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from fastapi import FastAPI, HTTPException, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import torch
import torchvision.transforms as transforms
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from transformers import AutoFeatureExtractor
import timm
import numpy as np
import json
import base64
from io import BytesIO
import uvicorn
app = FastAPI(title="VerifAI GradCAM API", description="API pour la détection d'images IA avec GradCAM")
# Configuration CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class AIDetectionGradCAM:
def __init__(self):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.models = {}
self.feature_extractors = {}
self.target_layers = {}
# Initialiser les modèles
self._load_models()
def _load_models(self):
"""Charge les modèles pour la détection"""
try:
# Modèle Swin Transformer
model_name = "microsoft/swin-base-patch4-window7-224-in22k"
self.models['swin'] = timm.create_model('swin_base_patch4_window7_224', pretrained=True, num_classes=2)
self.feature_extractors['swin'] = AutoFeatureExtractor.from_pretrained(model_name)
# Définir les couches cibles pour GradCAM
self.target_layers['swin'] = [self.models['swin'].layers[-1].blocks[-1].norm1]
# Mettre en mode évaluation
for model in self.models.values():
model.eval()
model.to(self.device)
except Exception as e:
print(f"Erreur lors du chargement des modèles: {e}")
def _preprocess_image(self, image, model_type='swin'):
"""Prétraite l'image pour le modèle"""
if isinstance(image, str):
# Si c'est un chemin ou base64
if image.startswith('data:image'):
# Décoder base64
header, data = image.split(',', 1)
image_data = base64.b64decode(data)
image = Image.open(BytesIO(image_data))
else:
image = Image.open(image)
# Convertir en RGB si nécessaire
if image.mode != 'RGB':
image = image.convert('RGB')
# Redimensionner
image = image.resize((224, 224))
# Normalisation standard
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
tensor = transform(image).unsqueeze(0).to(self.device)
return tensor, np.array(image) / 255.0
def _generate_gradcam(self, image_tensor, rgb_img, model_type='swin'):
"""Génère la carte de saillance GradCAM"""
try:
model = self.models[model_type]
target_layers = self.target_layers[model_type]
# Créer l'objet GradCAM
cam = GradCAM(model=model, target_layers=target_layers)
# Générer la carte de saillance
grayscale_cam = cam(input_tensor=image_tensor, targets=None)
grayscale_cam = grayscale_cam[0, :]
# Superposer sur l'image originale
cam_image = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
return cam_image
except Exception as e:
print(f"Erreur GradCAM: {e}")
return rgb_img * 255
def predict_and_explain(self, image):
"""Prédiction avec explication GradCAM"""
try:
# Prétraitement
image_tensor, rgb_img = self._preprocess_image(image)
# Prédiction
with torch.no_grad():
outputs = self.models['swin'](image_tensor)
probabilities = torch.nn.functional.softmax(outputs, dim=1)
confidence = probabilities.max().item()
prediction = probabilities.argmax().item()
# Génération GradCAM
cam_image = self._generate_gradcam(image_tensor, rgb_img)
# Convertir l'image GradCAM en base64
pil_image = Image.fromarray(cam_image.astype(np.uint8))
buffer = BytesIO()
pil_image.save(buffer, format='PNG')
cam_base64 = base64.b64encode(buffer.getvalue()).decode()
# Résultats
result = {
'prediction': prediction,
'confidence': confidence,
'class_probabilities': {
'Real': probabilities[0][0].item(),
'AI-Generated': probabilities[0][1].item()
},
'cam_image': f"data:image/png;base64,{cam_base64}",
'status': 'success'
}
return result
except Exception as e:
return {'status': 'error', 'message': str(e)}
# Initialiser le détecteur
detector = AIDetectionGradCAM()
@app.get("/")
async def root():
return {"message": "VerifAI GradCAM API", "status": "running"}
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
"""Endpoint pour analyser une image"""
try:
# Lire l'image
image_data = await file.read()
image = Image.open(BytesIO(image_data))
# Analyser
result = detector.predict_and_explain(image)
return result
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/predict-base64")
async def predict_base64(data: dict):
"""Endpoint pour analyser une image en base64"""
try:
if 'image' not in data:
raise HTTPException(status_code=400, detail="Champ 'image' requis")
image_b64 = data['image']
# Analyser
result = detector.predict_and_explain(image_b64)
return result
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |