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| import gradio as gr | |
| import pandas as pd | |
| from backend.language_detector import LanguageDetector | |
| from typing import List, Dict, Any | |
| import time | |
| # Sample text database organized by difficulty categories | |
| DEMO_SAMPLES = { | |
| "Easy/Obvious": [ | |
| ("Hello, how are you doing today?", "en", "Clear English sentence"), | |
| ("Bonjour, comment allez-vous aujourd'hui?", "fr", "Clear French sentence"), | |
| ("Hola, ¿cómo estás hoy?", "es", "Clear Spanish sentence"), | |
| ("Guten Tag, wie geht es Ihnen heute?", "de", "Clear German sentence"), | |
| ("こんにちは、今日はどうですか？", "ja", "Clear Japanese sentence"), | |
| ("Привет, как дела сегодня?", "ru", "Clear Russian sentence"), | |
| ("Ciao, come stai oggi?", "it", "Clear Italian sentence"), | |
| ("Olá, como você está hoje?", "pt", "Clear Portuguese sentence"), | |
| ("你好，你今天怎么样？", "zh", "Clear Chinese sentence"), | |
| ("안녕하세요, 오늘 어떻게 지내세요?", "ko", "Clear Korean sentence"), | |
| ], | |
| "Short Text": [ | |
| ("Hi", "en", "Very short greeting"), | |
| ("Oui", "fr", "Single word French"), | |
| ("Sí", "es", "Single word Spanish"), | |
| ("Ja", "de", "Single word German"), | |
| ("はい", "ja", "Single word Japanese"), | |
| ("Да", "ru", "Single word Russian"), | |
| ("Sì", "it", "Single word Italian"), | |
| ("Sim", "pt", "Single word Portuguese"), | |
| ("是", "zh", "Single character Chinese"), | |
| ("네", "ko", "Single word Korean"), | |
| ], | |
| "False Friends": [ | |
| ("actual", "en", "English word, but means 'current' in Spanish"), | |
| ("embarazada", "es", "Spanish for pregnant, not embarrassed"), | |
| ("gift", "en", "English word, but means 'poison' in German"), | |
| ("preservativo", "es", "Spanish for condom, not preservative"), | |
| ("sensible", "en", "English word, but means 'sensitive' in Spanish"), | |
| ("sympathique", "fr", "French for nice, not sympathetic"), | |
| ("biblioteca", "es", "Spanish for library, not Bible place"), | |
| ("realizzare", "it", "Italian for to achieve, not realize"), | |
| ("parents", "en", "English word, but means 'relatives' in French"), | |
| ("attualmente", "it", "Italian for currently, not actually"), | |
| ], | |
| "Mixed Scripts": [ | |
| ("Hello123世界", "mix", "Mixed English, numbers, Chinese"), | |
| ("Café #1 في العالم", "mix", "Mixed French, numbers, Arabic"), | |
| ("2023年は良い年です", "ja", "Japanese with numbers"), | |
| ("Prix: €50,000", "fr", "French with currency and numbers"), | |
| ("iPhone 15 Pro Max", "en", "Product name with numbers"), | |
| ("COVID-19 パンデミック", "mix", "Mixed English acronym and Japanese"), | |
| ("Wi-Fi пароль: 123456", "mix", "Mixed English tech term and Russian"), | |
| ("GPS координаты", "mix", "Mixed English acronym and Russian"), | |
| ("URL: https://example.com", "en", "Web address"), | |
| ("HTML <div>content</div>", "en", "Code with markup"), | |
| ], | |
| "Proper Nouns": [ | |
| ("Paris", "ambiguous", "City name - French or English context?"), | |
| ("Berlin", "ambiguous", "City name - German or English context?"), | |
| ("Madrid", "ambiguous", "City name - Spanish or English context?"), | |
| ("Tokyo", "ambiguous", "City name - Japanese or English context?"), | |
| ("Maria", "ambiguous", "Common name in many languages"), | |
| ("Alexander", "ambiguous", "Name used in many languages"), | |
| ("David", "ambiguous", "Biblical name used worldwide"), | |
| ("Anna", "ambiguous", "Name common across languages"), | |
| ("Michael", "ambiguous", "International name"), | |
| ("Sofia", "ambiguous", "Name and city, multiple languages"), | |
| ], | |
| "Common Words": [ | |
| ("hotel", "ambiguous", "Same spelling in many languages"), | |
| ("restaurant", "ambiguous", "French origin, used worldwide"), | |
| ("taxi", "ambiguous", "International word"), | |
| ("pizza", "ambiguous", "Italian origin, used worldwide"), | |
| ("chocolate", "ambiguous", "Similar in many languages"), | |
| ("hospital", "ambiguous", "Medical term used internationally"), | |
| ("radio", "ambiguous", "Technology term used worldwide"), | |
| ("metro", "ambiguous", "Transportation term"), | |
| ("cafe", "ambiguous", "French origin, international use"), | |
| ("photo", "ambiguous", "Greek origin, used worldwide"), | |
| ], | |
| "Technical Terms": [ | |
| ("algorithm", "en", "Technical English term"), | |
| ("algorithme", "fr", "Technical French term"), | |
| ("algoritmo", "es", "Technical Spanish term"), | |
| ("Algorithmus", "de", "Technical German term"), | |
| ("アルゴリズム", "ja", "Technical Japanese term"), | |
| ("алгоритм", "ru", "Technical Russian term"), | |
| ("algoritmo", "it", "Technical Italian term"), | |
| ("algoritmo", "pt", "Technical Portuguese term"), | |
| ("算法", "zh", "Technical Chinese term"), | |
| ("알고리즘", "ko", "Technical Korean term"), | |
| ], | |
| "Code-switching": [ | |
| ("I love sushi とても美味しい", "mix", "English-Japanese code switching"), | |
| ("C'est très nice aujourd'hui", "mix", "French-English code switching"), | |
| ("Me gusta this song mucho", "mix", "Spanish-English code switching"), | |
| ("Das ist very interessant", "mix", "German-English code switching"), | |
| ("Это really хорошо", "mix", "Russian-English code switching"), | |
| ("È molto beautiful oggi", "mix", "Italian-English code switching"), | |
| ("Está muito good today", "mix", "Portuguese-English code switching"), | |
| ("这个 is very 好", "mix", "Chinese-English code switching"), | |
| ("이것은 really 좋다", "mix", "Korean-English code switching"), | |
| ("Merci beaucoup for everything", "mix", "French-English code switching"), | |
| ], | |
| "Transliterated Text": [ | |
| ("Konnichiwa", "transliteration", "Japanese こんにちは in Latin script"), | |
| ("Spasibo", "transliteration", "Russian спасибо in Latin script"), | |
| ("Arigato", "transliteration", "Japanese ありがとう in Latin script"), | |
| ("Privyet", "transliteration", "Russian привет in Latin script"), | |
| ("Sayonara", "transliteration", "Japanese さようなら in Latin script"), | |
| ("Dosvedanya", "transliteration", "Russian до свидания in Latin script"), | |
| ("Nihao", "transliteration", "Chinese 你好 in Latin script"), | |
| ("Annyeonghaseyo", "transliteration", "Korean 안녕하세요 in Latin script"), | |
| ("Zdravstvuyte", "transliteration", "Russian здравствуйте in Latin script"), | |
| ("Ohayo gozaimasu", "transliteration", "Japanese おはようございます in Latin script"), | |
| ], | |
| "Ambiguous Script": [ | |
| ("casa", "ambiguous", "House in Spanish/Italian/Portuguese"), | |
| ("rose", "ambiguous", "Flower in English or pink in French"), | |
| ("more", "ambiguous", "English word or Italian 'deaths'"), | |
| ("come", "ambiguous", "English verb or Italian 'how/like'"), | |
| ("no", "ambiguous", "English word or Spanish 'no'"), | |
| ("si", "ambiguous", "Spanish 'if' or Italian 'yes'"), | |
| ("la", "ambiguous", "English 'la' or French/Spanish/Italian article"), | |
| ("me", "ambiguous", "English pronoun or Spanish 'me'"), | |
| ("le", "ambiguous", "French article or Italian article"), | |
| ("son", "ambiguous", "English word or Spanish 'they are'"), | |
| ] | |
| } | |
| def initialize_models(): | |
| """Initialize all four models for comparison.""" | |
| models = {} | |
| model_configs = [ | |
| ("model-a-dataset-a", "Model A Dataset A"), | |
| ("model-b-dataset-a", "Model B Dataset A"), | |
| ("model-a-dataset-b", "Model A Dataset B"), | |
| ("model-b-dataset-b", "Model B Dataset B") | |
| ] | |
| for model_key, model_name in model_configs: | |
| try: | |
| models[model_key] = { | |
| "detector": LanguageDetector(model_key=model_key), | |
| "name": model_name, | |
| "status": "Ready" | |
| } | |
| except Exception as e: | |
| models[model_key] = { | |
| "detector": None, | |
| "name": model_name, | |
| "status": f"Error: {str(e)}" | |
| } | |
| return models | |
| def detect_with_all_models(text: str, models: Dict) -> Dict[str, Any]: | |
| """Run language detection with all models and return results.""" | |
| results = {} | |
| for model_key, model_info in models.items(): | |
| if model_info["detector"] is None: | |
| results[model_key] = { | |
| "language": "Error", | |
| "confidence": 0.0, | |
| "language_code": "error", | |
| "status": model_info["status"] | |
| } | |
| else: | |
| try: | |
| result = model_info["detector"].detect_language(text) | |
| results[model_key] = { | |
| "language": result["language"], | |
| "confidence": result["confidence"], | |
| "language_code": result["language_code"], | |
| "status": "Success" | |
| } | |
| except Exception as e: | |
| results[model_key] = { | |
| "language": "Error", | |
| "confidence": 0.0, | |
| "language_code": "error", | |
| "status": f"Error: {str(e)}" | |
| } | |
| return results | |
| def create_results_dataframe(texts: List[str], all_results: List[Dict], expected_langs: List[str] = None, categories: List[str] = None) -> pd.DataFrame: | |
| """Create a pandas DataFrame for results display.""" | |
| data = [] | |
| for i, (text, results) in enumerate(zip(texts, all_results)): | |
| row = { | |
| "Text": text[:40] + "..." if len(text) > 40 else text, # Shortened text display | |
| "Expected": expected_langs[i] if expected_langs else "N/A", | |
| "Category": categories[i] if categories else "Custom" | |
| } | |
| expected_lang = expected_langs[i] if expected_langs else None | |
| # Add results from each model - combine language and confidence | |
| for model_key, result in results.items(): | |
| # Shortened model names | |
| if model_key == "model-a-dataset-a": | |
| col_name = "A-A" | |
| elif model_key == "model-b-dataset-a": | |
| col_name = "B-A" | |
| elif model_key == "model-a-dataset-b": | |
| col_name = "A-B" | |
| elif model_key == "model-b-dataset-b": | |
| col_name = "B-B" | |
| else: | |
| col_name = model_key[:6] | |
| # Determine if prediction is correct | |
| predicted_lang = result['language_code'] | |
| is_correct = False | |
| if expected_lang and expected_lang not in ['ambiguous', 'mix', 'transliteration', 'unknown', 'N/A']: | |
| # For specific expected languages, check exact match | |
| is_correct = predicted_lang == expected_lang | |
| emoji = "✅" if is_correct else "🚫" | |
| else: | |
| # For ambiguous/mixed/transliterated/unknown cases, don't show emoji | |
| emoji = "" | |
| # Combine emoji, language code and confidence in one column | |
| if emoji: | |
| row[col_name] = f"{emoji} {predicted_lang} ({result['confidence']:.3f})" | |
| else: | |
| row[col_name] = f"{predicted_lang} ({result['confidence']:.3f})" | |
| data.append(row) | |
| return pd.DataFrame(data) | |
| def run_demo_tests(selected_categories: List[str], custom_texts: str, models: Dict): | |
| """Run tests on selected categories and custom texts.""" | |
| if not selected_categories and not custom_texts.strip(): | |
| return "Please select at least one category or enter custom text.", None | |
| all_texts = [] | |
| expected_langs = [] | |
| categories = [] | |
| # Add selected category samples | |
| for category in selected_categories: | |
| if category in DEMO_SAMPLES: | |
| for text, expected, description in DEMO_SAMPLES[category]: | |
| all_texts.append(text) | |
| expected_langs.append(expected) | |
| categories.append(category) | |
| # Add custom texts | |
| if custom_texts.strip(): | |
| custom_lines = [line.strip() for line in custom_texts.strip().split('\n') if line.strip()] | |
| for text in custom_lines: | |
| all_texts.append(text) | |
| expected_langs.append("unknown") | |
| categories.append("Custom") | |
| if not all_texts: | |
| return "No texts to analyze.", None | |
| # Run detection on all texts | |
| all_results = [] | |
| for text in all_texts: | |
| results = detect_with_all_models(text, models) | |
| all_results.append(results) | |
| # Create results DataFrame | |
| df = create_results_dataframe(all_texts, all_results, expected_langs, categories) | |
| summary = f"Analyzed {len(all_texts)} texts across {len(set(categories))} categories." | |
| return summary, df | |
| def create_demo_interface(): | |
| """Create the demo interface.""" | |
| # Initialize models | |
| models = initialize_models() | |
| with gr.Blocks(title="Language Detection Demo - Model Comparison", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🚀 Language Detection Demo - Model Comparison") | |
| gr.Markdown("Compare all four language detection models simultaneously across various difficulty categories.") | |
| # Model Status Section | |
| with gr.Group(): | |
| gr.Markdown("## 🤖 Model Status") | |
| model_status_text = "" | |
| for model_key, model_info in models.items(): | |
| status_icon = "✅" if model_info["status"] == "Ready" else "❌" | |
| model_status_text += f"{status_icon} **{model_info['name']}**: {model_info['status']}\n\n" | |
| gr.Markdown(model_status_text) | |
| # Category Selection Section | |
| with gr.Group(): | |
| gr.Markdown("## 📊 Test Categories") | |
| gr.Markdown("Select categories to test different aspects of language detection difficulty:") | |
| category_checkboxes = gr.CheckboxGroup( | |
| choices=list(DEMO_SAMPLES.keys()), | |
| label="Select Test Categories", | |
| value=["Easy/Obvious", "Short Text"], # Default selection | |
| interactive=True | |
| ) | |
| # Custom Text Input Section | |
| with gr.Group(): | |
| gr.Markdown("## ✏️ Custom Text Input") | |
| gr.Markdown("Enter your own texts to test (one per line):") | |
| custom_text_input = gr.Textbox( | |
| label="Custom Texts", | |
| placeholder="Enter custom texts here, one per line...\nExample:\nHello world\nBonjour le monde\n你好世界", | |
| lines=5, | |
| max_lines=10 | |
| ) | |
| # Control Buttons | |
| with gr.Row(): | |
| run_demo_btn = gr.Button("🔍 Run Demo Tests", variant="primary", size="lg") | |
| clear_btn = gr.Button("🗑️ Clear Results", variant="secondary") | |
| # Sample Preview Section (moved up, condensed) | |
| with gr.Group(): | |
| gr.Markdown("## 📚 Category Explanations") | |
| gr.Markdown("Understanding what each test category evaluates:") | |
| category_explanations = """ | |
| **Easy/Obvious:** Clear, unambiguous sentences in their native language. Tests basic language detection capability. | |
| **Short Text:** Single words or very short phrases. Tests model performance with minimal context. | |
| **False Friends:** Words that look similar across languages but have different meanings. Tests ability to distinguish between closely related languages. | |
| **Mixed Scripts:** Text containing multiple languages, numbers, symbols, or scripts. Tests handling of multilingual content. | |
| **Proper Nouns:** Names of people, places, or entities that exist across multiple languages. Tests context-dependent detection. | |
| **Common Words:** International words with similar spelling across languages (hotel, taxi, etc.). Tests disambiguation of universal terms. | |
| **Technical Terms:** Specialized vocabulary that may be borrowed or translated across languages. Tests domain-specific detection. | |
| **Code-switching:** Text that switches between languages mid-sentence. Tests handling of bilingual communication patterns. | |
| **Transliterated Text:** Non-Latin scripts written in Latin characters. Tests recognition of transliteration vs. native language. | |
| **Ambiguous Script:** Words that could belong to multiple languages with identical spelling. Tests the model's decision-making under uncertainty. | |
| """ | |
| gr.Markdown(category_explanations) | |
| # Results Section (moved to bottom) | |
| with gr.Group(): | |
| gr.Markdown("## 📈 Results") | |
| summary_output = gr.Textbox( | |
| label="Summary", | |
| interactive=False, | |
| visible=False | |
| ) | |
| results_dataframe = gr.Dataframe( | |
| label="Model Comparison Results (A-A: Model A Dataset A, B-A: Model B Dataset A, A-B: Model A Dataset B, B-B: Model B Dataset B)", | |
| wrap=True, | |
| interactive=False, | |
| visible=False | |
| ) | |
| # Event Handlers | |
| def run_tests(selected_cats, custom_texts): | |
| summary, df = run_demo_tests(selected_cats, custom_texts, models) | |
| if df is not None: | |
| return ( | |
| gr.update(value=summary, visible=True), | |
| gr.update(value=df, visible=True) | |
| ) | |
| else: | |
| return ( | |
| gr.update(value=summary, visible=True), | |
| gr.update(visible=False) | |
| ) | |
| def clear_results(): | |
| return ( | |
| gr.update(value="", visible=False), | |
| gr.update(value=None, visible=False) | |
| ) | |
| # Connect event handlers | |
| run_demo_btn.click( | |
| fn=run_tests, | |
| inputs=[category_checkboxes, custom_text_input], | |
| outputs=[summary_output, results_dataframe] | |
| ) | |
| clear_btn.click( | |
| fn=clear_results, | |
| outputs=[summary_output, results_dataframe] | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_demo_interface() | |
| demo.launch( | |
| server_name="localhost", | |
| server_port=7860, | |
| share=False, | |
| debug=True | |
| ) |