Delete app.py

app.py

@@ -1,355 +0,0 @@
-import gradio as gr
-import pandas as pd
-import re
-from collections import defaultdict
-import io
-
-class TextAnonymizer:
-    def __init__(self):
-        self.person_counter = 0
-        self.company_counter = 0
-        self.amount_counter = 0
-        self.percent_counter = 0
-        
-        # دیکشنری برای نگه‌داری تبدیل‌ها
-        self.person_mapping = {}
-        self.company_mapping = {}
-        self.amount_mapping = {}
-        self.percent_mapping = {}
-    
-    def reset_counters(self):
-        """بازنشانی شمارنده‌ها برای پردازش جدید"""
-        self.person_counter = 0
-        self.company_counter = 0
-        self.amount_counter = 0
-        self.percent_counter = 0
-        self.person_mapping.clear()
-        self.company_mapping.clear()
-        self.amount_mapping.clear()
-        self.percent_mapping.clear()
-    
-    def detect_financial_amounts(self, text):
-        """تشخیص مبالغ مالی"""
-        patterns = [
-            r'\$[\d,]+(?:\.\d{2})?',  # $1,000.00
-            r'[\d,]+\s*(?:dollars?|USD|usd|Dollars?)',  # 1000 dollars
-            r'[\d,]+\s*(?:million|billion|thousand|Million|Billion|Thousand)',  # 1 million
-            r'[\d,]+(?:\.\d+)?\s*(?:M|B|K|m|b|k)',  # 1.5M, 2B, 500K
-            r'€[\d,]+(?:\.\d{2})?',  # €1,000.00
-            r'£[\d,]+(?:\.\d{2})?',  # £1,000.00
-        ]
-        
-        amounts = []
-        for pattern in patterns:
-            matches = re.finditer(pattern, text, re.IGNORECASE)
-            for match in matches:
-                amounts.append((match.start(), match.end(), match.group()))
-        
-        return amounts
-    
-    def detect_percentages(self, text):
-        """تشخیص درصدها"""
-        pattern = r'\d+(?:\.\d+)?%'
-        percentages = []
-        matches = re.finditer(pattern, text)
-        for match in matches:
-            percentages.append((match.start(), match.end(), match.group()))
-        
-        return percentages
-    
-    def detect_names_regex(self, text):
-        """تشخیص اسامی با regex (بدون spaCy)"""
-        patterns = [
-            r'\b[A-Z][a-z]+ [A-Z][a-z]+\b',  # John Smith
-            r'\b[A-Z][a-z]+ [A-Z]\. [A-Z][a-z]+\b',  # John M. Smith
-            r'\b[A-Z][a-z]+ [A-Z][a-z]+ [A-Z][a-z]+\b',  # John Michael Smith
-            r'\bMr\. [A-Z][a-z]+\b',  # Mr. Smith
-            r'\bMs\. [A-Z][a-z]+\b',  # Ms. Johnson
-            r'\bDr\. [A-Z][a-z]+\b',  # Dr. Brown
-        ]
-        
-        names = []
-        for pattern in patterns:
-            matches = re.finditer(pattern, text)
-            for match in matches:
-                names.append((match.start(), match.end(), match.group()))
-        
-        return names
-    
-    def detect_companies_regex(self, text):
-        """تشخیص شرکت‌ها با regex"""
-        patterns = [
-            r'\b[A-Z][a-z]+ (?:Inc|Corp|LLC|Ltd|Company|Co|Corporation|Group|Technologies|Tech|Systems|Solutions|Services|International|Global|Enterprises)\.?\b',
-            r'\b(?:Apple|Microsoft|Google|Amazon|Facebook|Meta|Netflix|Tesla|Oracle|IBM|Intel|Cisco|Adobe|Salesforce|PayPal|Uber|Airbnb|Twitter|LinkedIn|Samsung|Sony|LG|HP|Dell|Lenovo|Huawei|Xiaomi|OnePlus|NVIDIA|AMD|Qualcomm|Broadcom|Texas Instruments|Micron|SK Hynix|TSMC|ASML|Shopify|Square|Stripe|Zoom|Slack|Dropbox|Spotify|Pinterest|Snapchat|TikTok|ByteDance|Alibaba|Tencent|Baidu|JD|Meituan|Didi|WeChat|Ant Group|Ping An|ICBC|China Mobile|China Telecom|China Unicom|SoftBank|NTT|KDDI|Rakuten|Nintendo|Panasonic|Canon|Nikon|Olympus|Fujifilm|Sharp|Toshiba|Hitachi|Mitsubishi|Suzuki|Nissan|Toyota|Honda|Mazda|Subaru|Yamaha|Kawasaki|Bridgestone|Michelin|Goodyear|Continental|Bosch|Siemens|SAP|Volkswagen|BMW|Mercedes|Audi|Porsche|Ferrari|Lamborghini|Rolls Royce|Bentley|Aston Martin|McLaren|Bugatti|Koenigsegg|Pagani|Maserati|Alfa Romeo|Fiat|Peugeot|Renault|Citroen|Opel|Volvo|Saab|IKEA|H&M|Zara|Uniqlo|Nike|Adidas|Puma|Under Armour|Lululemon|Patagonia|North Face|Columbia|REI|Dick's|Foot Locker|Finish Line|JD Sports|Decathlon|Walmart|Target|Costco|Home Depot|Lowe's|Best Buy|GameStop|Barnes & Noble|Starbucks|McDonald's|Burger King|KFC|Pizza Hut|Domino's|Subway|Taco Bell|Chipotle|Panera|Dunkin|Tim Hortons|Costa|Nescafe|Coca Cola|Pepsi|Red Bull|Monster|Gatorade|Powerade|Vitamin Water|Smartwater|Evian|Perrier|San Pellegrino|Fiji|Dasani|Aquafina|Sprite|Fanta|Dr Pepper|Mountain Dew|7UP|Schweppes|Heineken|Budweiser|Corona|Stella Artois|Guinness|Beck's|Carlsberg|Tuborg|Amstel|Kronenbourg|Peroni|Moretti|Nastro Azzurro|Blue Moon|Coors|Miller|Bud Light|Michelob|Samuel Adams|Sierra Nevada|New Belgium|Dogfish Head|Stone|Lagunitas|Ballast Point|Anchor|Founders|Bell's|Great Lakes|Deschutes|Widmer|Rogue|Ninkasi|Hopworks|Bridgeport|Full Sail|Deschutes|Bend|10 Barrel|Crux|Boneyard|Goodlife|Worthy|Sunriver|Pelican|Rogue|Hair of the Dog|Upright|Cascade|Commons|Base Camp|Migration|StormBreaker|Culmination|Great Notion|Modern Times|Other Half|Tree House|Trillium|The Alchemist|Hill Farmstead|Lawson's Finest|The Veil|Monkish|Russian River|Pliny|Blind Pig|Row 2|Hill 2|Knee Deep|Auburn|Device|Moksa|Eight Bridges|Knee Deep|Abnormal|Fieldwork|New Glory|Fort Point|Cellarmaker|Toronado|Magnolia|Beach Chalet|Golden Gate Park|Ocean Beach|Land's End|Cliff House|Sutro Baths|Baker Beach|Presidio|Crissy Field|Marina|Fisherman's Wharf|Pier 39|Alcatraz|Angel Island|Treasure Island|Bay Bridge|Golden Gate Bridge|Twin Peaks|Mission Dolores|Castro|Haight Ashbury|North Beach|Chinatown|Union Square|Financial District|SOMA|Mission Bay|Potrero Hill|Dogpatch|Bernal Heights|Noe Valley|Glen Park|Excelsior|Sunset|Richmond|Avenues|Parkside|West Portal|Forest Hill|St Francis Wood|Sea Cliff|Pacific Heights|Russian Hill|Nob Hill|Telegraph Hill|North Beach|Lombard Street|Coit Tower|Transamerica Pyramid|Salesforce Tower|555 California|Bank of America|Wells Fargo|Chase|Citibank|US Bank|PNC|Capital One|Ally|American Express|Discover|Mastercard|Visa|JPMorgan|Goldman Sachs|Morgan Stanley|Bank of America|Merrill Lynch|Charles Schwab|Fidelity|Vanguard|BlackRock|State Street|T Rowe Price|Franklin Templeton|Invesco|Northern Trust|BNY Mellon|Credit Suisse|UBS|Deutsche Bank|Barclays|HSBC|Standard Chartered|RBS|Lloyds|Santander|BBVA|BNP Paribas|Societe Generale|Credit Agricole|ING|ABN AMRO|Rabobank|Danske Bank|Nordea|SEB|Handelsbanken|Swedbank|DNB|SpareBank|Storebrand)\\b',
-        ]
-        
-        companies = []
-        for pattern in patterns:
-            matches = re.finditer(pattern, text, re.IGNORECASE)
-            for match in matches:
-                companies.append((match.start(), match.end(), match.group()))
-        
-        return companies
-    
-    def anonymize_text(self, text):
-        """ناشناس‌سازی متن با regex"""
-        if not text or pd.isna(text):
-            return text
-        
-        replacements = []
-        
-        # تشخیص اسامی اشخاص
-        names = self.detect_names_regex(text)
-        for start, end, name in names:
-            if name not in self.person_mapping:
-                self.person_counter += 1
-                self.person_mapping[name] = f"person-{self.person_counter:02d}"
-            replacements.append((start, end, self.person_mapping[name]))
-        
-        # تشخیص شرکت‌ها
-        companies = self.detect_companies_regex(text)
-        for start, end, company in companies:
-            if company not in self.company_mapping:
-                self.company_counter += 1
-                self.company_mapping[company] = f"company-{self.company_counter:02d}"
-            replacements.append((start, end, self.company_mapping[company]))
-        
-        # تشخیص مبالغ مالی
-        amounts = self.detect_financial_amounts(text)
-        for start, end, amount in amounts:
-            if amount not in self.amount_mapping:
-                self.amount_counter += 1
-                self.amount_mapping[amount] = f"amount-{self.amount_counter:02d}"
-            replacements.append((start, end, self.amount_mapping[amount]))
-        
-        # تشخیص درصدها
-        percentages = self.detect_percentages(text)
-        for start, end, percent in percentages:
-            if percent not in self.percent_mapping:
-                self.percent_counter += 1
-                self.percent_mapping[percent] = f"percent-{self.percent_counter:02d}"
-            replacements.append((start, end, self.percent_mapping[percent]))
-        
-        # حذف تداخل‌ها و مرتب‌سازی
-        replacements = self.remove_overlaps(replacements)
-        replacements.sort(key=lambda x: x[0], reverse=True)
-        
-        # اعمال جایگزینی‌ها
-        result = text
-        for start, end, replacement in replacements:
-            result = result[:start] + replacement + result[end:]
-        
-        return result
-    
-    def remove_overlaps(self, replacements):
-        """حذف تداخل‌ها در جایگزینی‌ها"""
-        if not replacements:
-            return []
-        
-        # مرتب‌سازی بر اساس موقعیت شروع
-        replacements.sort(key=lambda x: x[0])
-        
-        filtered = []
-        for start, end, replacement in replacements:
-            # بررسی تداخل با آخرین جایگزینی اضافه شده
-            if not filtered or start >= filtered[-1][1]:
-                filtered.append((start, end, replacement))
-        
-        return filtered
-    
-    def get_mapping_summary(self):
-        """خلاصه‌ای از تبدیل‌های انجام شده"""
-        summary = []
-        
-        if self.person_mapping:
-            summary.append("**اسامی اشخاص:**")
-            for original, anonymized in self.person_mapping.items():
-                summary.append(f"- {original} → {anonymized}")
-            summary.append("")
-        
-        if self.company_mapping:
-            summary.append("**نام شرکت‌ها:**")
-            for original, anonymized in self.company_mapping.items():
-                summary.append(f"- {original} → {anonymized}")
-            summary.append("")
-        
-        if self.amount_mapping:
-            summary.append("**مبالغ مالی:**")
-            for original, anonymized in self.amount_mapping.items():
-                summary.append(f"- {original} → {anonymized}")
-            summary.append("")
-        
-        if self.percent_mapping:
-            summary.append("**درصدها:**")
-            for original, anonymized in self.percent_mapping.items():
-                summary.append(f"- {original} → {anonymized}")
-        
-        return "\n".join(summary) if summary else "هیچ موجودیت حساسی یافت نشد."
-
-# ایجاد نمونه از کلاس ناشناس‌ساز
-anonymizer = TextAnonymizer()
-
-def process_csv(file):
-    """پردازش فایل CSV"""
-    try:
-        # بازنشانی شمارنده‌ها
-        anonymizer.reset_counters()
-        
-        # بررسی فایل
-        if file is None:
-            return None, "لطفاً فایل CSV آپلود کنید.", "", None
-        
-        # خواندن فایل CSV
-        if file.name.endswith('.csv'):
-            df = pd.read_csv(file.name)
-        else:
-            return None, "لطفاً فایل CSV آپلود کنید.", "", None
-        
-        # بررسی وجود ستون original_text
-        if 'original_text' not in df.columns:
-            available_columns = ', '.join(df.columns.tolist())
-            return None, f"ستون 'original_text' در فایل یافت نشد. ستون‌های موجود: {available_columns}", "", None
-        
-        # ایجاد کپی از دیتافریم
-        result_df = df.copy()
-        
-        # ناشناس‌سازی متن‌های ستون original_text
-        result_df['anonymized_text'] = df['original_text'].apply(anonymizer.anonymize_text)
-        
-        # تبدیل به CSV برای دانلود
-        output = io.StringIO()
-        result_df.to_csv(output, index=False, encoding='utf-8')
-        csv_content = output.getvalue()
-        
-        # ایجاد فایل CSV برای دانلود
-        output_file = "anonymized_data.csv"
-        with open(output_file, 'w', encoding='utf-8') as f:
-            f.write(csv_content)
-        
-        # نمایش نمونه از نتایج
-        sample_df = result_df[['original_text', 'anonymized_text']].head(10)
-        
-        # خلاصه تبدیل‌ها
-        mapping_summary = anonymizer.get_mapping_summary()
-        
-        return output_file, f"✅ پردازش کامل شد! {len(df)} ردیف پردازش شد.", mapping_summary, sample_df
-        
-    except Exception as e:
-        return None, f"❌ خطا در پردازش فایل: {str(e)}", "", None
-
-def process_single_text(text):
-    """پردازش تک متن"""
-    if not text.strip():
-        return "", "لطفاً متنی وارد کنید."
-    
-    anonymizer.reset_counters()
-    anonymized = anonymizer.anonymize_text(text)
-    mapping_summary = anonymizer.get_mapping_summary()
-    
-    return anonymized, mapping_summary
-
-# ایجاد رابط کاربری Gradio
-with gr.Blocks(title="ناشناس‌سازی متن", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🔒 برنامه ناشناس‌سازی متن (نسخه Regex)
-    
-    ⚡ **وضعیت:** حالت سریع - بدون نیاز به spaCy
-    
-    این برنامه متن‌های شما را ناشناس می‌کند و اطلاعات حساس زیر را جایگزین می‌کند:
-    - 👤 **اسامی اشخاص** → person-01, person-02, ...
-    - 🏢 **نام شرکت‌ها** → company-01, company-02, ...
-    - 💰 **مبالغ مالی** → amount-01, amount-02, ...
-    - 📊 **درصدها** → percent-01, percent-02, ...
-    
-    **نسخه ۱:** آدرس‌ها، مکان‌ها و تاریخ‌ها ناشناس‌سازی نمی‌شوند.
-    """)
-    
-    with gr.Tabs():
-        # تب پردازش فایل CSV
-        with gr.TabItem("📁 پردازش فایل CSV"):
-            gr.Markdown("### آپلود فایل CSV با ستون 'original_text'")
-            
-            with gr.Row():
-                with gr.Column():
-                    file_input = gr.File(
-                        label="فایل CSV را انتخاب کنید",
-                        file_types=[".csv"],
-                        type="filepath"
-                    )
-                    process_btn = gr.Button("🚀 شروع پردازش", variant="primary")
-                
-                with gr.Column():
-                    status_output = gr.Textbox(
-                        label="وضعیت",
-                        interactive=False
-                    )
-                    download_file = gr.File(
-                        label="دانلود فایل ناشناس‌سازی شده",
-                        interactive=False
-                    )
-            
-            with gr.Row():
-                with gr.Column():
-                    mapping_output = gr.Markdown(
-                        label="خلاصه تبدیل‌ها",
-                        value="خلاصه تبدیل‌ها اینجا نمایش داده می‌شود..."
-                    )
-                
-                with gr.Column():
-                    sample_output = gr.Dataframe(
-                        label="نمونه نتایج (۱۰ ردیف اول)",
-                        interactive=False
-                    )
-        
-        # تب تست تک متن
-        with gr.TabItem("📝 تست تک متن"):
-            gr.Markdown("### تست ناشناس‌سازی روی یک متن")
-            
-            with gr.Row():
-                with gr.Column():
-                    text_input = gr.Textbox(
-                        label="متن اصلی",
-                        placeholder="متن خود را اینجا وارد کنید...",
-                        lines=5
-                    )
-                    test_btn = gr.Button("🔍 ناشناس‌سازی", variant="primary")
-                
-                with gr.Column():
-                    text_output = gr.Textbox(
-                        label="متن ناشناس‌سازی شده",
-                        lines=5,
-                        interactive=False
-                    )
-                    text_mapping = gr.Markdown(
-                        label="تبدیل‌های انجام شده"
-                    )
-    
-    # اتصال رویدادها
-    process_btn.click(
-        fn=process_csv,
-        inputs=[file_input],
-        outputs=[download_file, status_output, mapping_output, sample_output]
-    )
-    
-    test_btn.click(
-        fn=process_single_text,
-        inputs=[text_input],
-        outputs=[text_output, text_mapping]
-    )
-    
-    # مثال در بخش تست
-    gr.Examples(
-        examples=[
-            ["John Smith works at Microsoft and earned $50,000 with a 15% bonus."],
-            ["Sarah Johnson from Google Inc. reported revenues of $2.5 million, representing a 25% increase."],
-            ["The CEO of Apple, Tim Cook, announced profits of $1.2B with 18.5% growth rate."],
-            ["Dr. Michael Brown from IBM Corp. received €75,000 salary increase of 12%."],
-            ["Ms. Lisa Wilson at Amazon reported quarterly results of £500K with 8.7% margin."]
-        ],
-        inputs=[text_input],
-        label="نمونه متن‌ها"
-    )
-
-# اجرای برنامه
-if __name__ == "__main__":
-    demo.launch()