"""
VerseFlow Studio v5.0 - Hugging Face Gradio Demo
Author: XiaoZhe (Commercial Contact: janejulius119@gmail.com / WeChat: julius119)
"""

import gradio as gr
import time

def generate_ai_video(prompt, scene_type, quality_tier):
    time.sleep(1)
    return f"VerseFlow v5.0 任务已就绪! 模式: {scene_type}, 质量: {quality_tier}", "作者: XiaoZhe (julius119)"

with gr.Blocks(title="VerseFlow Studio v5.0 Demo") as demo:
    gr.Markdown("# 🎬 VerseFlow Studio v5.0 AI 视频操作系统\n### 开发者：XiaoZhe (XiaoZhe AIGC工作室)\n**联系**: janejulius119@gmail.com / WeChat: julius119")
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(label="Prompt")
            scene_dropdown = gr.Dropdown(["product_demo", "talking_head"], label="场景", value="product_demo")
            tier_dropdown = gr.Radio(["draft", "standard", "premium"], label="质量", value="standard")
            submit_btn = gr.Button("🚀 提交任务", variant="primary")
        with gr.Column():
            status_output = gr.Textbox(label="日志")
            author_notice = gr.Textbox(label="声明")

    submit_btn.click(fn=generate_ai_video, inputs=[prompt_input, scene_dropdown, tier_dropdown], outputs=[status_output, author_notice])

demo.queue().launch()
import gradio as gr
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

def predict(input_img):
    predictions = pipeline(input_img)
    return input_img, {p["label"]: p["score"] for p in predictions} 

gradio_app = gr.Interface(
    predict,
    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
    title="Hot Dog? Or Not?",
)

if __name__ == "__main__":
    gradio_app.launch()