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"""
Install dependencies for professional hallucination detector
Run this before using the new hallucination detection features
"""
import subprocess
import sys
def install_dependencies():
"""Install required packages for hallucination detection"""
print("=" * 60)
print("🔧 Installing Hallucination Detector Dependencies")
print("=" * 60)
packages = [
"sentence-transformers>=2.2.0",
"scikit-learn>=1.3.0",
"torch>=2.0.0",
"transformers>=4.30.0"
]
for package in packages:
print(f"\n📦 Installing {package}...")
try:
subprocess.check_call([
sys.executable, "-m", "pip", "install", package
])
print(f"✅ {package} installed successfully")
except subprocess.CalledProcessError as e:
print(f"❌ Failed to install {package}: {e}")
return False
print("\n" + "=" * 60)
print("✅ All dependencies installed successfully!")
print("=" * 60)
return True
def download_models():
"""Pre-download models to cache"""
print("\n" + "=" * 60)
print("🔧 Downloading Models (this may take a few minutes)...")
print("=" * 60)
try:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Download Vectara model
print("\n📥 Downloading Vectara HHEM model...")
try:
AutoTokenizer.from_pretrained("vectara/hallucination_evaluation_model")
AutoModelForSequenceClassification.from_pretrained("vectara/hallucination_evaluation_model")
print("✅ Vectara model downloaded")
except Exception as e:
print(f"⚠️ Vectara model download failed: {e}")
# Download NLI model
print("\n📥 Downloading DeBERTa NLI model...")
try:
from transformers import pipeline
pipeline("text-classification", model="microsoft/deberta-large-mnli")
print("✅ NLI model downloaded")
except Exception as e:
print(f"⚠️ NLI model download failed: {e}")
print("\n" + "=" * 60)
print("✅ Models downloaded successfully!")
print("=" * 60)
except ImportError as e:
print(f"❌ Cannot download models: {e}")
print("Please install transformers first")
return False
return True
def test_installation():
"""Test if installation works"""
print("\n" + "=" * 60)
print("🧪 Testing Installation...")
print("=" * 60)
try:
from hallucination_detector import HybridHallucinationDetector
print("\n📝 Creating test detector...")
detector = HybridHallucinationDetector(use_vectara=True, use_nli=True)
print("\n📝 Running test detection...")
test_doc = "Python is a programming language created by Guido van Rossum in 1991."
test_gen = "Python was created by Guido van Rossum."
result = detector.detect(test_gen, test_doc)
print(f"\n✅ Test result: {result}")
print("\n" + "=" * 60)
print("✅ Installation test passed!")
print("=" * 60)
return True
except Exception as e:
print(f"\n❌ Installation test failed: {e}")
print("\nPlease check the error messages above.")
return False
if __name__ == "__main__":
print("\n🚀 Starting installation...\n")
# Step 1: Install dependencies
if not install_dependencies():
print("\n❌ Installation failed at dependency stage")
sys.exit(1)
# Step 2: Download models
if not download_models():
print("\n⚠️ Model download had issues, but you can continue")
# Step 3: Test installation
if test_installation():
print("\n" + "=" * 60)
print("🎉 Installation Complete!")
print("=" * 60)
print("\nYou can now use the professional hallucination detector.")
print("\nTo test it, run:")
print(" python test_hallucination_detector.py")
print("\n" + "=" * 60)
else:
print("\n❌ Installation completed with errors")
print("The system will fallback to LLM-based detection")
sys.exit(1)
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