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# backend/app/inference.py
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
from pathlib import Path
from .model import create_detection_model
class InferenceEngine:
def __init__(self, model_path: str):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = None
self.model_path = model_path
# Class mapping
self.class_map = {
0: 'glioma',
1: 'meningioma',
2: 'pituitary',
3: 'notumor'
}
# Preprocessing transforms
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
self._load_model()
def _load_model(self):
"""Load the PyTorch model"""
try:
self.model = create_detection_model(num_classes=4)
self.model.load_state_dict(
torch.load(self.model_path, map_location=self.device)
)
self.model.to(self.device)
self.model.eval()
print(f"✅ Model loaded successfully on {self.device}")
except Exception as e:
print(f"❌ Error loading model: {e}")
raise
def predict(self, image_path: str) -> dict:
"""
Run inference on an image
Args:
image_path: Path to the image file
Returns:
Dictionary with prediction results
"""
try:
# Load and preprocess image
image = Image.open(image_path).convert('RGB')
input_tensor = self.transform(image).unsqueeze(0).to(self.device)
# Run inference
with torch.no_grad():
output = self.model(input_tensor)
# Get probabilities and prediction
probabilities = F.softmax(output, dim=1)
confidence, predicted_index = torch.max(probabilities, 1)
predicted_class = self.class_map[predicted_index.item()]
return {
"success": True,
"predicted_class": predicted_class,
"confidence": float(confidence.item()),
"all_probabilities": {
self.class_map[i]: float(probabilities[0][i].item())
for i in range(len(self.class_map))
}
}
except Exception as e:
return {
"success": False,
"error": str(e)
} |