text = "β Not supported for async"
status_style = "danger.TLabel"
ttk.Label(model_frame, text=status_text, style=status_style).pack(side=tk.LEFT)
# Cost estimation
cost_frame = ttk.Frame(info_frame)
cost_frame.pack(fill=tk.X, pady=(10, 0))
ttk.Label(cost_frame, text="Cost Estimation:", font=("", 10, "bold")).pack(anchor=tk.W)
self.cost_info_label = ttk.Label(cost_frame, text="Select chapters to see cost estimate")
self.cost_info_label.pack(anchor=tk.W, pady=(5, 0))
def _create_config_section(self, parent):
"""Create configuration section"""
config_frame = ttk.LabelFrame(parent, text="Async Processing Configuration", padding=10)
config_frame.pack(fill=tk.X, pady=(0, 10))
# Processing options
options_frame = ttk.Frame(config_frame)
options_frame.pack(fill=tk.X)
# Wait for completion
self.wait_for_completion_var = tk.BooleanVar(value=False)
ttk.Checkbutton(
options_frame,
text="Wait for completion (blocks GUI)",
variable=self.wait_for_completion_var
).pack(anchor=tk.W)
# Poll interval
poll_frame = ttk.Frame(options_frame)
poll_frame.pack(fill=tk.X, pady=(5, 0))
ttk.Label(poll_frame, text="Poll interval (seconds):").pack(side=tk.LEFT, padx=(0, 5))
self.poll_interval_var = tk.IntVar(value=60)
ttk.Spinbox(
poll_frame,
from_=10,
to=600,
textvariable=self.poll_interval_var,
width=10
).pack(side=tk.LEFT)
# Chapter selection info
chapter_frame = ttk.Frame(config_frame)
chapter_frame.pack(fill=tk.X, pady=(10, 0))
self.chapter_info_label = ttk.Label(
chapter_frame,
text="Note: Async processing will skip chapters that require chunking",
style="warning.TLabel"
)
self.chapter_info_label.pack(anchor=tk.W)
def _create_jobs_section(self, parent):
"""Create active jobs section"""
jobs_frame = ttk.LabelFrame(parent, text="Active Async Jobs", padding=10)
jobs_frame.pack(fill=tk.BOTH, expand=True, pady=(0, 10))
# Jobs treeview
tree_frame = ttk.Frame(jobs_frame)
tree_frame.pack(fill=tk.BOTH, expand=True)
# Scrollbars
vsb = ttk.Scrollbar(tree_frame, orient="vertical")
hsb = ttk.Scrollbar(tree_frame, orient="horizontal")
# Treeview
self.jobs_tree = ttk.Treeview(
tree_frame,
columns=("Provider", "Model", "Status", "Progress", "Created", "Cost"),
show="tree headings",
yscrollcommand=vsb.set,
xscrollcommand=hsb.set
)
vsb.config(command=self.jobs_tree.yview)
hsb.config(command=self.jobs_tree.xview)
# Add a progress bar for the selected job
progress_frame = ttk.Frame(jobs_frame)
progress_frame.pack(fill=tk.X, pady=(5, 0))
ttk.Label(progress_frame, text="Selected Job Progress:").pack(side=tk.LEFT, padx=(0, 5))
self.job_progress_bar = ttk.Progressbar(
progress_frame,
mode='determinate',
style='success.Horizontal.TProgressbar'
)
self.job_progress_bar.pack(side=tk.LEFT, fill=tk.X, expand=True)
self.progress_label = ttk.Label(progress_frame, text="0%")
self.progress_label.pack(side=tk.LEFT, padx=(5, 0))
# Configure columns
self.jobs_tree.heading("#0", text="Job ID")
self.jobs_tree.heading("Provider", text="Provider")
self.jobs_tree.heading("Model", text="Model")
self.jobs_tree.heading("Status", text="Status")
self.jobs_tree.heading("Progress", text="Progress")
self.jobs_tree.heading("Created", text="Created")
self.jobs_tree.heading("Cost", text="Est. Cost")
self.jobs_tree.column("#0", width=200)
self.jobs_tree.column("Provider", width=100)
self.jobs_tree.column("Model", width=150)
self.jobs_tree.column("Status", width=100)
self.jobs_tree.column("Progress", width=150)
self.jobs_tree.column("Created", width=150)
self.jobs_tree.column("Cost", width=100)
# Add right-click menu
self.jobs_context_menu = tk.Menu(self.jobs_tree, tearoff=0)
self.jobs_context_menu.add_command(label="Check Status", command=self._check_selected_status)
self.jobs_context_menu.add_command(label="Retrieve Results", command=self._retrieve_selected_results)
self.jobs_context_menu.add_separator()
self.jobs_context_menu.add_command(label="Delete", command=self._delete_selected_job)
# Context menu binding function - use unique name to avoid conflicts
def show_jobs_context_menu(event):
# Select the item under cursor
item = self.jobs_tree.identify_row(event.y)
if item:
self.jobs_tree.selection_set(item)
self._on_job_select(None) # Update selection
self.jobs_context_menu.post(event.x_root, event.y_root)
# Bind right-click
self.jobs_tree.bind("<Button-3>", show_jobs_context_menu) # Right-click on Windows/Linux
if sys.platform == "darwin":
self.jobs_tree.bind("<Button-2>", show_jobs_context_menu) # Right-click on macOS
# Pack treeview and scrollbars
self.jobs_tree.grid(row=0, column=0, sticky="nsew")
vsb.grid(row=0, column=1, sticky="ns")
hsb.grid(row=1, column=0, sticky="ew")
tree_frame.grid_rowconfigure(0, weight=1)
tree_frame.grid_columnconfigure(0, weight=1)
# Bind selection
self.jobs_tree.bind('<<TreeviewSelect>>', self._on_job_select)
# Job action buttons
action_frame = ttk.Frame(jobs_frame)
action_frame.pack(fill=tk.X, pady=(10, 0))
button_width = 15
ttk.Button(
action_frame,
text="Check Status",
command=self._check_selected_status,
style="info.TButton",
width=button_width
).pack(side=tk.LEFT, padx=(0, 5))
ttk.Button(
action_frame,
text="Retrieve Results",
command=self._retrieve_selected_results,
style="success.TButton",
width=button_width
).pack(side=tk.LEFT, padx=(0, 5))
ttk.Button(
action_frame,
text="Cancel Job",
command=self._cancel_selected_job,
style="warning.TButton",
width=button_width
).pack(side=tk.LEFT, padx=(0, 5))
# delete buttons
ttk.Button(
action_frame,
text="Delete Selected",
command=self._delete_selected_job,
style="danger.TButton",
width=button_width
).pack(side=tk.LEFT, padx=(30, 5)) # Extra padding to separate
ttk.Button(
action_frame,
text="Clear Completed",
command=self._clear_completed_jobs,
style="secondary.TButton",
width=button_width
).pack(side=tk.LEFT)
def _create_button_frame(self, parent):
"""Create bottom button frame"""
button_frame = ttk.Frame(parent)
button_frame.pack(fill=tk.X, pady=(20, 0))
# Start processing button
self.start_button = ttk.Button(
button_frame,
text="Start Async Processing",
command=self._start_processing,
style="success.TButton"
)
self.start_button.pack(side=tk.LEFT, padx=(0, 5))
# Estimate only button
ttk.Button(
button_frame,
text="Estimate Cost Only",
command=self._estimate_cost,
style="info.TButton"
).pack(side=tk.LEFT, padx=(0, 5))
# Close button - need to handle cleanup if using WindowManager
if hasattr(self.dialog, '_cleanup_scrolling'):
ttk.Button(
button_frame,
text="Close",
command=lambda: [self.dialog._cleanup_scrolling(), self.dialog.destroy()]
).pack(side=tk.RIGHT)
else:
ttk.Button(
button_frame,
text="Close",
command=self.dialog.destroy
).pack(side=tk.RIGHT)
def _update_selected_job_progress(self, job):
"""Update progress display for selected job"""
if hasattr(self, 'job_progress_bar'):
if job.total_requests > 0:
progress = int((job.completed_requests / job.total_requests) * 100)
self.job_progress_bar['value'] = progress
# Update progress label if exists
if hasattr(self, 'progress_label'):
self.progress_label.config(
text=f"{progress}% ({job.completed_requests}/{job.total_requests} chapters)"
)
else:
self.job_progress_bar['value'] = 0
if hasattr(self, 'progress_label'):
self.progress_label.config(text="0% (Waiting)")
def _refresh_jobs_list(self):
"""Refresh the jobs list"""
# Clear existing items
for item in self.jobs_tree.get_children():
self.jobs_tree.delete(item)
# Add jobs
for job_id, job in self.processor.jobs.items():
# Calculate progress percentage and format progress text
if job.total_requests > 0:
progress_pct = int((job.completed_requests / job.total_requests) * 100)
progress_text = f"{progress_pct}% ({job.completed_requests}/{job.total_requests})"
else:
progress_pct = 0
progress_text = "0% (0/0)"
# Override progress text for completed/failed/cancelled statuses
if job.status == AsyncAPIStatus.COMPLETED:
progress_text = "100% (Complete)"
elif job.status == AsyncAPIStatus.FAILED:
progress_text = f"{progress_pct}% (Failed)"
elif job.status == AsyncAPIStatus.CANCELLED:
progress_text = f"{progress_pct}% (Cancelled)"
elif job.status == AsyncAPIStatus.PENDING:
progress_text = "0% (Waiting)"
created = job.created_at.strftime("%Y-%m-%d %H:%M")
cost = f"${job.cost_estimate:.2f}" if job.cost_estimate else "N/A"
# Determine status style
status_text = job.status.value.capitalize()
# Shorten job ID for display
display_id = job_id[:20] + "..." if len(job_id) > 20 else job_id
self.jobs_tree.insert(
"",
"end",
text=display_id,
values=(
job.provider.upper(),
job.model[:15] + "..." if len(job.model) > 15 else job.model, # Shorten model name
status_text,
progress_text, # Now shows percentage and counts
created,
cost
),
tags=(job.status.value,)
)
# Configure tags for status colors
self.jobs_tree.tag_configure("pending", foreground="#FFA500") # Orange
self.jobs_tree.tag_configure("processing", foreground="#007BFF") # Blue
self.jobs_tree.tag_configure("completed", foreground="#28A745") # Green
self.jobs_tree.tag_configure("failed", foreground="#DC3545") # Red
self.jobs_tree.tag_configure("cancelled", foreground="#6C757D") # Gray
# Update progress bar if a job is selected
if hasattr(self, 'selected_job_id') and self.selected_job_id:
job = self.processor.jobs.get(self.selected_job_id)
if job:
self._update_selected_job_progress(job)
def _on_job_select(self, event):
"""Handle job selection"""
selection = self.jobs_tree.selection()
if selection:
item = self.jobs_tree.item(selection[0])
# Get full job ID from the item
job_id_prefix = item['text'].rstrip('...')
# Find matching job
for job_id in self.processor.jobs:
if job_id.startswith(job_id_prefix):
self.selected_job_id = job_id
# Update progress display for selected job
job = self.processor.jobs.get(job_id)
if job:
# Update progress bar if it exists
if hasattr(self, 'job_progress_bar'):
if job.total_requests > 0:
progress = int((job.completed_requests / job.total_requests) * 100)
self.job_progress_bar['value'] = progress
else:
self.job_progress_bar['value'] = 0
# Update progress label if it exists
if hasattr(self, 'progress_label'):
if job.total_requests > 0:
progress = int((job.completed_requests / job.total_requests) * 100)
self.progress_label.config(
text=f"{progress}% ({job.completed_requests}/{job.total_requests} chapters)"
)
else:
self.progress_label.config(text="0% (Waiting)")
# Log selection
logger.info(f"Selected job: {job_id[:30]}... - Status: {job.status.value}")
break
def _check_selected_status(self):
"""Check status of selected job"""
if not self.selected_job_id:
messagebox.showwarning("No Selection", "Please select a job to check status")
return
try:
job = self.processor.check_job_status(self.selected_job_id)
self._refresh_jobs_list()
# Build detailed status message
status_text = f"Job ID: {job.job_id}\n"
status_text += f"Provider: {job.provider.upper()}\n"
status_text += f"Status: {job.status.value}\n"
status_text += f"State: {job.metadata.get('raw_state', 'Unknown')}\n\n"
# Progress information
if job.completed_requests > 0 or job.status == AsyncAPIStatus.PROCESSING:
status_text += f"Progress: {job.completed_requests}/{job.total_requests}\n"
else:
status_text += f"Progress: Waiting to start (0/{job.total_requests})\n"
status_text += f"Failed: {job.failed_requests}\n\n"
# Time information
status_text += f"Created: {job.created_at.strftime('%Y-%m-%d %H:%M:%S')}\n"
status_text += f"Last Updated: {job.updated_at.strftime('%Y-%m-%d %H:%M:%S')}\n"
if 'last_check' in job.metadata:
status_text += f"Last Checked: {job.metadata['last_check']}\n"
# Show output file if available
if job.output_file:
status_text += f"\nOutput Ready: {job.output_file}\n"
messagebox.showinfo("Job Status", status_text)
except Exception as e:
messagebox.showerror("Error", f"Failed to check status: {str(e)}")
def _start_auto_refresh(self, interval_seconds=30):
"""Start automatic status refresh"""
def refresh():
if hasattr(self, 'dialog') and self.dialog.winfo_exists():
# Refresh all jobs
for job_id in list(self.processor.jobs.keys()):
try:
job = self.processor.jobs[job_id]
if job.status in [AsyncAPIStatus.PENDING, AsyncAPIStatus.PROCESSING]:
self.processor.check_job_status(job_id)
except:
pass
self._refresh_jobs_list()
# Schedule next refresh
self.dialog.after(interval_seconds * 1000, refresh)
# Start the refresh cycle
refresh()
def _retrieve_selected_results(self):
"""Retrieve results from selected job"""
if not self.selected_job_id:
messagebox.showwarning("No Selection", "Please select a job to retrieve results")
return
# Check job status first
job = self.processor.jobs.get(self.selected_job_id)
if not job:
messagebox.showerror("Error", "Selected job not found")
return
if job.status != AsyncAPIStatus.COMPLETED:
messagebox.showwarning(
"Job Not Complete",
f"This job is currently {job.status.value}.\n"
"Only completed jobs can have results retrieved."
)
return
try:
# Set cursor to busy (with safety check)
if hasattr(self, 'dialog') and self.dialog.winfo_exists():
self.dialog.config(cursor="wait")
self.dialog.update()
# Retrieve results
self._handle_completed_job(self.selected_job_id)
except Exception as e:
self._log(f"β Error retrieving results: {e}")
messagebox.showerror("Error", f"Failed to retrieve results: {str(e)}")
finally:
# Reset cursor (with safety check)
if hasattr(self, 'dialog') and self.dialog.winfo_exists():
try:
self.dialog.config(cursor="")
except tk.TclError:
# Dialog was closed, ignore
pass
def _cancel_selected_job(self):
"""Cancel selected job"""
if not self.selected_job_id:
messagebox.showwarning("No Selection", "Please select a job to cancel")
return
job = self.processor.jobs.get(self.selected_job_id)
if not job:
messagebox.showerror("Error", "Selected job not found")
return
if job.status in [AsyncAPIStatus.COMPLETED, AsyncAPIStatus.FAILED, AsyncAPIStatus.CANCELLED]:
messagebox.showwarning(
"Cannot Cancel",
f"This job is already {job.status.value}"
)
return
# Confirm cancellation
if not messagebox.askyesno(
"Cancel Job",
f"Are you sure you want to cancel this job?\n\n"
f"Job ID: {job.job_id}\n"
f"Status: {job.status.value}"
):
return
try:
api_key = self.gui.api_key_entry.get().strip()
if job.provider == 'openai':
headers = {'Authorization': f'Bearer {api_key}'}
response = requests.post(
f'https://api.openai.com/v1/batches/{job.job_id}/cancel',
headers=headers
)
if response.status_code == 200:
job.status = AsyncAPIStatus.CANCELLED
job.updated_at = datetime.now()
self.processor._save_jobs()
self._refresh_jobs_list()
messagebox.showinfo("Job Cancelled", "Job has been cancelled successfully")
else:
messagebox.showerror("Error", f"Failed to cancel job: {response.text}")
elif job.provider == 'gemini':
# Gemini batch cancellation using REST API
headers = {'x-goog-api-key': api_key}
# Format: batches/123456 -> https://generativelanguage.googleapis.com/v1beta/batches/123456:cancel
batch_name = job.job_id if job.job_id.startswith('batches/') else f'batches/{job.job_id}'
response = requests.post(
f'https://generativelanguage.googleapis.com/v1beta/{batch_name}:cancel',
headers=headers
)
if response.status_code == 200:
job.status = AsyncAPIStatus.CANCELLED
job.updated_at = datetime.now()
self.processor._save_jobs()
self._refresh_jobs_list()
messagebox.showinfo("Job Cancelled", "Gemini batch job has been cancelled successfully")
else:
messagebox.showerror("Error", f"Failed to cancel Gemini job: {response.text}")
elif job.provider == 'anthropic':
# Anthropic doesn't support cancellation via API yet
messagebox.showinfo(
"Not Supported",
"Anthropic doesn't support job cancellation via API.\n"
"The job will be marked as cancelled locally only."
)
job.status = AsyncAPIStatus.CANCELLED
job.updated_at = datetime.now()
self.processor._save_jobs()
self._refresh_jobs_list()
else:
# For other providers, just mark as cancelled locally
messagebox.showinfo(
"Local Cancellation",
f"{job.provider.title()} cancellation not implemented.\n"
"The job will be marked as cancelled locally only."
)
job.status = AsyncAPIStatus.CANCELLED
job.updated_at = datetime.now()
self.processor._save_jobs()
self._refresh_jobs_list()
except Exception as e:
messagebox.showerror("Error", f"Failed to cancel job: {str(e)}")
def _cancel_openai_job(self, job, api_key):
"""Cancel OpenAI batch job"""
headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}
# OpenAI has a specific cancel endpoint
cancel_url = f"https://api.openai.com/v1/batches/{job.job_id}/cancel"
response = requests.post(cancel_url, headers=headers)
if response.status_code not in [200, 204]:
raise Exception(f"OpenAI cancellation failed: {response.text}")
logger.info(f"OpenAI job {job.job_id} cancelled successfully")
def _cancel_anthropic_job(self, job, api_key):
"""Cancel Anthropic batch job"""
headers = {
'X-API-Key': api_key,
'anthropic-version': '2023-06-01',
'anthropic-beta': 'message-batches-2024-09-24'
}
# Anthropic uses DELETE method for cancellation
cancel_url = f"https://api.anthropic.com/v1/messages/batches/{job.job_id}"
response = requests.delete(cancel_url, headers=headers)
if response.status_code not in [200, 204]:
raise Exception(f"Anthropic cancellation failed: {response.text}")
logger.info(f"Anthropic job {job.job_id} cancelled successfully")
def _cancel_gemini_job(self, job, api_key):
"""Cancel Gemini batch job"""
try:
from google import genai
# Create client
client = genai.Client(api_key=api_key)
# Try to cancel using the SDK
# Note: The SDK might not have a cancel method yet
if hasattr(client.batches, 'cancel'):
client.batches.cancel(name=job.job_id)
logger.info(f"Gemini job {job.job_id} cancelled successfully")
else:
# If SDK doesn't support cancellation, inform the user
raise Exception(
"Gemini batch cancellation is not supported yet.\n"
"The job will continue to run and complete within 24 hours.\n"
"You can check the status later to retrieve results."
)
except AttributeError:
# SDK doesn't have cancel method
raise Exception(
"Gemini batch cancellation is not available in the current SDK.\n"
"The job will continue to run and complete within 24 hours."
)
except Exception as e:
# Check if it's a permission error
if "403" in str(e) or "PERMISSION_DENIED" in str(e):
raise Exception(
"Gemini batch jobs cannot be cancelled once submitted.\n"
"The job will complete within 24 hours and you can retrieve results then."
)
else:
# Re-raise other errors
raise
def _cancel_mistral_job(self, job, api_key):
"""Cancel Mistral batch job"""
headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}
# Mistral batch cancellation endpoint
cancel_url = f"https://api.mistral.ai/v1/batch/jobs/{job.job_id}/cancel"
response = requests.post(cancel_url, headers=headers)
if response.status_code not in [200, 204]:
raise Exception(f"Mistral cancellation failed: {response.text}")
logger.info(f"Mistral job {job.job_id} cancelled successfully")
def _cancel_groq_job(self, job, api_key):
"""Cancel Groq batch job"""
# Groq uses OpenAI-compatible endpoints
headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}
cancel_url = f"https://api.groq.com/openai/v1/batch/{job.job_id}/cancel"
response = requests.post(cancel_url, headers=headers)
if response.status_code not in [200, 204]:
raise Exception(f"Groq cancellation failed: {response.text}")
logger.info(f"Groq job {job.job_id} cancelled successfully")
def _estimate_cost(self):
"""Estimate cost for current file"""
# Get current file info
if not hasattr(self.gui, 'file_path') or not self.gui.file_path:
messagebox.showwarning("No File", "Please select a file first")
return
try:
# Show analyzing message
self.cost_info_label.config(text="Analyzing file...")
self.dialog.update()
file_path = self.gui.file_path
model = self.gui.model_var.get()
# Calculate overhead tokens (system prompt + glossary)
overhead_tokens = 0
# Count system prompt tokens
system_prompt = self.gui.prompt_text.get("1.0", "end").strip()
if system_prompt:
overhead_tokens += self.count_tokens(system_prompt, model)
logger.info(f"System prompt tokens: {overhead_tokens}")
# Count glossary tokens if enabled
glossary_tokens = 0
# Check if glossary should be appended - match the logic from _prepare_environment_variables
if (hasattr(self.gui, 'manual_glossary_path') and
self.gui.manual_glossary_path and
hasattr(self.gui, 'append_glossary_var') and
self.gui.append_glossary_var.get()): # This is the key check!
try:
glossary_path = self.gui.manual_glossary_path
logger.info(f"Loading glossary from: {glossary_path}")
if os.path.exists(glossary_path):
with open(glossary_path, 'r', encoding='utf-8') as f:
glossary_data = json.load(f)
# Format glossary same way as in translation
#glossary_text = self._format_glossary_for_prompt(glossary_data)
# Add append prompt if available
append_prompt = self.gui.append_glossary_prompt if hasattr(self.gui, 'append_glossary_prompt') else ''
if append_prompt:
if '{glossary}' in append_prompt:
glossary_text = append_prompt.replace('{glossary}', glossary_text)
else:
glossary_text = f"{append_prompt}\n{glossary_text}"
else:
glossary_text = f"Glossary:\n{glossary_text}"
glossary_tokens = self.count_tokens(glossary_text, model)
overhead_tokens += glossary_tokens
logger.info(f"Loaded glossary with {glossary_tokens} tokens")
else:
print(f"Glossary file not found: {glossary_path}")
except Exception as e:
print(f"Failed to load glossary: {e}")
logger.info(f"Total overhead per chapter: {overhead_tokens} tokens")
# Actually extract chapters and count tokens
num_chapters = 0
total_content_tokens = 0 # Just the chapter content
chapters_needing_chunking = 0
if file_path.lower().endswith('.epub'):
# Import and use EPUB extraction
try:
import ebooklib
from ebooklib import epub
from bs4 import BeautifulSoup
book = epub.read_epub(file_path)
chapters = []
# Extract text chapters
for item in book.get_items():
if item.get_type() == ebooklib.ITEM_DOCUMENT:
soup = BeautifulSoup(item.get_content(), 'html.parser')
text = soup.get_text(separator='\n').strip()
if len(text) > 500: # Minimum chapter length
chapters.append(text)
num_chapters = len(chapters)
# Count tokens for each chapter (sample more for better accuracy)
sample_size = min(20, num_chapters) # Sample up to 20 chapters for better accuracy
sampled_content_tokens = 0
for i, chapter_text in enumerate(chapters[:sample_size]):
# Count just the content tokens
content_tokens = self.count_tokens(chapter_text, model)
sampled_content_tokens += content_tokens
# Check if needs chunking (including overhead)
total_chapter_tokens = content_tokens + overhead_tokens
token_limit = int(self.gui.token_limit_entry.get() or 200000)
if total_chapter_tokens > token_limit * 0.8:
chapters_needing_chunking += 1
# Update progress
if i % 5 == 0:
self.cost_info_label.config(text=f"Analyzing chapters... {i+1}/{sample_size}")
self.dialog.update()
# Calculate average based on actual sample
if sample_size > 0:
avg_content_tokens_per_chapter = sampled_content_tokens // sample_size
# Extrapolate chunking needs if we didn't sample all
if num_chapters > sample_size:
chapters_needing_chunking = int(chapters_needing_chunking * (num_chapters / sample_size))
else:
avg_content_tokens_per_chapter = 15000 # Default
except Exception as e:
print(f"Failed to analyze EPUB: {e}")
# Fall back to estimates
num_chapters = 50
avg_content_tokens_per_chapter = 15000
elif file_path.lower().endswith('.txt'):
# Import and use TXT extraction
try:
from txt_processor import TextFileProcessor
processor = TextFileProcessor(file_path, '')
chapters = processor.extract_chapters()
num_chapters = len(chapters)
# Count tokens
sample_size = min(20, num_chapters) # Sample up to 20 chapters
sampled_content_tokens = 0
for i, chapter_text in enumerate(chapters[:sample_size]):
# Count just the content tokens
content_tokens = self.count_tokens(chapter_text, model)
sampled_content_tokens += content_tokens
# Check if needs chunking (including overhead)
total_chapter_tokens = content_tokens + overhead_tokens
token_limit = int(self.gui.token_limit_entry.get() or 200000)
if total_chapter_tokens > token_limit * 0.8:
chapters_needing_chunking += 1
# Update progress
if i % 5 == 0:
self.cost_info_label.config(text=f"Analyzing chapters... {i+1}/{sample_size}")
self.dialog.update()
# Calculate average based on actual sample
if sample_size > 0:
avg_content_tokens_per_chapter = sampled_content_tokens // sample_size
# Extrapolate chunking needs
if num_chapters > sample_size:
chapters_needing_chunking = int(chapters_needing_chunking * (num_chapters / sample_size))
else:
avg_content_tokens_per_chapter = 15000 # Default
except Exception as e:
print(f"Failed to analyze TXT: {e}")
# Fall back to estimates
num_chapters = 50
avg_content_tokens_per_chapter = 15000
else:
# Unsupported format
self.cost_info_label.config(
text="Unsupported file format. Only EPUB and TXT are supported."
)
return
# Calculate costs
processable_chapters = num_chapters - chapters_needing_chunking
if processable_chapters <= 0:
self.cost_info_label.config(
text=f"Warning: All {num_chapters} chapters require chunking.\n"
f"Async APIs do not support chunked chapters.\n"
f"Consider using regular batch translation instead."
)
return
# Add overhead to get total average tokens per chapter
avg_total_tokens_per_chapter = avg_content_tokens_per_chapter + overhead_tokens
# Get the translation compression factor from GUI
compression_factor = float(self.gui.compression_factor_var.get() or 1.0)
# Get accurate cost estimate
async_cost, regular_cost = self.processor.estimate_cost(
processable_chapters,
avg_total_tokens_per_chapter, # Now includes content + system prompt + glossary
model,
compression_factor
)
# Update any existing jobs for this file with the accurate estimate
current_file = self.gui.file_path
for job_id, job in self.processor.jobs.items():
# Check if this job is for the current file and model
if (job.metadata and
job.metadata.get('source_file') == current_file and
job.model == model and
job.status in [AsyncAPIStatus.PENDING, AsyncAPIStatus.PROCESSING]):
# Update the cost estimate
job.cost_estimate = async_cost
job.updated_at = datetime.now()
# Save updated jobs
self.processor._save_jobs()
# Refresh the display
self._refresh_jobs_list()
# Build detailed message
cost_text = f"File analysis complete!\n\n"
cost_text += f"Total chapters: {num_chapters}\n"
cost_text += f"Average content tokens per chapter: {avg_content_tokens_per_chapter:,}\n"
cost_text += f"Overhead per chapter: {overhead_tokens:,} tokens"
if glossary_tokens > 0:
cost_text += f" (system: {overhead_tokens - glossary_tokens:,}, glossary: {glossary_tokens:,})"
cost_text += f"\nTotal input tokens per chapter: {avg_total_tokens_per_chapter:,}\n"
if chapters_needing_chunking > 0:
cost_text += f"\nChapters requiring chunking: {chapters_needing_chunking} (will be skipped)\n"
cost_text += f"Processable chapters: {processable_chapters}\n"
cost_text += f"\nEstimated cost for {processable_chapters} chapters:\n"
cost_text += f"Regular processing: ${regular_cost:.2f}\n"
cost_text += f"Async processing: ${async_cost:.2f} (50% savings: ${regular_cost - async_cost:.2f})"
# Add note about token calculation
cost_text += f"\n\nNote: Costs include input (~{avg_total_tokens_per_chapter:,}) and "
cost_text += f"output (~{int(avg_content_tokens_per_chapter * compression_factor):,}) tokens per chapter."
self.cost_info_label.config(text=cost_text)
except Exception as e:
self.cost_info_label.config(
text=f"Error estimating cost: {str(e)}"
)
print(f"Cost estimation error: {traceback.format_exc()}")
def count_tokens(self, text, model):
"""Count tokens in text (content only - system prompt and glossary are counted separately)"""
try:
import tiktoken
# Get base encoding for model
if model.startswith(('gpt-4', 'gpt-3')):
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
encoding = tiktoken.get_encoding("cl100k_base")
elif model.startswith('claude'):
encoding = tiktoken.get_encoding("cl100k_base")
else:
encoding = tiktoken.get_encoding("cl100k_base")
# Just count the text tokens - don't include system/glossary here
# They are counted separately in _estimate_cost to avoid confusion
text_tokens = len(encoding.encode(text))
return text_tokens
except Exception as e:
# Fallback: estimate ~4 characters per token
return len(text) // 4
def _start_processing(self):
"""Start async processing"""
model = self.gui.model_var.get()
if not self.processor.supports_async(model):
messagebox.showerror(
"Not Supported",
f"Model '{model}' does not support async processing.\n"
"Supported providers: Gemini, Anthropic, OpenAI, Mistral, Groq"
)
return
# Add special check for Gemini
if model.lower().startswith('gemini'):
response = messagebox.askyesno(
"Gemini Batch API",
"Note: Gemini's batch API may not be publicly available yet.\n"
"This feature is experimental for Gemini models.\n\n"
"Would you like to try anyway?"
)
if not response:
return
if not self.processor.supports_async(model):
messagebox.showerror(
"Not Supported",
f"Model '{model}' does not support async processing.\n"
"Supported providers: Gemini, Anthropic, OpenAI, Mistral, Groq"
)
return
if not hasattr(self.gui, 'file_path') or not self.gui.file_path:
messagebox.showwarning("No File", "Please select a file to translate first")
return
# Confirm start
if not messagebox.askyesno(
"Start Async Processing",
"Start async batch processing?\n\n"
"This will submit all chapters for processing at 50% discount.\n"
"Processing may take up to 24 hours."
):
return
# Disable buttons during processing
self.start_button.config(state='disabled')
# Start processing in background thread
self.processing_thread = threading.Thread(
target=self._async_processing_worker,
daemon=True
)
self.processing_thread.start()
def _async_processing_worker(self):
"""Worker thread for async processing"""
try:
self._log("Starting async processing preparation...")
# Get all settings from GUI
file_path = self.gui.file_path
model = self.gui.model_var.get()
api_key = self.gui.api_key_entry.get().strip()
if not api_key:
self._show_error("API key is required")
return
# Prepare environment variables like the main translation
env_vars = self._prepare_environment_variables()
# Extract chapters
self._log("Extracting chapters from file...")
chapters, chapter_mapping = self._extract_chapters_for_async(file_path, env_vars) # CHANGED: Now unpacking both values
if not chapters:
self._show_error("No chapters found in file")
return
self._log(f"Found {len(chapters)} chapters to process")
# Check for chapters that need chunking
chapters_to_process = []
skipped_count = 0
for chapter in chapters:
if chapter.get('needs_chunking', False):
skipped_count += 1
self._log(f"Skipping chapter {chapter['number']} - requires chunking")
else:
chapters_to_process.append(chapter)
if skipped_count > 0:
self._log(f"β οΈ Skipped {skipped_count} chapters that require chunking")
if not chapters_to_process:
self._show_error("All chapters require chunking. Async APIs don't support chunked chapters.")
return
# Prepare batch request
self._log("Preparing batch request...")
batch_data = self.processor.prepare_batch_request(chapters_to_process, model)
# Submit batch
self._log("Submitting batch to API...")
job = self._submit_batch_sync(batch_data, model, api_key)
# Save job with chapter mapping in metadata
job.metadata = job.metadata or {}
job.metadata['chapter_mapping'] = chapter_mapping # ADDED: Store mapping for later use
# Save job
self.processor.jobs[job.job_id] = job
self.processor._save_jobs()
# Update UI
self.dialog.after(0, self._refresh_jobs_list)
self._log(f"β
Batch submitted successfully! Job ID: {job.job_id}")
# Show success message
self._show_info(
"Batch Submitted",
f"Successfully submitted {len(chapters_to_process)} chapters for async processing.\n\n"
f"Job ID: {job.job_id}\n\n"
"You can close this dialog and check back later for results.\n\n"
"Tip: Use the 'Estimate Cost Only' button to get accurate cost estimates before submitting."
)
# Start polling if requested
if self.wait_for_completion_var.get():
self._start_polling(job.job_id)
except Exception as e:
self._log(f"β Error: {str(e)}")
print(f"Async processing error: {traceback.format_exc()}")
self._show_error(f"Failed to start async processing: {str(e)}")
finally:
# Re-enable button
self.dialog.after(0, lambda: self.start_button.config(state='normal'))
def _prepare_environment_variables(self):
"""Prepare environment variables from GUI settings"""
env_vars = {}
# Core settings
env_vars['MODEL'] = self.gui.model_var.get()
env_vars['API_KEY'] = self.gui.api_key_entry.get().strip()
env_vars['OPENAI_API_KEY'] = env_vars['API_KEY']
env_vars['OPENAI_OR_Gemini_API_KEY'] = env_vars['API_KEY']
env_vars['GEMINI_API_KEY'] = env_vars['API_KEY']
env_vars['PROFILE_NAME'] = self.gui.lang_var.get().lower()
env_vars['CONTEXTUAL'] = '1' if self.gui.contextual_var.get() else '0'
env_vars['MAX_OUTPUT_TOKENS'] = str(self.gui.max_output_tokens)
env_vars['SYSTEM_PROMPT'] = self.gui.prompt_text.get("1.0", "end").strip()
env_vars['TRANSLATION_TEMPERATURE'] = str(self.gui.trans_temp.get())
env_vars['TRANSLATION_HISTORY_LIMIT'] = str(self.gui.trans_history.get())
# API settings
env_vars['SEND_INTERVAL_SECONDS'] = str(self.gui.delay_entry.get())
env_vars['TOKEN_LIMIT'] = self.gui.token_limit_entry.get() if hasattr(self.gui, 'token_limit_entry') else '200000'
# Book title translation
env_vars['TRANSLATE_BOOK_TITLE'] = "1" if self.gui.translate_book_title_var.get() else "0"
env_vars['BOOK_TITLE_PROMPT'] = self.gui.book_title_prompt if hasattr(self.gui, 'book_title_prompt') else ''
env_vars['BOOK_TITLE_SYSTEM_PROMPT'] = self.gui.config.get('book_title_system_prompt',
"You are a translator. Respond with only the translated text, nothing else. Do not add any explanation or additional content.")
# Processing options
env_vars['CHAPTER_RANGE'] = self.gui.chapter_range_entry.get().strip() if hasattr(self.gui, 'chapter_range_entry') else ''
env_vars['REMOVE_AI_ARTIFACTS'] = "1" if self.gui.REMOVE_AI_ARTIFACTS_var.get() else "0"
env_vars['BATCH_TRANSLATION'] = "1" if self.gui.batch_translation_var.get() else "0"
env_vars['BATCH_SIZE'] = self.gui.batch_size_var.get()
env_vars['CONSERVATIVE_BATCHING'] = "1" if self.gui.conservative_batching_var.get() else "0"
# Anti-duplicate parameters
env_vars['ENABLE_ANTI_DUPLICATE'] = '1' if hasattr(self.gui, 'enable_anti_duplicate_var') and self.gui.enable_anti_duplicate_var.get() else '0'
env_vars['TOP_P'] = str(self.gui.top_p_var.get()) if hasattr(self.gui, 'top_p_var') else '1.0'
env_vars['TOP_K'] = str(self.gui.top_k_var.get()) if hasattr(self.gui, 'top_k_var') else '0'
env_vars['FREQUENCY_PENALTY'] = str(self.gui.frequency_penalty_var.get()) if hasattr(self.gui, 'frequency_penalty_var') else '0.0'
env_vars['PRESENCE_PENALTY'] = str(self.gui.presence_penalty_var.get()) if hasattr(self.gui, 'presence_penalty_var') else '0.0'
env_vars['REPETITION_PENALTY'] = str(self.gui.repetition_penalty_var.get()) if hasattr(self.gui, 'repetition_penalty_var') else '1.0'
env_vars['CANDIDATE_COUNT'] = str(self.gui.candidate_count_var.get()) if hasattr(self.gui, 'candidate_count_var') else '1'
env_vars['CUSTOM_STOP_SEQUENCES'] = self.gui.custom_stop_sequences_var.get() if hasattr(self.gui, 'custom_stop_sequences_var') else ''
env_vars['LOGIT_BIAS_ENABLED'] = '1' if hasattr(self.gui, 'logit_bias_enabled_var') and self.gui.logit_bias_enabled_var.get() else '0'
env_vars['LOGIT_BIAS_STRENGTH'] = str(self.gui.logit_bias_strength_var.get()) if hasattr(self.gui, 'logit_bias_strength_var') else '-0.5'
env_vars['BIAS_COMMON_WORDS'] = '1' if hasattr(self.gui, 'bias_common_words_var') and self.gui.bias_common_words_var.get() else '0'
env_vars['BIAS_REPETITIVE_PHRASES'] = '1' if hasattr(self.gui, 'bias_repetitive_phrases_var') and self.gui.bias_repetitive_phrases_var.get() else '0'
# Glossary settings
env_vars['MANUAL_GLOSSARY'] = self.gui.manual_glossary_path if hasattr(self.gui, 'manual_glossary_path') and self.gui.manual_glossary_path else ''
env_vars['DISABLE_AUTO_GLOSSARY'] = "0" if self.gui.enable_auto_glossary_var.get() else "1"
env_vars['DISABLE_GLOSSARY_TRANSLATION'] = "0" if self.gui.enable_auto_glossary_var.get() else "1"
env_vars['APPEND_GLOSSARY'] = "1" if self.gui.append_glossary_var.get() else "0"
env_vars['APPEND_GLOSSARY_PROMPT'] = self.gui.append_glossary_prompt if hasattr(self.gui, 'append_glossary_prompt') else ''
env_vars['GLOSSARY_MIN_FREQUENCY'] = self.gui.glossary_min_frequency_var.get()
env_vars['GLOSSARY_MAX_NAMES'] = self.gui.glossary_max_names_var.get()
env_vars['GLOSSARY_MAX_TITLES'] = self.gui.glossary_max_titles_var.get()
env_vars['GLOSSARY_BATCH_SIZE'] = self.gui.glossary_batch_size_var.get()
env_vars['GLOSSARY_DUPLICATE_KEY_MODE'] = self.gui.config.get('glossary_duplicate_key_mode', 'auto')
env_vars['GLOSSARY_DUPLICATE_CUSTOM_FIELD'] = self.gui.config.get('glossary_duplicate_custom_field', '')
# History and summary settings
env_vars['TRANSLATION_HISTORY_ROLLING'] = "1" if self.gui.translation_history_rolling_var.get() else "0"
env_vars['USE_ROLLING_SUMMARY'] = "1" if self.gui.config.get('use_rolling_summary') else "0"
env_vars['SUMMARY_ROLE'] = self.gui.config.get('summary_role', 'user')
env_vars['ROLLING_SUMMARY_EXCHANGES'] = self.gui.rolling_summary_exchanges_var.get()
env_vars['ROLLING_SUMMARY_MODE'] = self.gui.rolling_summary_mode_var.get()
env_vars['ROLLING_SUMMARY_SYSTEM_PROMPT'] = self.gui.rolling_summary_system_prompt if hasattr(self.gui, 'rolling_summary_system_prompt') else ''
env_vars['ROLLING_SUMMARY_USER_PROMPT'] = self.gui.rolling_summary_user_prompt if hasattr(self.gui, 'rolling_summary_user_prompt') else ''
env_vars['ROLLING_SUMMARY_MAX_ENTRIES'] = self.gui.rolling_summary_max_entries_var.get() if hasattr(self.gui, 'rolling_summary_max_entries_var') else '10'
# Retry and error handling settings
env_vars['EMERGENCY_PARAGRAPH_RESTORE'] = "1" if self.gui.emergency_restore_var.get() else "0"
env_vars['RETRY_TRUNCATED'] = "1" if self.gui.retry_truncated_var.get() else "0"
env_vars['MAX_RETRY_TOKENS'] = self.gui.max_retry_tokens_var.get()
env_vars['RETRY_DUPLICATE_BODIES'] = "1" if self.gui.retry_duplicate_var.get() else "0"
env_vars['RETRY_TIMEOUT'] = "1" if self.gui.retry_timeout_var.get() else "0"
env_vars['CHUNK_TIMEOUT'] = self.gui.chunk_timeout_var.get()
# Image processing
env_vars['ENABLE_IMAGE_TRANSLATION'] = "1" if self.gui.enable_image_translation_var.get() else "0"
env_vars['PROCESS_WEBNOVEL_IMAGES'] = "1" if self.gui.process_webnovel_images_var.get() else "0"
env_vars['WEBNOVEL_MIN_HEIGHT'] = self.gui.webnovel_min_height_var.get()
env_vars['MAX_IMAGES_PER_CHAPTER'] = self.gui.max_images_per_chapter_var.get()
env_vars['IMAGE_API_DELAY'] = '1.0'
env_vars['SAVE_IMAGE_TRANSLATIONS'] = '1'
env_vars['IMAGE_CHUNK_HEIGHT'] = self.gui.image_chunk_height_var.get()
env_vars['HIDE_IMAGE_TRANSLATION_LABEL'] = "1" if self.gui.hide_image_translation_label_var.get() else "0"
# Advanced settings
env_vars['REINFORCEMENT_FREQUENCY'] = self.gui.reinforcement_freq_var.get()
env_vars['RESET_FAILED_CHAPTERS'] = "1" if self.gui.reset_failed_chapters_var.get() else "0"
env_vars['DUPLICATE_LOOKBACK_CHAPTERS'] = self.gui.duplicate_lookback_var.get()
env_vars['DUPLICATE_DETECTION_MODE'] = self.gui.duplicate_detection_mode_var.get()
env_vars['CHAPTER_NUMBER_OFFSET'] = str(self.gui.chapter_number_offset_var.get())
env_vars['COMPRESSION_FACTOR'] = self.gui.compression_factor_var.get()
extraction_mode = self.gui.extraction_mode_var.get() if hasattr(self.gui, 'extraction_mode_var') else 'smart'
env_vars['COMPREHENSIVE_EXTRACTION'] = "1" if extraction_mode in ['comprehensive', 'full'] else "0"
env_vars['EXTRACTION_MODE'] = extraction_mode
env_vars['DISABLE_ZERO_DETECTION'] = "1" if self.gui.disable_zero_detection_var.get() else "0"
env_vars['USE_HEADER_AS_OUTPUT'] = "1" if self.gui.use_header_as_output_var.get() else "0"
env_vars['ENABLE_DECIMAL_CHAPTERS'] = "1" if self.gui.enable_decimal_chapters_var.get() else "0"
env_vars['ENABLE_WATERMARK_REMOVAL'] = "1" if self.gui.enable_watermark_removal_var.get() else "0"
env_vars['ADVANCED_WATERMARK_REMOVAL'] = "1" if self.gui.advanced_watermark_removal_var.get() else "0"
env_vars['SAVE_CLEANED_IMAGES'] = "1" if self.gui.save_cleaned_images_var.get() else "0"
# EPUB specific settings
env_vars['DISABLE_EPUB_GALLERY'] = "1" if self.gui.disable_epub_gallery_var.get() else "0"
env_vars['FORCE_NCX_ONLY'] = '1' if self.gui.force_ncx_only_var.get() else '0'
# Special handling for Gemini safety filters
env_vars['DISABLE_GEMINI_SAFETY'] = str(self.gui.config.get('disable_gemini_safety', False)).lower()
# AI Hunter settings (if enabled)
if 'ai_hunter_config' in self.gui.config:
env_vars['AI_HUNTER_CONFIG'] = json.dumps(self.gui.config['ai_hunter_config'])
# Output settings
env_vars['EPUB_OUTPUT_DIR'] = os.getcwd()
output_path = self.gui.output_entry.get().strip() if hasattr(self.gui, 'output_entry') else ''
if output_path:
env_vars['OUTPUT_DIR'] = output_path
# File path (needed by some modules)
env_vars['EPUB_PATH'] = self.gui.file_path
return env_vars
def _extract_chapters_for_async(self, file_path, env_vars):
"""Extract chapters and prepare them for async processing"""
chapters = []
original_basename = None
chapter_mapping = {} # Map custom_id to chapter info
try:
if file_path.lower().endswith('.epub'):
# Use direct ZIP reading to avoid ebooklib's manifest validation
import zipfile
from bs4 import BeautifulSoup
raw_chapters = []
try:
with zipfile.ZipFile(file_path, 'r') as zf:
# Get all HTML/XHTML files
html_files = [f for f in zf.namelist() if f.endswith(('.html', '.xhtml', '.htm')) and not f.startswith('__MACOSX')]
html_files.sort() # Sort to maintain order
for idx, html_file in enumerate(html_files):
try:
content = zf.read(html_file)
soup = BeautifulSoup(content, 'html.parser')
# Remove all image tags
for img in soup.find_all('img'):
img.decompose()
# Remove all link tags that might reference CSS or other files
for link in soup.find_all('link'):
link.decompose()
chapter_text = soup.get_text(separator='\n').strip()
if len(chapter_text) > 500: # Minimum chapter length
chapter_num = idx + 1
# Try to extract chapter number from content
for element in soup.find_all(['h1', 'h2', 'h3', 'title']):
text = element.get_text().strip()
match = re.search(r'chapter\s*(\d+)', text, re.IGNORECASE)
if match:
chapter_num = int(match.group(1))
break
raw_chapters.append((chapter_num, chapter_text, html_file))
except Exception as e:
print(f"Error reading {html_file}: {e}")
continue
except Exception as e:
print(f"Failed to read EPUB as ZIP: {e}")
raise ValueError(f"Cannot read EPUB file: {str(e)}")
elif file_path.lower().endswith('.txt'):
# Import TXT processing
from txt_processor import TextFileProcessor
processor = TextFileProcessor(file_path, '')
txt_chapters = processor.extract_chapters()
raw_chapters = [(i+1, text, f"section_{i+1:04d}.txt") for i, text in enumerate(txt_chapters)]
else:
raise ValueError(f"Unsupported file type: {file_path}")
if not raw_chapters:
raise ValueError("No valid chapters found in file")
# Process each chapter to prepare for API
for idx, (chapter_num, content, original_filename) in enumerate(raw_chapters):
# Count tokens
token_count = self.count_tokens(content, env_vars['MODEL'])
# Check if needs chunking
token_limit = int(env_vars.get('TOKEN_LIMIT', '200000'))
needs_chunking = token_count > token_limit * 0.8 # 80% threshold
# Prepare messages format
messages = self._prepare_chapter_messages(content, env_vars)
custom_id = f"chapter_{chapter_num}"
chapter_data = {
'id': custom_id,
'number': chapter_num,
'content': content,
'messages': messages,
'temperature': float(env_vars.get('TRANSLATION_TEMPERATURE', '0.3')),
'max_tokens': int(env_vars['MAX_OUTPUT_TOKENS']),
'needs_chunking': needs_chunking,
'token_count': token_count,
'original_basename': original_filename # Use original_filename instead of undefined original_basename
}
chapters.append(chapter_data)
# Store mapping
chapter_mapping[custom_id] = {
'original_filename': original_filename,
'chapter_num': chapter_num
}
except Exception as e:
print(f"Failed to extract chapters: {e}")
raise
# Return both chapters and mapping
return chapters, chapter_mapping
def _delete_selected_job(self):
"""Delete selected job from the list"""
if not self.selected_job_id:
messagebox.showwarning("No Selection", "Please select a job to delete")
return
# Get job details for confirmation
job = self.processor.jobs.get(self.selected_job_id)
if not job:
messagebox.showerror("Error", "Selected job not found")
return
# Confirm deletion
response = messagebox.askyesno(
"Confirm Delete",
f"Are you sure you want to delete this job?\n\n"
f"Job ID: {job.job_id}\n"
f"Status: {job.status.value}\n"
f"Created: {job.created_at.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
"Note: This only removes the job from your local list.\n"
"The job may still be running on the server."
)
if response:
# Remove from jobs dictionary
del self.processor.jobs[self.selected_job_id]
# Save updated jobs
self.processor._save_jobs()
# Clear selection
self.selected_job_id = None
# Refresh the display
self._refresh_jobs_list()
messagebox.showinfo("Job Deleted", "Job removed from local list.")
def _clear_completed_jobs(self):
"""Clear all completed/failed/cancelled jobs"""
# Get list of jobs to remove
jobs_to_remove = []
for job_id, job in self.processor.jobs.items():
if job.status in [AsyncAPIStatus.COMPLETED, AsyncAPIStatus.FAILED,
AsyncAPIStatus.CANCELLED, AsyncAPIStatus.EXPIRED]:
jobs_to_remove.append(job_id)
if not jobs_to_remove:
messagebox.showinfo("No Jobs to Clear", "No completed/failed/cancelled jobs to clear.")
return
# Confirm
response = messagebox.askyesno(
"Clear Completed Jobs",
f"Remove {len(jobs_to_remove)} completed/failed/cancelled jobs from the list?\n\n"
"This will not affect any running jobs."
)
if response:
# Remove jobs
for job_id in jobs_to_remove:
del self.processor.jobs[job_id]
# Save
self.processor._save_jobs()
# Refresh
self._refresh_jobs_list()
messagebox.showinfo("Jobs Cleared", f"Removed {len(jobs_to_remove)} jobs from the list.")
def _prepare_chapter_messages(self, content, env_vars):
"""Prepare messages array for a chapter"""
messages = []
# System prompt
system_prompt = env_vars.get('SYSTEM_PROMPT', '')
# DEBUG: Log what we're sending
logger.info(f"Model: {env_vars.get('MODEL')}")
logger.info(f"System prompt length: {len(system_prompt)}")
logger.info(f"Content length: {len(content)}")
# Log the system prompt (first 200 chars)
logger.info(f"Using system prompt: {system_prompt[:200]}...")
# Add glossary if enabled
if (env_vars.get('MANUAL_GLOSSARY') and
env_vars.get('APPEND_GLOSSARY') == '1' and
env_vars.get('DISABLE_GLOSSARY_TRANSLATION') != '1'):
try:
glossary_path = env_vars['MANUAL_GLOSSARY']
with open(glossary_path, 'r', encoding='utf-8') as f:
glossary_data = json.load(f)
# TRUE BRUTE FORCE: Just dump the entire JSON
glossary_text = json.dumps(glossary_data, ensure_ascii=False, indent=2)
# Use the append prompt format if provided
append_prompt = env_vars.get('APPEND_GLOSSARY_PROMPT', '')
if append_prompt:
# Replace placeholder with actual glossary
if '{glossary}' in append_prompt:
glossary_section = append_prompt.replace('{glossary}', glossary_text)
else:
glossary_section = f"{append_prompt}\n{glossary_text}"
system_prompt = f"{system_prompt}\n\n{glossary_section}"
else:
# Default format
system_prompt = f"{system_prompt}\n\nGlossary:\n{glossary_text}"
logger.info(f"Glossary appended to system prompt ({len(glossary_text)} chars)")
# Log preview for debugging
if len(glossary_text) > 200:
logger.info(f"Glossary preview: {glossary_text[:200]}...")
else:
logger.info(f"Glossary: {glossary_text}")
except FileNotFoundError:
print(f"Glossary file not found: {env_vars.get('MANUAL_GLOSSARY')}")
except json.JSONDecodeError:
print(f"Invalid JSON in glossary file")
except Exception as e:
print(f"Failed to load glossary: {e}")
else:
# Log why glossary wasn't added
if not env_vars.get('MANUAL_GLOSSARY'):
logger.info("No glossary path specified")
elif env_vars.get('APPEND_GLOSSARY') != '1':
logger.info("Glossary append is disabled")
elif env_vars.get('DISABLE_GLOSSARY_TRANSLATION') == '1':
logger.info("Glossary translation is disabled")
messages.append({
'role': 'system',
'content': system_prompt
})
# Add context if enabled
if env_vars.get('CONTEXTUAL') == '1':
# This would need to load context from history
# For async, we might need to pre-generate context
logger.info("Note: Contextual mode enabled but not implemented for async yet")
# User message with chapter content
messages.append({
'role': 'user',
'content': content
})
return messages
def _submit_batch_sync(self, batch_data, model, api_key):
"""Submit batch synchronously (wrapper for async method)"""
provider = self.processor.get_provider_from_model(model)
if provider == 'openai':
return self.processor._submit_openai_batch_sync(batch_data, model, api_key)
elif provider == 'anthropic':
return self.processor._submit_anthropic_batch_sync(batch_data, model, api_key)
elif provider == 'gemini':
return self._submit_gemini_batch_sync(batch_data, model, api_key)
elif provider == 'mistral':
return self._submit_mistral_batch_sync(batch_data, model, api_key)
elif provider == 'groq':
return self._submit_groq_batch_sync(batch_data, model, api_key)
else:
raise ValueError(f"Unsupported provider: {provider}")
def _submit_gemini_batch_sync(self, batch_data, model, api_key):
"""Submit Gemini batch using the official Batch Mode API"""
try:
# Use the new Google Gen AI SDK
from google import genai
from google.genai import types
# Configure client with API key
client = genai.Client(api_key=api_key)
# Log for debugging
logger.info(f"Submitting Gemini batch with model: {model}")
logger.info(f"Number of requests: {len(batch_data['requests'])}")
# Create JSONL file for batch requests
import tempfile
with tempfile.NamedTemporaryFile(mode='w', suffix='.jsonl', delete=False, encoding='utf-8') as f:
for request in batch_data['requests']:
# Format for Gemini batch API
batch_line = {
"key": request['custom_id'],
"request": {
"contents": request['generateContentRequest']['contents'],
"generation_config": request['generateContentRequest'].get('generationConfig', {})
}
}
# Add safety settings if present
if 'safetySettings' in request['generateContentRequest']:
batch_line['request']['safety_settings'] = request['generateContentRequest']['safetySettings']
f.write(json.dumps(batch_line) + '\n')
batch_file_path = f.name
# Upload the batch file with explicit mime type
logger.info("Uploading batch file...")
# Use the upload config to specify mime type
upload_config = types.UploadFileConfig(
mime_type='application/jsonl', # Explicit JSONL mime type
display_name=f"batch_requests_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
)
uploaded_file = client.files.upload(
file=batch_file_path,
config=upload_config
)
logger.info(f"File uploaded: {uploaded_file.name}")
# Create batch job
batch_job = client.batches.create(
model=model,
src=uploaded_file.name,
config={
'display_name': f"glossarion_batch_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
}
)
logger.info(f"Gemini batch job created: {batch_job.name}")
# Clean up temp file
os.unlink(batch_file_path)
# Calculate cost estimate
total_tokens = sum(r.get('token_count', 15000) for r in batch_data['requests'])
async_cost, _ = self.processor.estimate_cost(
len(batch_data['requests']),
total_tokens // len(batch_data['requests']),
model
)
# Create job info
job = AsyncJobInfo(
job_id=batch_job.name,
provider='gemini',
model=model,
status=AsyncAPIStatus.PENDING,
created_at=datetime.now(),
updated_at=datetime.now(),
total_requests=len(batch_data['requests']),
cost_estimate=0.0, # No estimate initially
metadata={
'batch_info': {
'name': batch_job.name,
'state': batch_job.state.name if hasattr(batch_job, 'state') else 'PENDING',
'src_file': uploaded_file.name
},
'source_file': self.gui.file_path # Add this to track which file this job is for
}
)
return job
except ImportError:
print("Google Gen AI SDK not installed. Run: pip install google-genai")
raise Exception("Google Gen AI SDK not installed. Please run: pip install google-genai")
except Exception as e:
print(f"Gemini batch submission failed: {e}")
print(f"Full error: {traceback.format_exc()}")
raise
def _submit_mistral_batch_sync(self, batch_data, model, api_key):
"""Submit Mistral batch (synchronous version)"""
try:
headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}
response = requests.post(
'https://api.mistral.ai/v1/batch/jobs',
headers=headers,
json=batch_data
)
if response.status_code != 200:
raise Exception(f"Batch creation failed: {response.text}")
batch_info = response.json()
# Calculate cost estimate
total_tokens = sum(r.get('token_count', 15000) for r in batch_data['requests'])
async_cost, _ = self.processor.estimate_cost(
len(batch_data['requests']),
total_tokens // len(batch_data['requests']),
model
)
job = AsyncJobInfo(
job_id=batch_info['id'],
provider='mistral',
model=model,
status=AsyncAPIStatus.PENDING,
created_at=datetime.now(),
updated_at=datetime.now(),
total_requests=len(batch_data['requests']),
cost_estimate=async_cost,
metadata={'batch_info': batch_info}
)
return job
except Exception as e:
print(f"Mistral batch submission failed: {e}")
raise
def _submit_groq_batch_sync(self, batch_data, model, api_key):
"""Submit Groq batch (synchronous version)"""
# Groq uses OpenAI-compatible format
return self.processor._submit_openai_batch_sync(batch_data, model, api_key)
def _start_polling(self, job_id):
"""Start polling for job completion with progress updates"""
def poll():
try:
job = self.processor.check_job_status(job_id)
self._refresh_jobs_list()
# Update progress message
if job.total_requests > 0:
progress_pct = int((job.completed_requests / job.total_requests) * 100)
self._log(f"Progress: {progress_pct}% ({job.completed_requests}/{job.total_requests} chapters)")
if job.status == AsyncAPIStatus.COMPLETED:
self._log(f"β
Job {job_id} completed!")
self._handle_completed_job(job_id)
elif job.status in [AsyncAPIStatus.FAILED, AsyncAPIStatus.CANCELLED]:
self._log(f"β Job {job_id} {job.status.value}")
else:
# Continue polling with progress update
poll_interval = self.poll_interval_var.get() * 1000
self.dialog.after(poll_interval, poll)
except Exception as e:
self._log(f"β Polling error: {e}")
# Start polling
poll()
def _handle_completed_job(self, job_id):
"""Handle a completed job - retrieve results and save"""
try:
# Retrieve results
results = self.processor.retrieve_results(job_id)
if not results:
self._log("β No results retrieved from completed job")
return
# Get output directory - same name as input file, in exe location
if getattr(sys, 'frozen', False):
# Running as compiled exe - use exe directory
app_dir = os.path.dirname(sys.executable)
else:
# Running as script - use script directory
app_dir = os.path.dirname(os.path.abspath(__file__))
base_name = os.path.splitext(os.path.basename(self.gui.file_path))[0]
output_dir = os.path.join(app_dir, base_name)
# Handle existing directory
if os.path.exists(output_dir):
response = messagebox.askyesnocancel(
"Directory Exists",
f"The output directory already exists:\n{output_dir}\n\n"
"Yes = Overwrite\n"
"No = Create new with number\n"
"Cancel = Cancel operation"
)
if response is None:
return
elif response is False:
counter = 1
while os.path.exists(f"{output_dir}_{counter}"):
counter += 1
output_dir = f"{output_dir}_{counter}"
os.makedirs(output_dir, exist_ok=True)
# Extract ALL resources from EPUB (CSS, fonts, images)
self._log("π¦ Extracting EPUB resources...")
import zipfile
with zipfile.ZipFile(self.gui.file_path, 'r') as zf:
# Create resource directories
for res_type in ['css', 'fonts', 'images']:
os.makedirs(os.path.join(output_dir, res_type), exist_ok=True)
# Extract all resources
for file_path in zf.namelist():
if file_path.endswith('/'):
continue
file_lower = file_path.lower()
file_name = os.path.basename(file_path)
# Skip empty filenames
if not file_name:
continue
# Determine resource type and extract
if file_lower.endswith('.css'):
zf.extract(file_path, os.path.join(output_dir, 'css'))
elif file_lower.endswith(('.ttf', '.otf', '.woff', '.woff2')):
zf.extract(file_path, os.path.join(output_dir, 'fonts'))
elif file_lower.endswith(('.jpg', '.jpeg', '.png', '.gif', '.svg', '.webp')):
zf.extract(file_path, os.path.join(output_dir, 'images'))
# Extract chapter info and metadata from source EPUB
self._log("π Extracting metadata from source EPUB...")
import ebooklib
from ebooklib import epub
from bs4 import BeautifulSoup
from TransateKRtoEN import get_content_hash, should_retain_source_extension
# Extract metadata
metadata = {}
book = epub.read_epub(self.gui.file_path)
# Get book metadata
if book.get_metadata('DC', 'title'):
metadata['title'] = book.get_metadata('DC', 'title')[0][0]
if book.get_metadata('DC', 'creator'):
metadata['creator'] = book.get_metadata('DC', 'creator')[0][0]
if book.get_metadata('DC', 'language'):
metadata['language'] = book.get_metadata('DC', 'language')[0][0]
# Save metadata.json
metadata_path = os.path.join(output_dir, 'metadata.json')
with open(metadata_path, 'w', encoding='utf-8') as f:
json.dump(metadata, f, ensure_ascii=False, indent=2)
# Map chapter numbers to original info
chapter_map = {}
chapters_info = []
actual_chapter_num = 0
for item in book.get_items():
if item.get_type() == ebooklib.ITEM_DOCUMENT:
original_name = item.get_name()
original_basename = os.path.splitext(os.path.basename(original_name))[0]
soup = BeautifulSoup(item.get_content(), 'html.parser')
text = soup.get_text().strip()
if len(text) > 500: # Valid chapter
actual_chapter_num += 1
# Try to find chapter number in content
chapter_num = actual_chapter_num
for element in soup.find_all(['h1', 'h2', 'h3', 'title']):
element_text = element.get_text().strip()
match = re.search(r'chapter\s*(\d+)', element_text, re.IGNORECASE)
if match:
chapter_num = int(match.group(1))
break
# Calculate real content hash
content_hash = get_content_hash(text)
chapter_map[chapter_num] = {
'original_basename': original_basename,
'original_extension': os.path.splitext(original_name)[1],
'content_hash': content_hash,
'text_length': len(text),
'has_images': bool(soup.find_all('img'))
}
chapters_info.append({
'num': chapter_num,
'title': element_text if 'element_text' in locals() else f"Chapter {chapter_num}",
'original_filename': original_name,
'original_basename': original_basename,
'has_images': bool(soup.find_all('img')),
'text_length': len(text),
'content_hash': content_hash
})
# Save chapters_info.json
chapters_info_path = os.path.join(output_dir, 'chapters_info.json')
with open(chapters_info_path, 'w', encoding='utf-8') as f:
json.dump(chapters_info, f, ensure_ascii=False, indent=2)
# Create realistic progress tracking
progress_data = {
"version": "3.0",
"chapters": {},
"chapter_chunks": {},
"content_hashes": {},
"created": datetime.now().isoformat(),
"last_updated": datetime.now().isoformat(),
"total_chapters": len(results),
"completed_chapters": len(results),
"failed_chapters": 0,
"async_translated": True
}
# Sort results and save with proper filenames
sorted_results = sorted(results, key=lambda x: self._extract_chapter_number(x['custom_id']))
self._log("πΎ Saving translated chapters...")
for result in sorted_results:
chapter_num = self._extract_chapter_number(result['custom_id'])
# Get chapter info
chapter_info = chapter_map.get(chapter_num, {})
original_basename = chapter_info.get('original_basename', f"{chapter_num:04d}")
content_hash = chapter_info.get('content_hash', hashlib.md5(f"chapter_{chapter_num}".encode()).hexdigest())
# Save file with correct name (only once!)
retain_ext = should_retain_source_extension()
# Preserve compound extensions like .htm.xhtml when retaining
orig_name = chapter_info.get('original_filename') or chapter_info.get('original_basename')
if retain_ext and orig_name:
# Compute full extension suffix beyond the first dot from the left of the basename
full = os.path.basename(orig_name)
bn, ext1 = os.path.splitext(full)
full_ext = ''
while ext1:
full_ext = ext1 + full_ext
bn, ext1 = os.path.splitext(bn)
# If no extension found, default to .html
suffix = full_ext if full_ext else '.html'
filename = f"{original_basename}{suffix}"
elif retain_ext:
filename = f"{original_basename}.html"
else:
filename = f"response_{original_basename}.html"
file_path = os.path.join(output_dir, filename)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(result['content'])
# Add realistic progress entry
progress_data["chapters"][content_hash] = {
"status": "completed",
"output_file": filename,
"actual_num": chapter_num,
"chapter_num": chapter_num,
"content_hash": content_hash,
"original_basename": original_basename,
"started_at": datetime.now().isoformat(),
"completed_at": datetime.now().isoformat(),
"translation_time": 2.5, # Fake but realistic
"token_count": chapter_info.get('text_length', 5000) // 4, # Rough estimate
"model": self.gui.model_var.get(),
"from_async": True
}
# Add content hash tracking
progress_data["content_hashes"][content_hash] = {
"chapter_key": content_hash,
"chapter_num": chapter_num,
"status": "completed",
"index": chapter_num - 1
}
# Save realistic progress file
progress_file = os.path.join(output_dir, 'translation_progress.json')
with open(progress_file, 'w', encoding='utf-8') as f:
json.dump(progress_data, f, indent=2)
self._log(f"β
Saved {len(sorted_results)} chapters to: {output_dir}")
messagebox.showinfo(
"Async Translation Complete",
f"Successfully saved {len(sorted_results)} translated chapters to:\n{output_dir}\n\n"
"Ready for EPUB conversion or further processing."
)
except Exception as e:
self._log(f"β Error handling completed job: {e}")
import traceback
self._log(traceback.format_exc())
messagebox.showerror("Error", f"Failed to process results: {str(e)}")
def _show_error_details(self, job):
"""Show details from error file"""
if not job.metadata.get('error_file_id'):
return
try:
api_key = self.gui.api_key_entry.get().strip()
headers = {'Authorization': f'Bearer {api_key}'}
# Download error file
response = requests.get(
f'https://api.openai.com/v1/files/{job.metadata["error_file_id"]}/content',
headers=headers
)
if response.status_code == 200:
# Parse first few errors
errors = []
for i, line in enumerate(response.text.strip().split('\n')[:5]): # Show first 5 errors
if line:
try:
error_data = json.loads(line)
error_msg = error_data.get('error', {}).get('message', 'Unknown error')
errors.append(f"β’ {error_msg}")
except:
pass
error_text = '\n'.join(errors)
if len(response.text.strip().split('\n')) > 5:
newline = '\n'
error_text += f"\n\n... and {len(response.text.strip().split(newline)) - 5} more errors"
messagebox.showerror(
"Batch Processing Errors",
f"All requests failed with errors:\n\n{error_text}\n\n"
"Common causes:\n"
"β’ Invalid API key or insufficient permissions\n"
"β’ Model not available in your region\n"
"β’ Malformed request format"
)
except Exception as e:
print(f"Failed to retrieve error details: {e}")
def _extract_chapter_number(self, custom_id):
"""Extract chapter number from custom ID"""
match = re.search(r'chapter[_-](\d+)', custom_id, re.IGNORECASE)
if match:
return int(match.group(1))
return 0
# Helper methods for thread-safe UI updates
def _log(self, message, level="info"):
"""Thread-safe logging to GUI"""
# Log based on level
if level == "error":
print(f"β {message}") # This will show in GUI
elif level == "warning":
print(f"β οΈ {message}") # This will show in GUI
else:
logger.info(message) # This only goes to log file
# Also display info messages in GUI
if hasattr(self.gui, 'append_log'):
self.dialog.after(0, lambda: self.gui.append_log(message))
def _show_error(self, message):
"""Thread-safe error dialog"""
self._log(f"Error: {message}", level="error")
self.dialog.after(0, lambda: messagebox.showerror("Error", message))
def _show_info(self, title, message):
"""Thread-safe info dialog"""
self._log(f"{title}: {message}", level="info")
self.dialog.after(0, lambda: messagebox.showinfo(title, message))
def _show_warning(self, message):
"""Thread-safe warning display"""
self._log(f"Warning: {message}", level="warning")
def show_async_processing_dialog(parent, translator_gui):
"""Show the async processing dialog
Args:
parent: Parent window
translator_gui: Reference to main TranslatorGUI instance
"""
dialog = AsyncProcessingDialog(parent, translator_gui)
return dialog.dialog
# Integration function for translator_gui.py
def add_async_processing_button(translator_gui, parent_frame):
"""Add async processing button to GUI
This function should be called from translator_gui.py to add the button
Args:
translator_gui: TranslatorGUI instance
parent_frame: Frame to add button to
"""
# Create button with appropriate styling
async_button = ttk.Button(
parent_frame,
text="Async Processing (50% Off)",
command=lambda: show_async_processing_dialog(translator_gui.master, translator_gui),
style="primary.TButton"
)
# Place button appropriately
async_button.pack(side=tk.LEFT, padx=5)
# Store reference
translator_gui.async_button = async_button
return async_button
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