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"""
Test script for local model inference
Run this to verify your setup before deploying to HuggingFace Spaces
"""
import os
import sys
# Set environment for local model
os.environ["USE_HF_API"] = "False"
os.environ["USE_LMSTUDIO"] = "False"
os.environ["DEBUG_MODE"] = "True"
os.environ["LLM_BACKEND"] = "local"
os.environ["LLM_TEMPERATURE"] = "0.7"
print("="*80)
print("🧪 Testing Local Model Inference")
print("="*80)
# Test imports
print("\n1️⃣ Testing imports...")
try:
import torch
print(f" ✅ PyTorch {torch.__version__}")
print(f" 🔧 CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f" 🎮 GPU: {torch.cuda.get_device_name(0)}")
except ImportError as e:
print(f" ❌ PyTorch not installed: {e}")
print(" 📦 Install: pip install torch")
sys.exit(1)
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
print(f" ✅ Transformers installed")
except ImportError as e:
print(f" ❌ Transformers not installed: {e}")
print(" 📦 Install: pip install transformers accelerate")
sys.exit(1)
# Test LLM function
print("\n2️⃣ Testing LLM function...")
try:
from llm import query_llm
print(" ✅ LLM module imported")
except ImportError as e:
print(f" ❌ Failed to import llm module: {e}")
sys.exit(1)
# Test simple query
print("\n3️⃣ Testing simple query (this will download the model on first run)...")
print(" ⏳ This may take 2-5 minutes for first-time model download...\n")
test_prompt = """You are a medical transcript analyzer.
Analyze this brief interview segment:
Interviewer: How do you treat moderate acne?
Doctor: I typically start with topical retinoids and benzoyl peroxide. For more severe cases, I prescribe oral antibiotics like doxycycline 100mg daily.
Provide a brief summary and extract structured data in JSON format:
{
"diagnoses": ["list of conditions mentioned"],
"prescriptions": ["list of medications with dosages"],
"treatment_rationale": ["list of treatment approaches"]
}
"""
try:
response, structured_data = query_llm(
chunk=test_prompt,
user_context="Extract medical information from this dermatology interview",
interviewee_type="HCP",
extract_structured=True,
timeout=180
)
print("\n" + "="*80)
print("📊 RESULTS")
print("="*80)
print(f"\n📝 Response Text ({len(response)} chars):")
print("-" * 80)
print(response)
print(f"\n🔍 Structured Data ({len(structured_data)} fields):")
print("-" * 80)
import json
print(json.dumps(structured_data, indent=2))
# Validate results
print("\n" + "="*80)
print("✅ VALIDATION")
print("="*80)
if len(response) < 50:
print("⚠️ Warning: Response is very short")
else:
print(f"✅ Response length OK ({len(response)} chars)")
if not structured_data:
print("❌ No structured data extracted - check JSON parsing!")
elif len(structured_data) == 0:
print("⚠️ Structured data is empty")
else:
print(f"✅ Structured data extracted ({len(structured_data)} fields)")
for key, values in structured_data.items():
if values:
print(f" • {key}: {len(values)} items")
if "[Error]" in response:
print("❌ Response contains error message!")
else:
print("✅ No error messages in response")
print("\n" + "="*80)
print("🎉 TEST COMPLETE!")
print("="*80)
print("\nYour system is ready for HuggingFace Spaces deployment.")
print("\n📖 See HUGGINGFACE_SPACES_SETUP.md for deployment instructions.")
except Exception as e:
print("\n" + "="*80)
print("❌ TEST FAILED")
print("="*80)
print(f"\nError: {e}")
import traceback
print("\nFull traceback:")
print(traceback.format_exc())
print("\n🔧 Troubleshooting:")
print("1. Make sure GPU is available (or set device_map='cpu')")
print("2. Check if you have enough RAM/VRAM (~8GB needed)")
print("3. Try a smaller model: LOCAL_MODEL=TinyLlama/TinyLlama-1.1B-Chat-v1.0")
print("4. Check internet connection for model download")
sys.exit(1)
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