Update app.py

app.py

@@ -1,63 +1,18 @@
 import gradio as gr
-import random
-import time
+from transformers import pipeline
 
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-# 假设这是队标的URL或本地路径
-team_logo_url = './team-logo.jpg'  # 替换为你的队标图片路径
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-speakers = ["subway"]
-project_intro = """
-## Automatic learning DEMO
-  
-**atomatic learning是一个训练方式的探索**    
-**通过采样视频素材来微调LLM和音色模型**    
-尽可能模仿出训练对象的说话语气和音色   
-     
-_训练结果来自嘉然4个视频,时长6小时_
-
-项目使用方案     
-- **语音**：bert-vits2 - **LLM**：Chat GLM-2    
-特别感谢：trochkera开源训练库    
-
-
-**注意**
-- 所有文本数据来自视频ASR提取，价值观与基本信息未经人工对齐。所以我们不能对输出结果进行保证    
-_（例如她不知道自己是女生还是男生，因为视频未提及。也可能出现LLM的“幻觉”现象）_
-- 所用的BERT-VITS2版本缘故，项目暂时只支持中文    
-
-项目即将开源，测试完成后率先打包至autodl社区-Code with GPU
-
-_我们诚挚地感谢您的支持与反馈。_   
-
-_feedback email:hhyxnh@gmail_
-
-
-"""
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column(scale=4):
-            gr.Image(value=team_logo_url, width=400)  
-            gr.Markdown(project_intro)  
-            
-        with gr.Column(scale=8):
-            chatbot = gr.Chatbot()
-            msg = gr.Textbox(placeholder="Type your message here...")
-            # people_speak = gr.Textbox(value="Human", label="Your name")
-            bot_speak = gr.Dropdown(
-                choices=speakers, value=speakers[0], label="Speaker"
-            )
-            with gr.Row():
-                submit = gr.Button("Chat!", variant="primary")
-                clear = gr.ClearButton([msg, chatbot])
-    def respond(people_speak, bot_speak, message, chat_history):
-        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
-        chat_history.append(( message, bot_speak + ": " + bot_message))
-        time.sleep(1)  
-        return "", chat_history  
-
-    submit.click(respond, inputs=[bot_speak, msg, chatbot], outputs=[msg, chatbot])
+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__":
-    demo.launch(share=True)
+    gradio_app.launch()