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Debug script to test model loading and identify issues.
Run this to diagnose problems with model loading.
"""
import torch
import logging
from transformers import AutoModelForImageClassification, AutoImageProcessor
from huggingface_hub import list_repo_files, model_info
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
print("=" * 70)
print("INDIAN CURRENCY DETECTOR - MODEL LOADING DIAGNOSTICS")
print("=" * 70)
# 1. Check PyTorch
print("\n[1] PYTORCH CHECK")
print(f" PyTorch version: {torch.__version__}")
print(f" GPU available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f" GPU name: {torch.cuda.get_device_name(0)}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f" Using device: {device}")
# 2. Check HuggingFace connectivity
print("\n[2] HUGGINGFACE CONNECTIVITY CHECK")
MODEL_ID = "Shubhamm007/indian-currency-classifier"
print(f" Model ID: {MODEL_ID}")
try:
print(f" Checking model repository access...")
info = model_info(MODEL_ID)
print(f" ✓ Model found on HuggingFace!")
print(f" Model type: {info.model_name}")
print(f" Downloads: {info.downloads}")
print(f" Likes: {info.likes}")
except Exception as e:
print(f" ✗ Error accessing model: {e}")
print(f" Error type: {type(e).__name__}")
print(f"\n TROUBLESHOOTING:")
print(f" - Check your internet connection")
print(f" - Verify model ID is correct")
print(f" - Try: huggingface-cli login (if private model)")
print(f" - Check HuggingFace hub status at: https://status.huggingface.co/")
# 3. Try loading model
print("\n[3] MODEL LOADING TEST")
try:
print(f" Loading model: {MODEL_ID}")
model = AutoModelForImageClassification.from_pretrained(
MODEL_ID,
trust_remote_code=True
)
print(f" ✓ Model loaded successfully!")
print(f" Model class: {model.__class__.__name__}")
print(f" Number of parameters: {sum(p.numel() for p in model.parameters()):,}")
# Move to device
model.to(device)
model.eval()
print(f" ✓ Model moved to {device} and set to eval mode")
except Exception as e:
print(f" ✗ Error loading model: {e}")
print(f" Error type: {type(e).__name__}")
print(f"\n TROUBLESHOOTING:")
print(f" - Check model repository exists")
print(f" - Try clearing cache: rm -rf ~/.cache/huggingface/hub/")
print(f" - Try fallback model: google/vit-base-patch16-224")
# 4. Try loading image processor
print("\n[4] IMAGE PROCESSOR TEST")
try:
print(f" Loading image processor for: {MODEL_ID}")
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
print(f" ✓ Image processor loaded successfully!")
print(f" Processor class: {processor.__class__.__name__}")
except Exception as e:
print(f" ✗ Error loading processor: {e}")
print(f" Using fallback: Custom preprocessing")
# 5. Test inference
print("\n[5] INFERENCE TEST")
try:
from PIL import Image
import requests
from io import BytesIO
print(f" Downloading sample image...")
# Download a simple test image
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
response = requests.get(url, timeout=10)
image = Image.open(BytesIO(response.content))
print(f" Image loaded: {image.size}")
# Try prediction
from torchvision import transforms
# Preprocess
transform = transforms.Compose([
transforms.Resize((384, 384)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
image_tensor = transform(image).unsqueeze(0).to(device)
# Run inference
with torch.no_grad():
outputs = model(image_tensor)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
predicted_class = torch.argmax(probabilities, dim=-1).item()
confidence = probabilities[0][predicted_class].item() * 100
print(f" ✓ Inference successful!")
print(f" Predicted class: {predicted_class}")
print(f" Confidence: {confidence:.2f}%")
except Exception as e:
print(f" ✗ Error during inference test: {e}")
print(f" Error type: {type(e).__name__}")
print("\n" + "=" * 70)
print("DIAGNOSTICS COMPLETE")
print("=" * 70)
print("\nIf you see ✓ marks above, your setup is working correctly!")
print("If you see ✗ marks, check the troubleshooting steps for that section.")
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