app.py

import torch
import gradio as gr
import json
import time
import random
from transformers import pipeline
import pycountry
from datetime import datetime
from pydantic import BaseModel, PydanticUserError, ConfigDict
from pydantic import BaseModel, ConfigDict
import json

class MyModel(BaseModel):
    request: 'starlette.requests.Request'
    model_config = ConfigDict(arbitrary_types_allowed=True)
from pydantic_core import core_schema
from starlette.requests import Request

def get_pydantic_core_schema(request_type, handler):
    return core_schema.any_schema()

Request.__get_pydantic_core_schema__ = get_pydantic_core_schema
# Initialize pipelines with error handling
try:
    lang_detector = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
    #text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M", torch_dtype=torch.bfloat16)
    text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")
    print("🚀 AI Translation Hub initialized successfully!")
except Exception as e:
    print(f"⚠️ Error initializing models: {e}")

# Extended language support with emojis
LANGUAGES = {
    'English': '🇺🇸',
    'Spanish': '🇪🇸', 
    'French': '🇫🇷',
    'German': '🇩🇪',
    'Italian': '🇮🇹',
    'Portuguese': '🇵🇹',
    'Russian': '🇷🇺',
    'Chinese (Simplified)': '🇨🇳',
    'Japanese': '🇯🇵',
    'Korean': '🇰🇷',
    'Arabic': '🇸🇦',
    'Hindi': '🇮🇳',
    'Dutch': '🇳🇱',
    'Swedish': '🇸🇪',
    'Norwegian': '🇳🇴'
}

# Load language data with fallback
try:
    with open('language.json', 'r') as file:
        language_data = json.load(file)
except FileNotFoundError:
    print("⚠️ Language data file not found. Using basic mapping.")
    language_data = {'languages': []}

# Translation statistics
translation_stats = {
    'total_translations': 0,
    'languages_detected': set(),
    'session_start': datetime.now()
}

def get_FLORES_code_from_language(language):
    """Enhanced FLORES code lookup with fallback mapping"""
    # Remove emoji and extract clean language name
    import re
    # Remove emoji flags and extra spaces
    clean_language = re.sub(r'[🇦-🇿]{2}\s*', '', language).strip()
    
    for entry in language_data.get('languages', []):
        if entry['Language'].lower() == clean_language.lower():
            return entry['FLORES-200 code']
    
    # Fallback mapping for common languages
    fallback_mapping = {
        'english': 'eng_Latn',
        'spanish': 'spa_Latn',
        'french': 'fra_Latn',
        'german': 'deu_Latn',
        'chinese (simplified)': 'zho_Hans',
        'italian': 'ita_Latn',
        'portuguese': 'por_Latn',
        'russian': 'rus_Cyrl',
        'japanese': 'jpn_Jpan',
        'korean': 'kor_Hang',
        'arabic': 'arb_Arab',
        'hindi': 'hin_Deva',
        'dutch': 'nld_Latn',
        'swedish': 'swe_Latn',
        'norwegian': 'nor_Latn'
    }
    
    return fallback_mapping.get(clean_language.lower())

def detect_language_confidence(text):
    """Get language detection with confidence score"""
    if not text.strip():
        return "Unknown", 0.0
    
    try:
        result = lang_detector(text)[0]
        return result['label'], result['score']
    except:
        return "Unknown", 0.0

def translate_with_analytics(text, destination_language, show_confidence=True):
    """Enhanced translation with analytics and progress tracking"""
    
    if not text.strip():
        return "⚠️ Please enter some text to translate", "", ""
    
    # Update statistics
    translation_stats['total_translations'] += 1
    
    # Simulate processing for dramatic effect
    yield "🔍 Analyzing text...", "", ""
    time.sleep(0.5)
    
    # Detect source language with confidence
    detected_lang, confidence = detect_language_confidence(text)
    translation_stats['languages_detected'].add(detected_lang)
    
    yield f"🧠 Detected language: {detected_lang.upper()} ({confidence:.1%} confidence)", "", ""
    time.sleep(0.3)
    
    # Get language codes
    try:
        lang = pycountry.languages.get(alpha_2=detected_lang)
        src_code = f"{lang.alpha_3}_Latn" if lang else "eng_Latn"
    except:
        src_code = "eng_Latn"
    
    dest_code = get_FLORES_code_from_language(destination_language)
    if not dest_code:
        yield f"❌ Unsupported target language: {destination_language}", "", ""
        return
    
    yield f"⚡ Translating to {destination_language.split(' ', 1)[-1] if ' ' in destination_language else destination_language}...", "", ""
    time.sleep(0.5)
    
    # Handle same language
    if src_code == dest_code:
        analytics = f"""
📊 **Translation Analytics**
- Source: {detected_lang.upper()} ({confidence:.1%} confidence)
- Target: Same language detected
- Action: No translation needed
- Processing time: <1s
        """
        yield "✅ Translation complete!", text, analytics.strip()
        return
    
    # Perform translation
    try:
        start_time = time.time()
        
        # Calculate appropriate max_length based on input length
        input_length = len(text)
        # Set max_length to be 1.5x input length with a minimum of 512 and maximum of 2048
        max_length = max(512, min(2048, int(input_length * 1.5)))
        
        translation = text_translator(
            text, 
            src_lang=src_code, 
            tgt_lang=dest_code,
            max_length=max_length,
            do_sample=False,  # For more consistent results
            num_beams=4       # Better quality translation
        )
        processing_time = time.time() - start_time
        
        result = translation[0]['translation_text']
        
        # Generate analytics
        import re
        clean_dest_lang = re.sub(r'[🇦-🇿]{2}\s*', '', destination_language).strip()
        
        analytics = f"""
📊 **Translation Analytics**
- **Source Language**: {detected_lang.upper()} ({confidence:.1%} confidence)
- **Target Language**: {clean_dest_lang}
- **Characters Processed**: {len(text):,}
- **Max Length Used**: {max_length}
- **Processing Time**: {processing_time:.2f}s
- **Session Translations**: {translation_stats['total_translations']}
- **Languages Detected**: {len(translation_stats['languages_detected'])}
        """
        
        yield "✅ Translation complete!", result, analytics.strip()
        
    except Exception as e:
        yield f"❌ Translation failed: {str(e)}", "", ""

def clear_all():
    """Reset all fields"""
    return "", "", "", ""

# Custom CSS for a modern, sleek interface
custom_css = """
.gradio-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}

.gr-button {
    background: linear-gradient(45deg, #FF6B6B, #4ECDC4);
    border: none;
    border-radius: 25px;
    color: white;
    font-weight: bold;
    transition: all 0.3s ease;
    box-shadow: 0 4px 15px rgba(0,0,0,0.2);
}

.gr-button:hover {
    transform: translateY(-2px);
    box-shadow: 0 6px 20px rgba(0,0,0,0.3);
}

.gr-textbox {
    border-radius: 15px;
    border: 2px solid #e0e0e0;
    transition: all 0.3s ease;
}

.gr-textbox:focus {
    border-color: #667eea;
    box-shadow: 0 0 15px rgba(102, 126, 234, 0.3);
}

.gr-dropdown {
    border-radius: 15px;
    border: 2px solid #e0e0e0;
}

.gr-panel {
    background: rgba(255,255,255,0.95);
    border-radius: 20px;
    backdrop-filter: blur(10px);
    box-shadow: 0 8px 32px rgba(0,0,0,0.1);
}

.gr-form {
    background: transparent;
}

.gr-box {
    border-radius: 15px;
    background: rgba(255,255,255,0.9);
}
"""

# Create the interface
with gr.Blocks(css=custom_css, title="🌍 KS Translation Hub") as demo:
    gr.HTML("""
    <div style="text-align: center; padding: 20px; background: linear-gradient(45deg, #FF6B6B, #4ECDC4); border-radius: 20px; margin-bottom: 20px;">
        <h1 style="color: white; font-size: 3em; margin: 0; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);">
            🌍 KS Translation Hub
        </h1>
        <p style="color: white; font-size: 1.2em; margin: 10px 0 0 0; opacity: 0.9;">
            Powered by Advanced Neural Networks • Real-time Language Detection • 15+ Languages
        </p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.HTML("<h3 style='text-align: center; color: #333;'>📝 Input</h3>")
            input_text = gr.Textbox(
                label="Enter text to translate",
                placeholder="Type or paste your text here... 🖊️",
                lines=8,
                show_label=False
            )
            
            with gr.Row():
                target_lang = gr.Dropdown(
                    choices=[f"{flag} {lang}" for lang, flag in LANGUAGES.items()],
                    label="🎯 Target Language",
                    value="🇪🇸 Spanish",
                    show_label=True
                )
        
        with gr.Column(scale=1):
            gr.HTML("<h3 style='text-align: center; color: #333;'>✨ Output</h3>")
            output_text = gr.Textbox(
                label="Translation",
                lines=8,
                show_label=False,
                interactive=False
            )
    
    with gr.Row():
        with gr.Column(scale=1):
            status_text = gr.Textbox(
                label="🔄 Status",
                value="Ready to translate...",
                interactive=False,
                show_label=True
            )
        
        with gr.Column(scale=1):
            analytics_text = gr.Textbox(
                label="📊 Analytics",
                value="Translation analytics will appear here...",
                interactive=False,
                show_label=True,
                lines=6
            )
    
    with gr.Row():
        translate_btn = gr.Button("🚀 Translate", variant="primary", size="lg")
        clear_btn = gr.Button("🗑️ Clear All", variant="secondary", size="lg")
    
    # Event handlers
    translate_btn.click(
        fn=translate_with_analytics,
        inputs=[input_text, target_lang],
        outputs=[status_text, output_text, analytics_text]
    )
    
    clear_btn.click(
        fn=clear_all,
        outputs=[input_text, output_text, status_text, analytics_text]
    )
    
    # Auto-translate on Enter key
    input_text.submit(
        fn=translate_with_analytics,
        inputs=[input_text, target_lang],
        outputs=[status_text, output_text, analytics_text]
    )
    
    gr.HTML("""
    <div style="text-align: center; padding: 20px; margin-top: 20px; background: rgba(255,255,255,0.8); border-radius: 15px;">
        <p style="color: #666; font-size: 0.9em;">
            🤖 Powered by Transformers • 🔒 Privacy-First • ⚡ Real-time Processing
        </p>
    </div>
    """)

# Launch with enhanced settings
if __name__ == "__main__":
    demo.launch()
    #demo.launch(
    #    share=True,
    #    server_name="127.0.0.1",
    #    server_port=7860,
        #show_tips=True,
        #enable_queue=True,
    #    max_threads=40
    #)