File size: 3,258 Bytes
a52b4d4 924d062 a52b4d4 924d062 a52b4d4 924d062 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import time
from concrete.ml.torch.compile import compile_torch_model
from custom_resnet import resnet18_custom # Assuming custom_resnet.py is in the same directory
# Load class names (FLIPPED as ['Fake', 'Real'])
class_names = ['Fake', 'Real'] # Fix the incorrect mapping
# Load the trained model
def load_model(model_path, device):
model = resnet18_custom(weights=None)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(class_names)) # Assuming 2 classes: Fake and Real
model.load_state_dict(torch.load(model_path, map_location=device))
model = model.to(device)
model.eval() # Set model to evaluation mode
return model
def load_secure_model(model):
print("Compiling secure model...")
secure_model = compile_torch_model(model.to("cpu"),
n_bits={"model_inputs": 4, "op_inputs": 3, "op_weights": 3, "model_outputs": 5},
rounding_threshold_bits={"n_bits": 7},
torch_inputset=torch.rand(10, 3, 224, 224))
return secure_model
# Image preprocessing (match with the transforms used during training)
data_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
# Prediction function
def predict(image, mode):
# Device configuration
device = torch.device(
"cuda:0" if torch.cuda.is_available() else
"mps" if torch.backends.mps.is_available() else
"cpu"
)
print(f"Device: {device}")
# Load model
model_path = 'models/deepfake_detection_model.pth'
model = load_model(model_path, device)
# Apply transformations to the input image
image = Image.open(image).convert('RGB')
image = data_transform(image).unsqueeze(0).to(device) # Add batch dimension
# Inference
with torch.no_grad():
start_time = time.time()
if mode == "Fast":
# Fast mode (less computation)
outputs = model(image)
elif mode == "Secure":
# Secure mode (e.g., running multiple times for higher confidence)
secure_model = load_secure_model(model)
detached_input = image.detach().numpy()
outputs = secure_model(detached_input, fhe="simulate")
print(outputs)
_, preds = torch.max(outputs, 1)
elapsed_time = time.time() - start_time
predicted_class = class_names[preds[0]]
return f"Predicted: {predicted_class}", f"Time taken: {elapsed_time:.2f} seconds"
# Gradio interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Image(type="filepath", label="Upload an Image"), # Update to gr.Image
gr.Radio(choices=["Fast", "Secure"], label="Inference Mode", value="Fast") # Update to gr.Radio
],
outputs=[
gr.Textbox(label="Prediction"), # Update to gr.Textbox
gr.Textbox(label="Time Taken") # Update to gr.Textbox
],
title="Deepfake Detection Model",
description="Upload an image and select the inference mode (Fast or Secure)."
)
if __name__ == "__main__":
iface.launch(share=True) |