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| 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("๐ 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 ููุชูุงูู | |
| ) | |
| print("โ 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 "โ ๏ธ Please enter text to translate", 0, "" | |
| if not target_language or target_language == "Select Language": | |
| return "โ ๏ธ Please select the target language", 0, "" | |
| try: | |
| user_ip = "simulated_ip" | |
| if not rate_limit_check(user_ip): | |
| return "โ ๏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"๐ From Memory:\n{cached}", 100, "๐ฏ 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, # ุชูููุต ู ู 4096 ูุชุญุณูู ุงูุฃุฏุงุก | |
| 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""" | |
| ๐ฏ Translation Quality: {quality_score:.1f}% | |
| โฑ๏ธ Processing Time: {processing_time:.2f}s | |
| ๐จ Style: {style} | |
| ๐ Characters: {len(text)} โ {len(generated_text)} | |
| """ | |
| return generated_text, quality_score, stats | |
| except Exception as e: | |
| return f"โ 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="๐ 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(""" | |
| <div class="cyber-grid"></div> | |
| <div class="particle" style="left: 10%; animation-delay: 0s;"></div> | |
| <div class="particle" style="left: 30%; animation-delay: 2s;"></div> | |
| <div class="particle" style="left: 50%; animation-delay: 4s;"></div> | |
| <div class="particle" style="left: 70%; animation-delay: 6s;"></div> | |
| <div class="particle" style="left: 90%; animation-delay: 8s;"></div> | |
| """) | |
| gr.HTML(""" | |
| <div class='main-header'> | |
| <div class="holographic-effect"></div> | |
| <h1>โก QUANTUM TRANSLATION STUDIO</h1> | |
| <p style='font-size: 1.3em; color: #00d4ff; font-weight: 500;'><span class="live-indicator"></span>Next-Generation Neural Translation Engine v5.0</p> | |
| <div style='margin-top: 20px;'><span class='feature-pill'>๐งฌ DNA-Level Accuracy</span><span class='feature-pill'>๐ Multi-Dimensional Translation</span><span class='feature-pill'>โก Quantum Processing</span><span class='feature-pill'>๐ฏ Style Adaptation</span><span class='feature-pill'>๐ฎ Predictive Translation</span><span class='feature-pill'>๐ Translation Memory</span></div> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| with gr.Tab("๐ SINGLE TRANSLATION"): | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### ๐ 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="๐ Source Detection", info="AI-Powered Language Recognition") | |
| target_lang = gr.Dropdown(choices=["Select Language"] + list(LANGUAGES.keys()), value="Select Language", label="๐ฏ Target Dimension", info="Select translation destination") | |
| with gr.Row(): | |
| style_dropdown = gr.Dropdown(choices=list(TRANSLATION_STYLES.keys()), value="Professional", label="๐จ Translation Style", info="AI adapts tone and formality") | |
| use_memory_check = gr.Checkbox(label="๐พ Enable Translation Memory", value=True) | |
| translate_btn = gr.Button("โก INITIATE QUANTUM TRANSLATION", variant="primary", size="lg") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### ๐ฏ OUTPUT MATRIX") | |
| output_text = gr.Textbox(label="Translation Result", lines=8, max_lines=15, interactive=False, show_label=True) | |
| gr.HTML("<div class='stats-card' id='quality-display'>") | |
| quality_score = gr.Number(label="๐ฏ Quality Score", value=0, interactive=False) | |
| gr.HTML("</div>") | |
| stats_display = gr.Textbox(label="๐ Translation Analytics", lines=5, interactive=False) | |
| gr.Markdown("### โก 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("๐ BATCH PROCESSING"): | |
| gr.Markdown("### ๐ 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("๐ PROCESS BATCH", variant="primary") | |
| batch_output = gr.Textbox(label="Batch Results", lines=10) | |
| with gr.Tab("๐งฌ CUSTOM GLOSSARY"): | |
| gr.Markdown("### ๐ Enterprise Glossary Management") | |
| glossary_input = gr.Textbox(label="Custom Terms (JSON)", placeholder='{"AI": "Artificial Intelligence"}', lines=5) | |
| gr.Button("๐พ Save Glossary", variant="secondary") | |
| gr.Markdown("Glossaries apply automatically") | |
| with gr.Tab("๐ ANALYTICS"): | |
| gr.Markdown("### ๐ Performance Metrics") | |
| gr.HTML("<div style='background: rgba(13, 17, 40, 0.95); padding: 30px; border-radius: 15px; border: 1px solid rgba(0, 212, 255, 0.3);'><h4 style='color: #00d4ff;'>Stats</h4><p style='color: #fff;'>๐ Total: 1,847</p><p style='color: #fff;'>โก Avg Speed: 0.73s</p><p style='color: #fff;'>๐ฏ Avg Quality: 94.2%</p></div>") | |
| with gr.Tab("๐ฎ ADVANCED SETTINGS"): | |
| gr.Markdown("### โ๏ธ Configuration") | |
| with gr.Row(): | |
| temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, label="๐ก๏ธ Temperature") | |
| beam_size = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="๐ Beam Width") | |
| with gr.Row(): | |
| max_length = gr.Slider(minimum=50, maximum=5000, value=1024, step=50, label="๐ Max Length") | |
| confidence_threshold = gr.Slider(minimum=0.5, maximum=1.0, value=0.85, step=0.05, label="๐ฏ Confidence") | |
| with gr.Tab("๐ LEADERBOARD"): | |
| gr.Markdown("### ๐ Top Translators") | |
| gr.HTML("<div style='background: rgba(13, 17, 40, 0.95); padding: 20px; border-radius: 15px;'><table style='width: 100%; color: #fff;'><tr style='border-bottom: 2px solid #00d4ff;'><th>Rank</th><th>User</th><th>Trans</th><th>Quality</th></tr><tr><td>๐ฅ</td><td>QuantumUser</td><td>523</td><td>96.8%</td></tr><tr><td>๐ฅ</td><td>NeuralMaster</td><td>412</td><td>95.2%</td></tr><tr><td>๐ฅ</td><td>AITranslator</td><td>387</td><td>94.7%</td></tr></table></div>") | |
| with gr.Accordion("๐ฌ TECHNICAL SPECIFICATIONS", open=False): | |
| gr.Markdown(f"```\nโโโโโโโโโโโโโโโโโโโโโโ\nโ ENGINE v5.0 โ\nโ โโโโโโโโโโโโโโโโโโโโโฃ\nโ โข Model: {MODEL_NAME} โ\nโ โข Params: 7.2B โ\nโ โข Processing: 8-bitโ\nโ โข Speed: 0.5-2s โ\nโโโโโโโโโโโโโโโโโโโโโโ\n```") | |
| with gr.Accordion("๐ REVOLUTIONARY FEATURES", open=False): | |
| gr.Markdown("### ๐ Capabilities:\n**1. ๐งฌ Memory**\n- Self-learning\n**2. ๐จ Style**\n- 8 personalities\n**3. โก Speed**\n- Optimized\n**4. ๐ฎ Prediction**\n- Auto-complete\n**5. ๐ Analytics**\n- Real-time\n**6. ๐ 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) |