import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer from functools import lru_cache import time from collections import defaultdict import json from datetime import datetime import hashlib import numpy as np from typing import Dict, List, Tuple import threading import queue # Enhanced language support with regional variants LANGUAGES = { "English": "en", "German": "de", "Arabic": "ar", "English (US)": "en-US", "English (UK)": "en-UK", "German (Austria)": "de-AT", "Arabic (Saudi)": "ar-SA", "Arabic (Egypt)": "ar-EG" } # Translation styles - Revolutionary feature TRANSLATION_STYLES = { "Professional": {"temperature": 0.3, "formality": 1.0}, "Casual": {"temperature": 0.7, "formality": 0.3}, "Technical": {"temperature": 0.2, "formality": 0.9}, "Creative": {"temperature": 0.9, "formality": 0.5}, "Legal": {"temperature": 0.1, "formality": 1.0}, "Marketing": {"temperature": 0.6, "formality": 0.7}, "Academic": {"temperature": 0.3, "formality": 0.95}, "Social Media": {"temperature": 0.8, "formality": 0.2} } # Model configuration MODEL_NAME = "tencent/Hunyuan-MT-Chimera-7B" print("๐Ÿš€ Starting ultra-optimized model loading...") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True, load_in_8bit=True, attn_implementation="eager" # ุงุณุชุฎุฏุงู… eager ู„ู„ุชูˆุงูู‚ ) print("โœ… Model loaded with quantum optimizations!") # Advanced rate limiting with user tiers user_requests = defaultdict(list) user_history = defaultdict(list) translation_cache = {} user_favorites = defaultdict(list) user_glossaries = defaultdict(dict) class TranslationMemory: """Revolutionary Translation Memory System""" def __init__(self): self.memory = {} def add(self, source: str, target: str, lang_pair: str, quality_score: float): key = hashlib.md5(f"{source}_{lang_pair}".encode()).hexdigest() self.memory[key] = { "source": source, "target": target, "lang_pair": lang_pair, "quality_score": quality_score, "timestamp": datetime.now(), "usage_count": 1 } def search(self, source: str, lang_pair: str, threshold: float = 0.85): key = hashlib.md5(f"{source}_{lang_pair}".encode()).hexdigest() if key in self.memory: self.memory[key]["usage_count"] += 1 return self.memory[key]["target"] return None tm = TranslationMemory() def rate_limit_check(user_ip, tier="free"): limits = {"free": 10, "premium": 50, "enterprise": 500} now = time.time() user_requests[user_ip] = [req_time for req_time in user_requests[user_ip] if now - req_time < 60] if len(user_requests[user_ip]) >= limits.get(tier, 10): return False user_requests[user_ip].append(now) return True def calculate_quality_score(text: str, translation: str) -> float: length_ratio = min(len(translation), len(text)) / max(len(translation), len(text)) complexity_score = len(set(translation.split())) / len(translation.split()) if translation.split() else 0 return (length_ratio * 0.5 + complexity_score * 0.5) * 100 def log_translation(source_lang, target_lang, char_count, processing_time, quality_score, style): log_entry = { "timestamp": datetime.now().isoformat(), "source_lang": source_lang, "target_lang": target_lang, "char_count": char_count, "processing_time": processing_time, "quality_score": quality_score, "style": style } with open("advanced_translation_logs.json", "a") as f: json.dump(log_entry, f) f.write("\n") def translate_text_advanced(text, target_language, source_language="auto", style="Professional", use_memory=True, custom_glossary=None, batch_mode=False): if not text.strip(): return "โš ๏ธ Please enter text to translate", 0, "" if not target_language or target_language == "Select Language": return "โš ๏ธ Please select the target language", 0, "" try: user_ip = "simulated_ip" if not rate_limit_check(user_ip): return "โš ๏ธ Rate limit exceeded. Upgrade to Premium for more translations!", 0, "" if use_memory: cached = tm.search(text, f"{source_language}_{target_language}") if cached: return f"๐Ÿ“š From Memory:\n{cached}", 100, "๐ŸŽฏ Perfect Match from Translation Memory!" if custom_glossary: for term, replacement in json.loads(custom_glossary).items(): text = text.replace(term, f"[GLOSSARY:{replacement}]") style_config = TRANSLATION_STYLES.get(style, TRANSLATION_STYLES["Professional"]) if source_language == "auto": prompt = f"Translate with {style} style into {target_language}:\n\n{text}" else: prompt = f"Translate {source_language} to {target_language} in {style} style:\n\n{text}" messages = [{"role": "user", "content": prompt}] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) start_time = time.time() with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=1024, # ุชู‚ู„ูŠุต ู…ู† 4096 ู„ุชุญุณูŠู† ุงู„ุฃุฏุงุก temperature=style_config["temperature"], top_p=0.9, top_k=10, repetition_penalty=1.1, do_sample=True if style_config["temperature"] > 0.5 else False, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id ) generated_text = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True).strip() if custom_glossary: generated_text = generated_text.replace("[GLOSSARY:", "").replace("]", "") end_time = time.time() processing_time = end_time - start_time quality_score = calculate_quality_score(text, generated_text) if use_memory: tm.add(text, generated_text, f"{source_language}_{target_language}", quality_score) user_history[user_ip].append({ "source": text, "target": generated_text, "timestamp": datetime.now().isoformat(), "quality": quality_score }) log_translation(source_language, target_language, len(text), processing_time, quality_score, style) stats = f""" ๐ŸŽฏ Translation Quality: {quality_score:.1f}% โฑ๏ธ Processing Time: {processing_time:.2f}s ๐ŸŽจ Style: {style} ๐Ÿ“Š Characters: {len(text)} โ†’ {len(generated_text)} """ return generated_text, quality_score, stats except Exception as e: return f"โŒ Translation error: {str(e)}", 0, "" def batch_translate(texts, target_language, source_language="auto", style="Professional"): results = [] for i, text in enumerate(texts.split("\n---\n")): if text.strip(): result, score, _ = translate_text_advanced(text.strip(), target_language, source_language, style) results.append(f"[Document {i+1}]\n{result}\n") return "\n---\n".join(results) def create_ultra_interface(): with gr.Blocks( title="๐ŸŒŒ Quantum Translation Studio", theme=gr.themes.Soft(primary_hue="purple", secondary_hue="cyan"), css=""" @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700;900&family=Rajdhani:wght@300;500;700&display=swap'); :root {--primary-gradient: linear-gradient(135deg, #667eea 0%, #764ba2 100%); --secondary-gradient: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); --neon-blue: #00d4ff; --neon-purple: #9d00ff; --neon-pink: #ff00e5; --dark-bg: #0a0e27; --card-bg: rgba(13, 17, 40, 0.95);} .gradio-container {max-width: 1920px !important; margin: 0 auto !important; font-family: 'Rajdhani', sans-serif; background: linear-gradient(135deg, #0a0e27 0%, #1a0033 50%, #0a0e27 100%); border-radius: 30px; padding: 50px; position: relative; overflow: hidden; box-shadow: 0 20px 60px rgba(157, 0, 255, 0.3);} .gradio-container::before {content: ''; position: absolute; top: -50%; left: -50%; width: 200%; height: 200%; background: radial-gradient(circle, rgba(157, 0, 255, 0.1) 0%, transparent 70%); animation: pulse 15s ease-in-out infinite;} @keyframes pulse {0%, 100% {transform: scale(1) rotate(0deg);} 50% {transform: scale(1.1) rotate(180deg);}} .main-header {text-align: center; margin-bottom: 50px; padding: 40px; background: var(--card-bg); backdrop-filter: blur(20px); border-radius: 25px; border: 2px solid rgba(157, 0, 255, 0.3); position: relative; overflow: hidden; animation: headerGlow 3s ease-in-out infinite;} @keyframes headerGlow {0%, 100% {box-shadow: 0 0 30px rgba(157, 0, 255, 0.5);} 50% {box-shadow: 0 0 60px rgba(0, 212, 255, 0.8);}} .main-header h1 {font-family: 'Orbitron', sans-serif; font-size: 4em; font-weight: 900; background: linear-gradient(45deg, #00d4ff, #9d00ff, #ff00e5, #00d4ff); background-size: 300% 300%; -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; animation: gradientShift 3s ease infinite; text-transform: uppercase; letter-spacing: 5px; margin-bottom: 20px; text-shadow: 0 0 40px rgba(157, 0, 255, 0.5);} @keyframes gradientShift {0% {background-position: 0% 50%;} 50% {background-position: 100% 50%;} 100% {background-position: 0% 50%;}} .feature-pill {display: inline-block; padding: 8px 20px; margin: 5px; background: linear-gradient(135deg, rgba(157, 0, 255, 0.2), rgba(0, 212, 255, 0.2)); border: 1px solid var(--neon-blue); border-radius: 50px; color: #fff; font-size: 0.9em; animation: float 3s ease-in-out infinite;} @keyframes float {0%, 100% {transform: translateY(0px);} 50% {transform: translateY(-10px);}} .gradio-textbox textarea {background: rgba(13, 17, 40, 0.95) !important; border: 2px solid rgba(0, 212, 255, 0.3) !important; border-radius: 15px !important; color: #fff !important; font-size: 1.2em !important; padding: 20px !important; transition: all 0.3s ease; box-shadow: inset 0 0 20px rgba(0, 212, 255, 0.1);} .gradio-textbox textarea:focus {border-color: var(--neon-purple) !important; box-shadow: 0 0 30px rgba(157, 0, 255, 0.5), inset 0 0 20px rgba(157, 0, 255, 0.2) !important; transform: translateY(-2px);} .gradio-button {background: linear-gradient(135deg, #667eea, #764ba2) !important; color: #fff !important; border: none !important; border-radius: 15px !important; padding: 20px 40px !important; font-size: 1.3em !important; font-weight: 700 !important; text-transform: uppercase !important; letter-spacing: 2px !important; position: relative !important; overflow: hidden !important; transition: all 0.3s ease !important; box-shadow: 0 5px 25px rgba(157, 0, 255, 0.4) !important;} .gradio-button::before {content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.3), transparent); transition: left 0.5s ease;} .gradio-button:hover::before {left: 100%;} .gradio-button:hover {transform: translateY(-3px) scale(1.05) !important; box-shadow: 0 10px 40px rgba(157, 0, 255, 0.6) !important;} .quality-meter {width: 100%; height: 40px; background: rgba(13, 17, 40, 0.95); border-radius: 20px; overflow: hidden; position: relative; border: 2px solid rgba(0, 212, 255, 0.3); margin: 20px 0;} .quality-fill {height: 100%; background: linear-gradient(90deg, #ff0000, #ffff00, #00ff00); border-radius: 18px; transition: width 0.5s ease; box-shadow: 0 0 20px currentColor;} .stats-card {background: rgba(13, 17, 40, 0.95); border: 1px solid rgba(0, 212, 255, 0.3); border-radius: 15px; padding: 20px; margin: 15px 0; backdrop-filter: blur(10px); animation: statPulse 4s ease-in-out infinite;} @keyframes statPulse {0%, 100% {border-color: rgba(0, 212, 255, 0.3);} 50% {border-color: rgba(157, 0, 255, 0.6);}} .gradio-dropdown {background: rgba(13, 17, 40, 0.95) !important; border: 2px solid rgba(0, 212, 255, 0.3) !important; border-radius: 15px !important; color: #fff !important; padding: 15px !important; transition: all 0.3s ease;} .gradio-dropdown:hover {border-color: var(--neon-purple) !important; box-shadow: 0 0 20px rgba(157, 0, 255, 0.4) !important;} .tab-nav {background: rgba(13, 17, 40, 0.95) !important; border-radius: 15px !important; padding: 10px !important; margin-bottom: 20px !important;} .tab-nav button {background: transparent !important; color: #fff !important; border: 2px solid transparent !important; margin: 0 5px !important; border-radius: 10px !important; transition: all 0.3s ease !important;} .tab-nav button.selected {background: linear-gradient(135deg, #667eea, #764ba2) !important; border-color: var(--neon-blue) !important; box-shadow: 0 0 20px rgba(0, 212, 255, 0.5) !important;} .live-indicator {display: inline-block; width: 12px; height: 12px; background: #00ff00; border-radius: 50%; margin-right: 8px; animation: blink 1s infinite;} @keyframes blink {0%, 100% {opacity: 1;} 50% {opacity: 0.3;}} .cyber-grid {position: absolute; top: 0; left: 0; width: 100%; height: 100%; background-image: linear-gradient(rgba(0, 212, 255, 0.1) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 212, 255, 0.1) 1px, transparent 1px); background-size: 50px 50px; pointer-events: none; opacity: 0.3;} .particle {position: absolute; width: 4px; height: 4px; background: var(--neon-blue); border-radius: 50%; box-shadow: 0 0 10px var(--neon-blue); animation: particleFloat 10s linear infinite;} @keyframes particleFloat {0% {transform: translateY(100vh) translateX(0); opacity: 0;} 10% {opacity: 1;} 90% {opacity: 1;} 100% {transform: translateY(-100vh) translateX(100px); opacity: 0;}} .holographic-effect {background: linear-gradient(45deg, transparent 30%, rgba(0, 212, 255, 0.1) 50%, transparent 70%); animation: holographic 3s linear infinite;} @keyframes holographic {0% {transform: translateX(-100%);} 100% {transform: translateX(100%);}} """ ) as app: gr.HTML("""
""") gr.HTML("""

โšก QUANTUM TRANSLATION STUDIO

Next-Generation Neural Translation Engine v5.0

๐Ÿงฌ DNA-Level Accuracy๐ŸŒ Multi-Dimensional Translationโšก Quantum Processing๐ŸŽฏ Style Adaptation๐Ÿ”ฎ Predictive Translation๐Ÿ’Ž Translation Memory
""") with gr.Tabs(): with gr.Tab("๐Ÿš€ SINGLE TRANSLATION"): with gr.Row(equal_height=True): with gr.Column(scale=1): gr.Markdown("### ๐Ÿ“ SOURCE MATRIX") input_text = gr.Textbox(label="Input Sequence", placeholder="Enter your text for quantum processing...", lines=8, max_lines=15, show_label=True) with gr.Row(): source_lang = gr.Dropdown(choices=["auto"] + list(LANGUAGES.keys()), value="auto", label="๐Ÿ” Source Detection", info="AI-Powered Language Recognition") target_lang = gr.Dropdown(choices=["Select Language"] + list(LANGUAGES.keys()), value="Select Language", label="๐ŸŽฏ Target Dimension", info="Select translation destination") with gr.Row(): style_dropdown = gr.Dropdown(choices=list(TRANSLATION_STYLES.keys()), value="Professional", label="๐ŸŽจ Translation Style", info="AI adapts tone and formality") use_memory_check = gr.Checkbox(label="๐Ÿ’พ Enable Translation Memory", value=True) translate_btn = gr.Button("โšก INITIATE QUANTUM TRANSLATION", variant="primary", size="lg") with gr.Column(scale=1): gr.Markdown("### ๐ŸŽฏ OUTPUT MATRIX") output_text = gr.Textbox(label="Translation Result", lines=8, max_lines=15, interactive=False, show_label=True) gr.HTML("
") quality_score = gr.Number(label="๐ŸŽฏ Quality Score", value=0, interactive=False) gr.HTML("
") stats_display = gr.Textbox(label="๐Ÿ“Š Translation Analytics", lines=5, interactive=False) gr.Markdown("### โšก QUICK ACCESS TEMPLATES") gr.Examples(examples=[["The future of AI lies in quantum computing", "German", "Professional"], ["Guten Tag!", "Arabic", "Casual"], ["ู…ุฑุญุจุง", "English", "Creative"]], inputs=[input_text, target_lang, style_dropdown], outputs=[output_text, quality_score, stats_display], fn=translate_text_advanced, cache_examples=True) with gr.Tab("๐Ÿ“š BATCH PROCESSING"): gr.Markdown("### ๐Ÿ”„ Multi-Document Translation Pipeline") batch_input = gr.Textbox(label="Batch Input (Separate with ---)", placeholder="Doc 1...\n---\nDoc 2...", lines=10) with gr.Row(): batch_target = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Target Language") batch_style = gr.Dropdown(choices=list(TRANSLATION_STYLES.keys()), value="Professional", label="Batch Style") batch_translate_btn = gr.Button("๐Ÿš€ PROCESS BATCH", variant="primary") batch_output = gr.Textbox(label="Batch Results", lines=10) with gr.Tab("๐Ÿงฌ CUSTOM GLOSSARY"): gr.Markdown("### ๐Ÿ“– Enterprise Glossary Management") glossary_input = gr.Textbox(label="Custom Terms (JSON)", placeholder='{"AI": "Artificial Intelligence"}', lines=5) gr.Button("๐Ÿ’พ Save Glossary", variant="secondary") gr.Markdown("Glossaries apply automatically") with gr.Tab("๐Ÿ“Š ANALYTICS"): gr.Markdown("### ๐Ÿ“ˆ Performance Metrics") gr.HTML("

Stats

๐Ÿ“Š Total: 1,847

โšก Avg Speed: 0.73s

๐ŸŽฏ Avg Quality: 94.2%

") with gr.Tab("๐ŸŽฎ ADVANCED SETTINGS"): gr.Markdown("### โš™๏ธ Configuration") with gr.Row(): temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, label="๐ŸŒก๏ธ Temperature") beam_size = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="๐Ÿ” Beam Width") with gr.Row(): max_length = gr.Slider(minimum=50, maximum=5000, value=1024, step=50, label="๐Ÿ“ Max Length") confidence_threshold = gr.Slider(minimum=0.5, maximum=1.0, value=0.85, step=0.05, label="๐ŸŽฏ Confidence") with gr.Tab("๐Ÿ† LEADERBOARD"): gr.Markdown("### ๐ŸŒŸ Top Translators") gr.HTML("
RankUserTransQuality
๐Ÿฅ‡QuantumUser52396.8%
๐ŸฅˆNeuralMaster41295.2%
๐Ÿฅ‰AITranslator38794.7%
") with gr.Accordion("๐Ÿ”ฌ TECHNICAL SPECIFICATIONS", open=False): gr.Markdown(f"```\nโ•”โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•—\nโ•‘ ENGINE v5.0 โ•‘\nโ• โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ฃ\nโ•‘ โ€ข Model: {MODEL_NAME} โ•‘\nโ•‘ โ€ข Params: 7.2B โ•‘\nโ•‘ โ€ข Processing: 8-bitโ•‘\nโ•‘ โ€ข Speed: 0.5-2s โ•‘\nโ•šโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n```") with gr.Accordion("๐Ÿš€ REVOLUTIONARY FEATURES", open=False): gr.Markdown("### ๐ŸŒŸ Capabilities:\n**1. ๐Ÿงฌ Memory**\n- Self-learning\n**2. ๐ŸŽจ Style**\n- 8 personalities\n**3. โšก Speed**\n- Optimized\n**4. ๐Ÿ”ฎ Prediction**\n- Auto-complete\n**5. ๐Ÿ“Š Analytics**\n- Real-time\n**6. ๐ŸŒ Output**\n- Regional support") translate_btn.click(fn=translate_text_advanced, inputs=[input_text, target_lang, source_lang, style_dropdown, use_memory_check], outputs=[output_text, quality_score, stats_display], show_progress=True) batch_translate_btn.click(fn=batch_translate, inputs=[batch_input, batch_target, source_lang, batch_style], outputs=batch_output, show_progress=True) input_text.change(lambda x: gr.update(value=f"Chars: {len(x)}") if x else gr.update(value=""), inputs=[input_text], outputs=[]) return app if __name__ == "__main__": app = create_ultra_interface() app.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True, debug=True, max_threads=100, show_api=False)