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()