Upload quantum_inference.py: Quantum-enhanced inference script

quantum_inference.py

@@ -0,0 +1,234 @@
+#!/usr/bin/env python3
+"""
+Hypnos-i3-1.5B - Quantum-Classical Hybrid Model
+================================================
+Sentiment Analysis с квантовым ядром
+
+Версия: 1.0
+Релиз: December 2024
+"""
+
+import numpy as np
+import json
+import torch
+from transformers import AutoModel, AutoTokenizer
+from sklearn.decomposition import PCA
+import time
+
+print("="*70)
+print("🌙 HYPNOS-i3-1.5B - QUANTUM-CLASSICAL MODEL")
+print("="*70)
+print("Version: 1.0")
+print("Type: Quantum Kernel-Enhanced Sentiment Analysis")
+print("Base: VibeThinker-1.5B + 2-qubit Quantum Kernel\n")
+
+# Проверка наличия модели
+try:
+    with open('hypnos_i3_results.json', 'r') as f:
+        results = json.load(f)
+    
+    K_train = np.load('K_train_quantum.npy')
+    K_test = np.load('K_test_quantum.npy')
+    
+    print("✅ Модель загружена из кэша")
+    print(f"📊 Точность (quantum): {results['accuracy_quantum']:.1%}")
+    print(f"📊 Точность (classical): {results['accuracy_baseline']:.1%}")
+    
+except FileNotFoundError:
+    print("⚠️  Модель не найдена. Запустите обучение:")
+    print("   python3 hypnos_i3.py")
+    exit(1)
+
+# Загрузка VibeThinker
+print("\n🔄 Загрузка базовой модели VibeThinker-1.5B...")
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-1.5B")
+model = AutoModel.from_pretrained(
+    "WeiboAI/VibeThinker-1.5B",
+    torch_dtype=torch.float16
+).to(device).eval()
+
+print(f"✅ VibeThinker загружен на {device}")
+
+# Обучающие данные (для квантового ядра)
+TRAIN_DATA = [
+    ("I absolutely love quantum computing! It's amazing!", 1),
+    ("This is the worst experience ever, terrible.", 0),
+    ("Quantum neural networks are fascinating and powerful.", 1),
+    ("I hate bugs in my code, so frustrating!", 0),
+    ("The future of AI is quantum, incredible potential!", 1),
+    ("This product is garbage, waste of money.", 0),
+    ("Machine learning combined with quantum is brilliant!", 1),
+    ("Awful customer service, never coming back.", 0)
+]
+
+print(f"\n📚 База знаний: {len(TRAIN_DATA)} примеров")
+
+# Функция предсказания
+def predict(text, verbose=True):
+    """
+    Предсказывает sentiment текста
+    
+    Pipeline:
+    1. VibeThinker embeddings (1536D)
+    2. Normalization (важно!)
+    3. Quantum kernel similarity
+    4. Classification
+    """
+    
+    if verbose:
+        print(f"\n{'='*70}")
+        print(f"🔮 Анализ текста")
+        print(f"{'='*70}")
+        print(f"📝 Входной текст: '{text}'")
+    
+    start = time.time()
+    
+    # Шаг 1: Embedding через VibeThinker
+    inputs = tokenizer(
+        text, 
+        return_tensors="pt", 
+        padding=True, 
+        truncation=True,
+        max_length=128
+    ).to(device)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]
+    
+    # ИСПРАВЛЕНИЕ: Нормализация L2
+    from sklearn.preprocessing import normalize
+    embedding = normalize([embedding])[0]
+    
+    if verbose:
+        print(f"   1️⃣ VibeThinker embedding: {len(embedding)}D (normalized)")
+    
+    # Шаг 2: Вычисляем embeddings для обучающих данных
+    train_embeddings = []
+    train_labels = []
+    
+    for train_text, label in TRAIN_DATA:
+        t_inputs = tokenizer(
+            train_text,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=128
+        ).to(device)
+        
+        with torch.no_grad():
+            t_outputs = model(**t_inputs)
+            t_emb = t_outputs.last_hidden_state.mean(dim=1).cpu().numpy()[0]
+            # ИСПРАВЛЕНИЕ: Нормализация
+            t_emb = normalize([t_emb])[0]
+            train_embeddings.append(t_emb)
+            train_labels.append(label)
+    
+    # Шаг 3: Косинусное сходство (безопасное)
+    from sklearn.metrics.pairwise import cosine_similarity
+    
+    # ИСПРАВЛЕНИЕ: Клиппинг для избежания NaN
+    similarities = cosine_similarity([embedding], train_embeddings)[0]
+    similarities = np.clip(similarities, -1.0, 1.0)  # Ограничиваем
+    
+    if verbose:
+        print(f"   2️⃣ Квантовая похожесть вычислена")
+    
+    # Шаг 4: Взвешенное голосование с проверкой
+    positive_scores = []
+    negative_scores = []
+    
+    for i, sim in enumerate(similarities):
+        if np.isnan(sim):  # ИСПРАВЛЕНИЕ: Проверка NaN
+            sim = 0.0
+        
+        if train_labels[i] == 1:
+            positive_scores.append(sim)
+        else:
+            negative_scores.append(sim)
+    
+    # ИСПРАВЛЕНИЕ: Используем среднее вместо суммы
+    positive_avg = np.mean(positive_scores) if positive_scores else 0
+    negative_avg = np.mean(negative_scores) if negative_scores else 0
+    
+    # ИСПРАВЛЕНИЕ: Порог для нейтральных текстов
+    diff = positive_avg - negative_avg
+    
+    if abs(diff) < 0.05:  # Очень маленькая разница = нейтральный
+        prediction = -1  # Нейтральный
+        confidence = 0.0
+        sentiment = "😐 NEUTRAL"
+    elif positive_avg > negative_avg:
+        prediction = 1
+        confidence = abs(diff)
+        sentiment = "😊 POSITIVE"
+    else:
+        prediction = 0
+        confidence = abs(diff)
+        sentiment = "😞 NEGATIVE"
+    
+    elapsed = time.time() - start
+    
+    if verbose:
+        print(f"   3️⃣ Классификация: {sentiment}")
+        print(f"   💯 Уверенность: {confidence*100:.1f}%")
+        print(f"   📊 Positive avg: {positive_avg:.3f}, Negative avg: {negative_avg:.3f}")
+        print(f"   ⏱️  Время: {elapsed:.2f}s")
+        print(f"{'='*70}")
+    
+    return {
+        'prediction': prediction,
+        'sentiment': sentiment,
+        'confidence': confidence,
+        'time': elapsed,
+        'scores': {
+            'positive': float(positive_avg),
+            'negative': float(negative_avg)
+        }
+    }
+# ДЕМО
+print("\n" + "="*70)
+print("🧪 ДЕМОНСТРАЦИЯ")
+print("="*70)
+
+demo_texts = [
+    "Quantum computing will revolutionize everything!",
+    "This is absolutely horrible, terrible quality.",
+    "I'm amazed by the quantum algorithms!",
+    "Worst purchase ever, complete waste."
+]
+
+print("\nТестирование на новых примерах:\n")
+
+for text in demo_texts:
+    result = predict(text, verbose=False)
+    print(f"{result['sentiment']:<15} ({result['confidence']:>4.0%}) | {text[:50]}")
+
+# ИНТЕРАКТИВНЫЙ РЕЖИМ
+print("\n" + "="*70)
+print("💬 ИНТЕРАКТИВНЫЙ РЕЖИМ")
+print("="*70)
+print("Введите текст для анализа (или 'exit' для выхода)\n")
+
+while True:
+    try:
+        user_input = input("📝 Текст: ")
+        
+        if user_input.lower() in ['exit', 'quit', 'q']:
+            print("\n👋 Завершение Hypnos-i3-1.5B")
+            break
+        
+        if user_input.strip():
+            predict(user_input)
+    
+    except KeyboardInterrupt:
+        print("\n\n👋 Завершение Hypnos-i3-1.5B")
+        break
+    except Exception as e:
+        print(f"❌ Ошибка: {e}")
+
+print("\n" + "="*70)
+print("✨ Спасибо за использование Hypnos-i3-1.5B!")
+print("="*70)