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import gradio as gr
import os
import sys
import json
import argparse
from types import SimpleNamespace
from support.detect import run_detect
# Download weights on first run (for HF Spaces)
if os.environ.get("SPACE_ID"):
try:
from download_weights import download_all_weights
download_all_weights()
except Exception as e:
print(f"Warning: Could not download weights: {e}")
# Available detectors based on launcher.py
DETECTORS = ['R50_TF', 'R50_nodown', 'CLIP-D', 'P2G', 'NPR']
def predict(image_path, detector_name):
if not image_path:
return {"error": "Please upload an image."}
# Create a temporary output file path
output_path = "temp_result.json"
# Mock args object
args = SimpleNamespace(
image=image_path,
detector=detector_name,
config_dir='configs',
output=output_path,
weights='pretrained', # Use default/pretrained
device='cpu', # Force CPU
dry_run=False,
verbose=False
)
try:
# Run detection
# We need to capture stdout/stderr or just trust the function
# run_detect might raise FileNotFoundError if weights are missing
run_detect(args)
# Read results
if os.path.exists(output_path):
with open(output_path, 'r') as f:
result = json.load(f)
# Format output
prediction = result.get('prediction', 'Unknown')
confidence = result.get('confidence', 0.0)
elapsed_time = result.get('elapsed_time', 0.0)
return {
"Prediction": prediction,
"Confidence": f"{confidence:.4f}",
"Elapsed Time": f"{elapsed_time:.3f}s"
}
else:
return {"error": "No result file generated. Check console logs for details."}
except FileNotFoundError as e:
return {"error": str(e), "message": f"Please ensure you have downloaded the weights for {detector_name}."}
except Exception as e:
return {"error": str(e)}
finally:
# Cleanup
if os.path.exists(output_path):
os.remove(output_path)
# Create Gradio Interface
with gr.Blocks(title="Deepfake Detection", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🔍 Deepfake Detection Library")
gr.Markdown("""
Upload an image and select a detector to check if it's real or fake.
**Available Detectors:**
- **R50_TF**: ResNet-50 based detector
- **R50_nodown**: ResNet-50 without downsampling
- **CLIP-D**: CLIP-based detector
- **P2G**: Prompt2Guard detector
- **NPR**: Neural Posterior Regularization
""")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="filepath", label="Input Image", height=400)
detector_input = gr.Dropdown(
choices=DETECTORS,
value=DETECTORS[0],
label="Select Detector",
info="Choose which deepfake detection model to use"
)
submit_btn = gr.Button("🔍 Detect", variant="primary")
with gr.Column():
output_json = gr.JSON(label="Detection Results")
gr.Markdown("""
---
### About
This Space provides access to multiple state-of-the-art deepfake detection models.
All models are trained on StyleGAN2, StableDiffusionXL, FFHQ, and FORLAB datasets.
**Note:** First detection may be slower due to model loading.
""")
submit_btn.click(
fn=predict,
inputs=[image_input, detector_input],
outputs=output_json
)
if __name__ == "__main__":
# For HF Spaces, share is automatically enabled
demo.launch()
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