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import gradio as gr
from run import get_model, detect_video
model = get_model()
def greet(video):
print(video, type(video))
generated_pred = detect_video(video_path=video, model=model)
# generated_pred = 0.93443
# context_pred = 0.9164814352989197
# context_pred = 0.3335219025611877
context_pred = 0.7490718364715576
generated_output = f"Fake: {generated_pred*100:.2f}%" if generated_pred > 0.5 else f"Real: {(1-generated_pred)*100:.2f}%"
context_output = f"Fake: {context_pred*100:.2f}%" if context_pred > 0.5 else f"Real: {(1-context_pred)*100:.2f}%"
print(generated_output, '\n', context_output)
return generated_output, context_output
with gr.Blocks() as demo:
gr.Markdown("# Fake Video Detector")
with gr.Tabs():
with gr.TabItem("Video Detect"):
with gr.Column():
video_input = gr.Video(height=330)
video_button = gr.Button("detect")
detect_output = gr.Textbox(label="AI detect result")
context_output = gr.Textbox(label="Context detect result")
video_button.click(greet, inputs=video_input, outputs=[detect_output, context_output])
demo.launch() |