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import gradio as gr
from PIL import Image
import os
import sys
current_dir = os.path.dirname(os.path.abspath(__file__))
defake_dir = os.path.join(current_dir, "defake")
if defake_dir not in sys.path:
sys.path.insert(0, defake_dir)
import defake.test_api
# 把 defake.test_api 里的 NeuralNet 类，“挂载”到当前的主模块 (__main__) 上
setattr(sys.modules['__main__'], 'NeuralNet', defake.test_api.NeuralNet)
from app_infer import run_infer_from_image
APP_API_TOKEN = os.environ.get("APP_API_TOKEN", "")
def detect_api(image: Image.Image, api_token: str = ""):
if APP_API_TOKEN and api_token != APP_API_TOKEN:
raise gr.Error("Invalid API token.")
result = run_infer_from_image(image)
return result
def detect_ui(image: Image.Image):
result = run_infer_from_image(image)
return result["fake_score"], str(result["is_fake"])
with gr.Blocks(title="CISPA Citizen DeFake") as demo:
gr.Markdown("# CISPA Citizen DeFake")
gr.Markdown("Upload an image to detect if it is fake.")
with gr.Row():
with gr.Column():
img_input = gr.Image(type="pil", label="Upload Image")
btn = gr.Button("Detect")
with gr.Column():
score_out = gr.Number(label="Fake score (1=fake)")
is_fake_out = gr.Textbox(label="Prediction (True=fake)")
btn.click(fn=detect_ui, inputs=img_input, outputs=[score_out, is_fake_out])
gr.Markdown("## API")
gr.Markdown(
"POST to `/run/predict` with form-data: `image`, `api_token`.\n"
"Returns JSON: `{fake_score: float, is_fake: bool, probs: [p0,p1], pred_class: int}`."
)
api = gr.Interface(
fn=detect_api,
inputs=[
gr.Image(type="pil", label="image"),
gr.Textbox(label="api_token", type="password"),
],
outputs="json",
# allow_flagging="never",
title="API Endpoint",
)
if __name__ == "__main__":
demo.launch()
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