Upload app_multi_api1.py

app_multi_api1.py

@@ -0,0 +1,727 @@
+import requests
+import json
+import gradio as gr
+from typing import Dict, Any, List, Generator, Optional
+import os
+from dataclasses import dataclass
+import re
+import pandas as pd
+import time
+from datetime import datetime
+import threading
+from queue import Queue
+import io
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+from enum import Enum
+
+class APIProvider(Enum):
+    """پرووایدرهای API پشتیبانی شده"""
+    CEREBRAS = "cerebras"
+    GROQ = "groq"
+    OPENAI = "openai"
+
+@dataclass
+class APIConfig:
+    """تنظیمات API"""
+    provider: APIProvider
+    api_key: str
+    base_url: str
+    model: str
+    max_tokens: int = 2000
+    temperature: float = 0.1
+
+# تنظیمات پیش‌فرض برای هر پرووایدر
+API_CONFIGS = {
+    APIProvider.CEREBRAS: {
+        "base_url": "https://api.cerebras.ai/v1",
+        "models": ["qwen-3-32b", "llama-4-scout-17b-16e-instruct", "llama3.1-8b"],
+        "default_model": "qwen-3-32b"
+    },
+    APIProvider.GROQ: {
+        "base_url": "https://api.groq.com/openai/v1",
+        "models": ["llama-3.3-70b-versatile", "llama-3.1-8b-instant", "mixtral-8x7b-32768"],
+        "default_model": "llama-3.3-70b-versatile"
+    },
+    APIProvider.OPENAI: {
+        "base_url": "https://api.openai.com/v1",
+        "models": ["gpt-4o-mini", "gpt-4o", "gpt-3.5-turbo"],
+        "default_model": "gpt-4o-mini"
+    }
+}
+
+@dataclass
+class RateLimitConfig:
+    """تنظیمات محدودیت نرخ درخواست"""
+    requests_per_minute: int = 30
+    min_delay_between_requests: float = 2.0
+    max_retries: int = 5
+    initial_backoff: float = 5.0
+    max_backoff: float = 60.0
+    backoff_multiplier: float = 2.0
+    recovery_window: float = 60.0
+
+class RateLimiter:
+    """مدیریت محدودیت نرخ درخواست"""
+    
+    def __init__(self, config: RateLimitConfig):
+        self.config = config
+        self.request_times: List[float] = []
+        self.lock = threading.Lock()
+        self.consecutive_failures = 0
+        self.last_failure_time = 0
+        self.last_success_time = time.time()
+        self.total_429_errors = 0
+    
+    def wait_if_needed(self) -> float:
+        """انتظار تا زمان مجاز ارسال درخواست بعدی"""
+        with self.lock:
+            now = time.time()
+            
+            # پاک کردن درخواست‌های قدیمی
+            self.request_times = [t for t in self.request_times if now - t < 60]
+            
+            # بازیابی از خطاهای قبلی
+            if self.consecutive_failures > 0 and (now - self.last_failure_time) > self.config.recovery_window:
+                self.consecutive_failures = max(0, self.consecutive_failures - 1)
+            
+            wait_time = 0.0
+            
+            # محدودیت درخواست در دقیقه
+            if len(self.request_times) >= self.config.requests_per_minute:
+                oldest_request = min(self.request_times)
+                wait_time = max(wait_time, 60 - (now - oldest_request) + 1)
+            
+            # حداقل تأخیر
+            if self.request_times:
+                time_since_last = now - max(self.request_times)
+                if time_since_last < self.config.min_delay_between_requests:
+                    wait_time = max(wait_time, self.config.min_delay_between_requests - time_since_last)
+            
+            # backoff برای خطاها
+            if self.consecutive_failures > 0:
+                failure_wait = min(
+                    self.config.initial_backoff * (self.config.backoff_multiplier ** min(self.consecutive_failures, 4)),
+                    self.config.max_backoff
+                )
+                wait_time = max(wait_time, failure_wait)
+            
+            if wait_time > 0:
+                time.sleep(wait_time)
+            
+            self.request_times.append(time.time())
+            return wait_time
+    
+    def report_success(self):
+        with self.lock:
+            self.last_success_time = time.time()
+            self.consecutive_failures = 0
+    
+    def report_failure(self, is_rate_limit: bool = False):
+        with self.lock:
+            self.last_failure_time = time.time()
+            if is_rate_limit:
+                self.consecutive_failures += 1
+                self.total_429_errors += 1
+            else:
+                self.consecutive_failures = min(self.consecutive_failures + 0.5, 3)
+    
+    def get_estimated_wait_time(self) -> float:
+        with self.lock:
+            now = time.time()
+            self.request_times = [t for t in self.request_times if now - t < 60]
+            if len(self.request_times) >= self.config.requests_per_minute:
+                return max(0, 60 - (now - min(self.request_times)) + 1)
+            return self.config.min_delay_between_requests
+    
+    def get_status(self) -> Dict[str, Any]:
+        with self.lock:
+            return {
+                "consecutive_failures": self.consecutive_failures,
+                "total_429_errors": self.total_429_errors,
+                "requests_in_last_minute": len(self.request_times)
+            }
+
+
+class MultiProviderAnonymizer:
+    """سیستم ناشناس‌سازی با پشتیبانی از چند API"""
+    
+    def __init__(
+        self, 
+        provider: APIProvider,
+        api_key: str,
+        model: str = None,
+        rate_limit_config: RateLimitConfig = None
+    ):
+        self.provider = provider
+        self.api_key = api_key
+        
+        provider_config = API_CONFIGS[provider]
+        self.base_url = provider_config["base_url"]
+        self.model = model or provider_config["default_model"]
+        
+        self.rate_limit_config = rate_limit_config or RateLimitConfig()
+        self.rate_limiter = RateLimiter(self.rate_limit_config)
+        self.system_prompt = self._create_system_prompt()
+        self.session = self._create_session()
+    
+    def _create_session(self) -> requests.Session:
+        """ایجاد session با تنظیمات بهینه"""
+        session = requests.Session()
+        retry_strategy = Retry(
+            total=3,
+            backoff_factor=1,
+            status_forcelist=[500, 502, 503, 504],
+            allowed_methods=["POST"]
+        )
+        adapter = HTTPAdapter(max_retries=retry_strategy, pool_connections=10, pool_maxsize=10)
+        session.mount("https://", adapter)
+        session.mount("http://", adapter)
+        return session
+    
+    def _create_system_prompt(self) -> str:
+        """دستورالعمل سیستمی"""
+        return """شما یک «ناشناس‌ساز متون مالی/خبری فارسی» هستید. وظیفه‌تان جایگزینی اسامی خاص و مقادیر عددی با شناسه‌های بی‌معناست.
+
+## **قوانین اندیس‌گذاری**
+- شرکت‌ها: company-01, company-02, ... (پیوسته)
+- اشخاص: person-01, person-02, ... (پیوسته)
+- اعداد: amount-01, amount-02, ... (پیوسته)
+- درصدها: percent-01, percent-02, ... (پیوسته)
+
+## **ثبات شناسه‌ها:**
+- اگر "همراه اول" اول‌بار company-01 شد، در تمام متن همان باشد
+
+## **تشخیص صحیح:**
+- **شرکت/سازمان:** همراه اول، بانک ملی، ایران‌خودرو، سایپا، بانک مرکزی
+- **گروه‌ها:** "گروه همراه اول" → company-XX (نه group-XX)
+- **کلمات عمومی:** "سه شرکت"، "چند بانک" → حفظ شوند
+- **شخص:** مهدی اخوان بهابادی، محمدرضا فرزین
+- **عدد:** 37، 70، 677 (هر عددی)
+- **درصد:** 37 درصدی، 15%
+
+## **مثال:**
+**ورودی:** مهدی اخوان، مدیرعامل همراه اول، اعلام کرد درآمد با رشد 37 درصدی به 70 میلیارد رسید.
+**خروجی:** person-01، مدیرعامل company-01، اعلام کرد درآمد با رشد percent-01 به amount-01 رسید.
+
+## **حفظ شود:**
+- تاریخ‌ها: 1404/04/23
+- دوره‌های زمانی: ۹ ماهه، ۵ ماهه سال
+- واحدها: میلیارد تومان، همت
+- مکان‌ها: تهران، اصفهان
+
+**فقط متن ناشناس‌شده را برگردان - بدون توضیح اضافی.**
+"""
+
+    def _make_api_request(self, text: str) -> Dict[str, Any]:
+        """ارسال درخواست به API"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "messages": [
+                {"role": "system", "content": self.system_prompt},
+                {"role": "user", "content": text}
+            ],
+            "model": self.model,
+            "temperature": 0.1,
+            "max_tokens": 2000
+        }
+        
+        # اضافه کردن پارامترهای خاص هر پرووایدر
+        if self.provider == APIProvider.GROQ:
+            # Groq نیاز به این پارامتر نداره
+            pass
+        
+        last_error = None
+        
+        for attempt in range(self.rate_limit_config.max_retries):
+            self.rate_limiter.wait_if_needed()
+            
+            try:
+                response = self.session.post(
+                    f"{self.base_url}/chat/completions",
+                    headers=headers,
+                    json=payload,
+                    timeout=90
+                )
+                
+                if response.status_code == 429:
+                    self.rate_limiter.report_failure(is_rate_limit=True)
+                    retry_after = response.headers.get('Retry-After')
+                    wait_seconds = int(retry_after) if retry_after else min(
+                        self.rate_limit_config.initial_backoff * (self.rate_limit_config.backoff_multiplier ** attempt),
+                        60.0
+                    )
+                    last_error = f"Rate limit (429). تلاش {attempt + 1}/{self.rate_limit_config.max_retries}"
+                    print(f"🚫 {last_error} - انتظار {wait_seconds}s")
+                    time.sleep(wait_seconds)
+                    continue
+                
+                if response.status_code == 401:
+                    raise Exception(f"❌ کلید API نامعتبر است برای {self.provider.value}")
+                
+                if response.status_code == 503:
+                    self.rate_limiter.report_failure(is_rate_limit=False)
+                    last_error = f"سرویس در دسترس نیست (503)"
+                    time.sleep(10)
+                    continue
+                
+                response.raise_for_status()
+                self.rate_limiter.report_success()
+                return response.json()
+                
+            except requests.exceptions.Timeout:
+                self.rate_limiter.report_failure(is_rate_limit=False)
+                last_error = f"Timeout"
+                time.sleep(5)
+                
+            except requests.exceptions.ConnectionError as e:
+                self.rate_limiter.report_failure(is_rate_limit=False)
+                last_error = f"خطای اتصال: {str(e)[:50]}"
+                time.sleep(10)
+                
+            except requests.exceptions.RequestException as e:
+                self.rate_limiter.report_failure(is_rate_limit=False)
+                last_error = f"خطای شبکه: {str(e)[:50]}"
+                time.sleep(5)
+        
+        raise Exception(f"ناموفق پس از {self.rate_limit_config.max_retries} تلاش: {last_error}")
+    
+    def anonymize_text(self, text: str) -> Dict[str, Any]:
+        """ناشناس‌سازی متن"""
+        if not text or not text.strip():
+            return {"success": False, "error": "متن ورودی خالی است", "anonymized_text": ""}
+        
+        try:
+            response = self._make_api_request(text)
+            
+            if "choices" not in response or not response["choices"]:
+                return {"success": False, "error": "پاسخ نامعتبر", "anonymized_text": ""}
+            
+            content = response["choices"][0]["message"]["content"]
+            
+            # پاک کردن markdown
+            if "```" in content:
+                lines = content.split('\n')
+                clean_lines = []
+                skip = False
+                for line in lines:
+                    if line.strip().startswith('```'):
+                        skip = not skip
+                        continue
+                    if not skip:
+                        clean_lines.append(line)
+                content = '\n'.join(clean_lines)
+            
+            content = content.strip()
+            
+            # پاک کردن تگ‌های thinking (برای مدل‌های Qwen و مشابه)
+            content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL)
+            content = re.sub(r'<thinking>.*?</thinking>', '', content, flags=re.DOTALL)
+            content = content.strip()
+            
+            # تحلیل نتایج
+            companies = re.findall(r'company-(\d+)', content)
+            persons = re.findall(r'person-(\d+)', content)
+            amounts = re.findall(r'amount-(\d+)', content)
+            percents = re.findall(r'percent-(\d+)', content)
+            
+            return {
+                "success": True,
+                "anonymized_text": content,
+                "statistics": {
+                    "company": len(set(companies)),
+                    "person": len(set(persons)),
+                    "amount": len(set(amounts)),
+                    "percent": len(set(percents))
+                },
+                "usage": response.get("usage", {})
+            }
+                
+        except Exception as e:
+            return {"success": False, "error": f"خطا: {str(e)}", "anonymized_text": ""}
+
+
+class BatchProcessor:
+    """پردازشگر دسته‌ای"""
+    
+    def __init__(
+        self, 
+        provider: APIProvider,
+        api_key: str,
+        model: str = None,
+        rate_limit_config: RateLimitConfig = None
+    ):
+        self.provider = provider
+        self.api_key = api_key
+        self.model = model
+        self.rate_limit_config = rate_limit_config or RateLimitConfig()
+        self.anonymizer = None
+        self.is_cancelled = False
+        self.current_progress = 0
+        self.total_rows = 0
+        self.processed_rows = 0
+        self.failed_rows = 0
+        self.start_time = None
+        self.consecutive_api_failures = 0
+        self.max_consecutive_failures = 10
+    
+    def cancel(self):
+        self.is_cancelled = True
+    
+    def reset(self):
+        self.is_cancelled = False
+        self.current_progress = 0
+        self.total_rows = 0
+        self.processed_rows = 0
+        self.failed_rows = 0
+        self.start_time = None
+        self.consecutive_api_failures = 0
+    
+    def process_csv(
+        self, 
+        file_path: str, 
+        text_column: str,
+        output_column: str = "anonymized_text"
+    ) -> Generator[Dict[str, Any], None, None]:
+        """پردازش فایل CSV"""
+        
+        self.reset()
+        self.start_time = time.time()
+        
+        # خواندن فایل
+        try:
+            df = pd.read_csv(file_path, encoding='utf-8')
+        except UnicodeDecodeError:
+            try:
+                df = pd.read_csv(file_path, encoding='utf-8-sig')
+            except:
+                df = pd.read_csv(file_path, encoding='cp1256')
+        
+        if text_column not in df.columns:
+            yield {"type": "error", "message": f"ستون '{text_column}' یافت نشد"}
+            return
+        
+        self.total_rows = len(df)
+        
+        # ایجاد anonymizer
+        self.anonymizer = MultiProviderAnonymizer(
+            provider=self.provider,
+            api_key=self.api_key,
+            model=self.model,
+            rate_limit_config=self.rate_limit_config
+        )
+        
+        df[output_column] = ""
+        df["anonymization_status"] = ""
+        df["entities_found"] = ""
+        
+        yield {
+            "type": "info",
+            "message": f"🚀 شروع پردازش {self.total_rows} ردیف با {self.provider.value}..."
+        }
+        
+        for idx, row in df.iterrows():
+            if self.is_cancelled:
+                yield {
+                    "type": "cancelled",
+                    "message": "لغو شد",
+                    "processed": self.processed_rows,
+                    "failed": self.failed_rows
+                }
+                break
+            
+            if self.consecutive_api_failures >= self.max_consecutive_failures:
+                partial_path = file_path.replace('.csv', f'_partial_{idx}.csv')
+                df.to_csv(partial_path, index=False, encoding='utf-8-sig')
+                yield {
+                    "type": "error",
+                    "message": f"❌ توقف - خطاهای متوالی زیاد. ذخیره شد: {partial_path}"
+                }
+                return
+            
+            text = str(row[text_column]) if pd.notna(row[text_column]) else ""
+            
+            if not text.strip():
+                df.at[idx, output_column] = ""
+                df.at[idx, "anonymization_status"] = "خالی"
+                self.processed_rows += 1
+                continue
+            
+            result = self.anonymizer.anonymize_text(text)
+            
+            if result["success"]:
+                df.at[idx, output_column] = result["anonymized_text"]
+                df.at[idx, "anonymization_status"] = "موفق"
+                stats = result.get("statistics", {})
+                df.at[idx, "entities_found"] = f"C:{stats.get('company',0)}|P:{stats.get('person',0)}|A:{stats.get('amount',0)}|%:{stats.get('percent',0)}"
+                self.processed_rows += 1
+                self.consecutive_api_failures = 0
+            else:
+                df.at[idx, output_column] = f"خطا: {result.get('error', '')}"
+                df.at[idx, "anonymization_status"] = "ناموفق"
+                self.failed_rows += 1
+                if "rate limit" in result.get('error', '').lower() or "429" in result.get('error', ''):
+                    self.consecutive_api_failures += 1
+            
+            # پیشرفت
+            self.current_progress = (idx + 1) / self.total_rows * 100
+            elapsed = time.time() - self.start_time
+            
+            yield {
+                "type": "progress",
+                "current": idx + 1,
+                "total": self.total_rows,
+                "progress": self.current_progress,
+                "processed": self.processed_rows,
+                "failed": self.failed_rows,
+                "elapsed": elapsed,
+                "estimated_remaining": (elapsed / (idx + 1)) * (self.total_rows - idx - 1),
+                "next_wait": self.anonymizer.rate_limiter.get_estimated_wait_time(),
+                "last_result": result,
+                "rate_status": self.anonymizer.rate_limiter.get_status()
+            }
+            
+            # Checkpoint هر 100 ردیف
+            if (idx + 1) % 100 == 0:
+                checkpoint_path = file_path.replace('.csv', f'_checkpoint_{idx+1}.csv')
+                df.to_csv(checkpoint_path, index=False, encoding='utf-8-sig')
+                yield {"type": "info", "message": f"💾 Checkpoint: {checkpoint_path}"}
+        
+        # ذخیره نهایی
+        if not self.is_cancelled:
+            output_path = file_path.replace('.csv', '_anonymized.csv')
+            if output_path == file_path:
+                output_path = file_path + '_anonymized.csv'
+            
+            df.to_csv(output_path, index=False, encoding='utf-8-sig')
+            
+            yield {
+                "type": "complete",
+                "message": "✅ تکمیل شد!",
+                "output_path": output_path,
+                "total": self.total_rows,
+                "processed": self.processed_rows,
+                "failed": self.failed_rows,
+                "total_time": time.time() - self.start_time
+            }
+
+
+def create_interface():
+    """ایجاد رابط کاربری"""
+    
+    custom_css = """
+    .rtl-text { direction: rtl; text-align: right; font-family: 'Vazirmatn', 'Tahoma', sans-serif; }
+    """
+    
+    with gr.Blocks(css=custom_css, title="ناشناس‌ساز چند-API", theme=gr.themes.Soft()) as interface:
+        gr.Markdown("""
+        # 🔒 سیستم ناشناس‌سازی متون فارسی
+        ### پشتیبانی از Cerebras، Groq و OpenAI
+        """, elem_classes=["rtl-text"])
+        
+        batch_processor = {"instance": None}
+        
+        with gr.Tabs():
+            # تب پردازش تکی
+            with gr.Tab("پردازش تکی"):
+                with gr.Row():
+                    with gr.Column():
+                        single_provider = gr.Dropdown(
+                            label="🌐 پرووایدر API",
+                            choices=["cerebras", "groq", "openai"],
+                            value="groq",
+                            elem_classes=["rtl-text"]
+                        )
+                        single_api_key = gr.Textbox(
+                            label="🔑 کلید API",
+                            placeholder="کلید API خود را وارد کنید",
+                            type="password"
+                        )
+                        single_model = gr.Dropdown(
+                            label="🤖 مدل",
+                            choices=["llama-3.3-70b-versatile", "llama-3.1-8b-instant"],
+                            value="llama-3.3-70b-versatile"
+                        )
+                        single_input = gr.Textbox(
+                            label="متن ورودی",
+                            lines=5,
+                            elem_classes=["rtl-text"]
+                        )
+                        single_btn = gr.Button("🔄 ناشناس‌سازی", variant="primary")
+                    
+                    with gr.Column():
+                        single_output = gr.Textbox(label="متن ناشناس‌شده", lines=5, elem_classes=["rtl-text"])
+                        single_stats = gr.Textbox(label="آمار", lines=2)
+            
+            # تب پردازش دسته‌ای
+            with gr.Tab("پردازش دسته‌ای"):
+                with gr.Row():
+                    with gr.Column():
+                        batch_provider = gr.Dropdown(
+                            label="🌐 پرووایدر API",
+                            choices=["cerebras", "groq", "openai"],
+                            value="groq"
+                        )
+                        batch_api_key = gr.Textbox(
+                            label="🔑 کلید API",
+                            type="password"
+                        )
+                        batch_model = gr.Dropdown(
+                            label="🤖 مدل",
+                            choices=["llama-3.3-70b-versatile", "qwen-3-32b", "gpt-4o-mini"],
+                            value="llama-3.3-70b-versatile"
+                        )
+                        csv_file = gr.File(label="📁 فایل CSV", file_types=[".csv"])
+                        text_column = gr.Dropdown(label="ستون متن", choices=[], allow_custom_value=True)
+                        output_column = gr.Textbox(label="نام ستون خروجی", value="anonymized_text")
+                    
+                    with gr.Column():
+                        delay_slider = gr.Slider(1, 30, 3, step=0.5, label="تأخیر (ثانیه)")
+                        rpm_slider = gr.Slider(5, 30, 20, step=1, label="حداکثر درخواست/دقیقه")
+                        retries_slider = gr.Slider(1, 10, 5, step=1, label="تلاش مجدد")
+                
+                with gr.Row():
+                    start_btn = gr.Button("🚀 شروع", variant="primary", size="lg")
+                    cancel_btn = gr.Button("⏹️ لغو", variant="stop", size="lg")
+                
+                progress_bar = gr.Slider(0, 100, 0, label="پیشرفت", interactive=False)
+                progress_text = gr.Markdown("در انتظار...")
+                time_stats = gr.Markdown("")
+                process_log = gr.Textbox(lines=10, label="لاگ")
+                preview_table = gr.Dataframe(headers=["متن", "ناشناس‌شده", "وضعیت"])
+                output_file = gr.File(label="دانلود", visible=False)
+        
+        # توابع
+        def update_models(provider):
+            if provider == "cerebras":
+                return gr.update(choices=["qwen-3-32b", "llama-4-scout-17b-16e-instruct"], value="qwen-3-32b")
+            elif provider == "groq":
+                return gr.update(choices=["llama-3.3-70b-versatile", "llama-3.1-8b-instant", "mixtral-8x7b-32768"], value="llama-3.3-70b-versatile")
+            else:
+                return gr.update(choices=["gpt-4o-mini", "gpt-4o"], value="gpt-4o-mini")
+        
+        def update_columns(file):
+            if file is None:
+                return gr.update(choices=[], value=None)
+            try:
+                df = pd.read_csv(file.name, nrows=0, encoding='utf-8')
+            except:
+                try:
+                    df = pd.read_csv(file.name, nrows=0, encoding='utf-8-sig')
+                except:
+                    df = pd.read_csv(file.name, nrows=0, encoding='cp1256')
+            columns = list(df.columns)
+            return gr.update(choices=columns, value=columns[0] if columns else None)
+        
+        def process_single(provider, api_key, model, text):
+            if not text.strip():
+                return "", "❌ متن خالی"
+            if not api_key:
+                return "", "❌ کلید API وارد نشده"
+            
+            try:
+                prov = APIProvider(provider)
+                anonymizer = MultiProviderAnonymizer(provider=prov, api_key=api_key, model=model)
+                result = anonymizer.anonymize_text(text)
+                
+                if result["success"]:
+                    stats = result.get("statistics", {})
+                    return result["anonymized_text"], f"✅ C:{stats.get('company',0)} | P:{stats.get('person',0)} | A:{stats.get('amount',0)} | %:{stats.get('percent',0)}"
+                else:
+                    return "", f"❌ {result.get('error', '')}"
+            except Exception as e:
+                return "", f"❌ {str(e)}"
+        
+        def start_batch(file, text_col, output_col, delay, rpm, retries, provider, api_key, model):
+            if file is None:
+                yield (0, "❌ فایل انتخاب نشده", "", "", None, gr.update(visible=False))
+                return
+            if not api_key:
+                yield (0, "❌ کلید API وارد نشده", "", "", None, gr.update(visible=False))
+                return
+            
+            prov = APIProvider(provider)
+            rate_config = RateLimitConfig(
+                requests_per_minute=int(rpm),
+                min_delay_between_requests=float(delay),
+                max_retries=int(retries)
+            )
+            
+            processor = BatchProcessor(provider=prov, api_key=api_key, model=model, rate_limit_config=rate_config)
+            batch_processor["instance"] = processor
+            
+            log_lines = []
+            preview_data = []
+            
+            for update in processor.process_csv(file.name, text_col, output_col):
+                update_type = update.get("type")
+                
+                if update_type == "error":
+                    log_lines.append(f"❌ {update['message']}")
+                    yield (0, f"❌ {update['message']}", "", "\n".join(log_lines), None, gr.update(visible=False))
+                    return
+                
+                elif update_type == "info":
+                    log_lines.append(f"ℹ️ {update['message']}")
+                
+                elif update_type == "progress":
+                    progress = update["progress"]
+                    current = update["current"]
+                    total = update["total"]
+                    elapsed = update["elapsed"]
+                    remaining = update["estimated_remaining"]
+                    
+                    progress_md = f"**{current}/{total}** ({progress:.1f}%) | ✅ {update['processed']} | ❌ {update['failed']}"
+                    time_md = f"⏱️ {elapsed/60:.1f}m | باقیمانده: {remaining/60:.1f}m"
+                    
+                    if current % 10 == 0:
+                        log_lines.append(f"📊 {current}/{total}")
+                    
+                    last_result = update.get("last_result", {})
+                    if last_result.get("success"):
+                        preview_data.append(["...", last_result.get("anonymized_text", "")[:80] + "...", "✅"])
+                        if len(preview_data) > 5:
+                            preview_data = preview_data[-5:]
+                    
+                    yield (progress, progress_md, time_md, "\n".join(log_lines[-15:]), preview_data if preview_data else None, gr.update(visible=False))
+                
+                elif update_type == "cancelled":
+                    log_lines.append("⏹️ لغو شد")
+                    yield (0, "⏹️ لغو شد", "", "\n".join(log_lines), preview_data if preview_data else None, gr.update(visible=False))
+                    return
+                
+                elif update_type == "complete":
+                    log_lines.append(f"✅ {update['message']}")
+                    progress_md = f"✅ تکمیل! {update['processed']}/{update['total']} موفق | {update['total_time']/60:.1f} دقیقه"
+                    yield (100, progress_md, "", "\n".join(log_lines), preview_data if preview_data else None, gr.update(value=update['output_path'], visible=True))
+        
+        def cancel():
+            if batch_processor["instance"]:
+                batch_processor["instance"].cancel()
+            return "⏹️ لغو شد..."
+        
+        # اتصالات
+        single_provider.change(update_models, [single_provider], [single_model])
+        batch_provider.change(update_models, [batch_provider], [batch_model])
+        csv_file.change(update_columns, [csv_file], [text_column])
+        
+        single_btn.click(process_single, [single_provider, single_api_key, single_model, single_input], [single_output, single_stats])
+        
+        start_btn.click(
+            start_batch,
+            [csv_file, text_column, output_column, delay_slider, rpm_slider, retries_slider, batch_provider, batch_api_key, batch_model],
+            [progress_bar, progress_text, time_stats, process_log, preview_table, output_file]
+        )
+        
+        cancel_btn.click(cancel, outputs=[process_log])
+    
+    return interface
+
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch(server_name="0.0.0.0", server_port=7860, share=True, show_error=True)