Upload backend/hue_portal/hue-portal-backendDocker/check_models.py with huggingface_hub

backend/hue_portal/hue-portal-backendDocker/check_models.py

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+#!/usr/bin/env python3
+"""
+Script to check which models are currently being used on Hugging Face Space.
+"""
+import os
+import sys
+
+# Add backend to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend'))
+
+def check_embedding_model():
+    """Check embedding model configuration."""
+    from hue_portal.core.embeddings import (
+        DEFAULT_MODEL_NAME,
+        FALLBACK_MODEL_NAME,
+        AVAILABLE_MODELS,
+        get_embedding_model
+    )
+    
+    print("=" * 60)
+    print("🔍 EMBEDDING MODEL CONFIGURATION")
+    print("=" * 60)
+    
+    # Check environment variable
+    env_model = os.environ.get("EMBEDDING_MODEL")
+    if env_model:
+        print(f"📌 EMBEDDING_MODEL env var: {env_model}")
+    else:
+        print(f"📌 EMBEDDING_MODEL env var: Not set (using default)")
+    
+    print(f"📌 Default model: {DEFAULT_MODEL_NAME}")
+    print(f"📌 Fallback model: {FALLBACK_MODEL_NAME}")
+    
+    # Try to load model
+    print("\n🔄 Attempting to load embedding model...")
+    try:
+        model = get_embedding_model()
+        if model:
+            # Get dimension
+            test_embedding = model.encode("test", show_progress_bar=False)
+            dim = len(test_embedding)
+            print(f"✅ Model loaded successfully!")
+            print(f"   Model name: {DEFAULT_MODEL_NAME}")
+            print(f"   Dimension: {dim}")
+            print(f"   Status: ✅ GOOD")
+            
+            # Evaluate
+            if dim >= 768:
+                print(f"   Quality: ⭐⭐⭐⭐ High quality (768+ dim)")
+            elif dim >= 384:
+                print(f"   Quality: ⭐⭐⭐ Good quality (384 dim)")
+            else:
+                print(f"   Quality: ⭐⭐ Basic quality")
+        else:
+            print("❌ Failed to load model")
+    except Exception as e:
+        print(f"❌ Error: {e}")
+    
+    print("\n📊 Available models:")
+    for key, value in AVAILABLE_MODELS.items():
+        marker = "⭐" if value == DEFAULT_MODEL_NAME else "  "
+        print(f"   {marker} {key}: {value}")
+
+
+def check_llm_model():
+    """Check LLM model configuration."""
+    from hue_portal.chatbot.llm_integration import (
+        LLM_PROVIDER,
+        LLM_PROVIDER_NONE,
+        LLM_PROVIDER_OPENAI,
+        LLM_PROVIDER_ANTHROPIC,
+        LLM_PROVIDER_OLLAMA,
+        LLM_PROVIDER_HUGGINGFACE,
+        LLM_PROVIDER_LOCAL,
+        get_llm_generator
+    )
+    
+    print("\n" + "=" * 60)
+    print("🔍 LLM MODEL CONFIGURATION")
+    print("=" * 60)
+    
+    print(f"📌 LLM_PROVIDER: {LLM_PROVIDER}")
+    
+    if LLM_PROVIDER == LLM_PROVIDER_NONE:
+        print("⚠️  No LLM provider configured!")
+        print("   Status: ❌ NOT USING LLM (template-based only)")
+        print("   Quality: ⭐⭐ Basic (no LLM generation)")
+        print("\n💡 To enable LLM, set LLM_PROVIDER to one of:")
+        print("   - ollama (for local Qwen)")
+        print("   - openai (for GPT)")
+        print("   - anthropic (for Claude)")
+        print("   - huggingface (for HF Inference API)")
+        print("   - local (for local Transformers)")
+    elif LLM_PROVIDER == LLM_PROVIDER_OPENAI:
+        model = os.environ.get("OPENAI_MODEL", "gpt-3.5-turbo")
+        print(f"✅ Using OpenAI")
+        print(f"   Model: {model}")
+        print(f"   Status: ✅ GOOD")
+        print(f"   Quality: ⭐⭐⭐⭐⭐ Excellent")
+    elif LLM_PROVIDER == LLM_PROVIDER_ANTHROPIC:
+        model = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022")
+        print(f"✅ Using Anthropic Claude")
+        print(f"   Model: {model}")
+        print(f"   Status: ✅ EXCELLENT")
+        print(f"   Quality: ⭐⭐⭐⭐⭐ Best for Vietnamese")
+    elif LLM_PROVIDER == LLM_PROVIDER_OLLAMA:
+        model = os.environ.get("OLLAMA_MODEL", "qwen2.5:7b")
+        base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
+        print(f"✅ Using Ollama (local)")
+        print(f"   Model: {model}")
+        print(f"   Base URL: {base_url}")
+        print(f"   Status: ✅ GOOD (if Ollama running)")
+        print(f"   Quality: ⭐⭐⭐⭐ Very good for Vietnamese")
+    elif LLM_PROVIDER == LLM_PROVIDER_HUGGINGFACE:
+        model = os.environ.get("HF_MODEL", "Qwen/Qwen2.5-7B-Instruct")
+        print(f"✅ Using Hugging Face Inference API")
+        print(f"   Model: {model}")
+        print(f"   Status: ✅ GOOD")
+        print(f"   Quality: ⭐⭐⭐⭐ Good for Vietnamese")
+    elif LLM_PROVIDER == LLM_PROVIDER_LOCAL:
+        model = os.environ.get("LOCAL_MODEL_PATH", "Qwen/Qwen2.5-1.5B-Instruct")
+        device = os.environ.get("LOCAL_MODEL_DEVICE", "auto")
+        print(f"✅ Using Local Transformers")
+        print(f"   Model: {model}")
+        print(f"   Device: {device}")
+        print(f"   Status: ✅ GOOD (if model loaded)")
+        print(f"   Quality: ⭐⭐⭐⭐ Good for Vietnamese")
+    
+    # Try to get LLM generator
+    print("\n🔄 Checking LLM availability...")
+    try:
+        llm = get_llm_generator()
+        if llm and llm.is_available():
+            print("✅ LLM is available and ready!")
+        else:
+            print("⚠️  LLM is not available")
+    except Exception as e:
+        print(f"❌ Error checking LLM: {e}")
+
+
+def main():
+    """Main function."""
+    print("\n" + "=" * 60)
+    print("📊 MODEL STATUS CHECK")
+    print("=" * 60)
+    print()
+    
+    check_embedding_model()
+    check_llm_model()
+    
+    print("\n" + "=" * 60)
+    print("📋 SUMMARY")
+    print("=" * 60)
+    
+    # Embedding summary
+    from hue_portal.core.embeddings import DEFAULT_MODEL_NAME
+    embedding_model = os.environ.get("EMBEDDING_MODEL", DEFAULT_MODEL_NAME)
+    print(f"Embedding: {embedding_model}")
+    
+    # LLM summary
+    from hue_portal.chatbot.llm_integration import LLM_PROVIDER, LLM_PROVIDER_NONE
+    if LLM_PROVIDER == LLM_PROVIDER_NONE:
+        print("LLM: None (template-based only)")
+    else:
+        print(f"LLM: {LLM_PROVIDER}")
+    
+    print("\n💡 Recommendations:")
+    print("   - Embedding: multilingual-mpnet (current) is good ✅")
+    print("   - LLM: Consider adding Qwen 2.5 for better answers")
+    print()
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+