update

app.py

@@ -1,11 +1,19 @@
 import gradio as gr
 import torch
+import uuid
+import time
+import threading
+from datetime import datetime
 from transformers.models.bert import BertForSequenceClassification, BertTokenizer
 from transformers.models.roberta import RobertaForSequenceClassification, RobertaTokenizer
 
+# 任务状态管理
+task_results = {}
+task_lock = threading.Lock()
+
 # torch.set_grad_enabled(False)
 print('Loading Models from HuggingFace...')
-# load by default
+# load V3 models only
 name_en = "yuchuantian/AIGC_detector_env3"
 model_en = RobertaForSequenceClassification.from_pretrained(name_en)
 tokenizer_en = RobertaTokenizer.from_pretrained(name_en)
@@ -13,9 +21,6 @@ tokenizer_en = RobertaTokenizer.from_pretrained(name_en)
 name_en3 = "yuchuantian/AIGC_detector_env3short"
 model_en3 = RobertaForSequenceClassification.from_pretrained(name_en3)
 
-name_en5 = "yuchuantian/AIGC_detector_env2"
-model_en5 = RobertaForSequenceClassification.from_pretrained(name_en5)
-
 name_zh = "yuchuantian/AIGC_detector_zhv3"
 model_zh = BertForSequenceClassification.from_pretrained(name_zh)
 tokenizer_zh = BertTokenizer.from_pretrained(name_zh)
@@ -23,9 +28,6 @@ tokenizer_zh = BertTokenizer.from_pretrained(name_zh)
 name_zh4 = "yuchuantian/AIGC_detector_zhv3short"
 model_zh4 = BertForSequenceClassification.from_pretrained(name_zh4)
 
-name_zh6 = "yuchuantian/AIGC_detector_zhv2"
-model_zh6 = BertForSequenceClassification.from_pretrained(name_zh6)
-
 print('Model Loading from HuggingFace Complete!')
 
 
@@ -48,12 +50,6 @@ def predict_en3(text):
     res = predict_func(text, tokenizer_en, model_en3)
     return id2label[res['label']], res['score']
 
-def predict_en5(text):
-    id2label = ['Human', 'AI']
-    res = predict_func(text, tokenizer_en, model_en5)
-    return id2label[res['label']], res['score']
-
-
 def predict_zh(text):
     id2label = ['人类', 'AI']
     res = predict_func(text, tokenizer_zh, model_zh)
@@ -64,14 +60,80 @@ def predict_zh4(text):
     res = predict_func(text, tokenizer_zh, model_zh4)
     return id2label[res['label']], res['score']
 
-def predict_zh6(text):
-    id2label = ['人类', 'AI']
-    res = predict_func(text, tokenizer_zh, model_zh6)
-    return id2label[res['label']], res['score']
 
+# 异步任务处理函数
+def process_task_async(task_id, text, model_type):
+    """异步处理任务"""
+    try:
+        with task_lock:
+            task_results[task_id] = {
+                "status": "processing",
+                "created_at": datetime.now().isoformat(),
+                "result": None,
+                "error": None
+            }
+        
+        # 根据模型类型选择对应的预测函数
+        if model_type == "en_v3":
+            result = predict_en(text)
+        elif model_type == "en_v3_short":
+            result = predict_en3(text)
+        elif model_type == "zh_v3":
+            result = predict_zh(text)
+        elif model_type == "zh_v3_short":
+            result = predict_zh4(text)
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+        
+        with task_lock:
+            task_results[task_id]["status"] = "completed"
+            task_results[task_id]["result"] = {
+                "label": result[0],
+                "score": float(result[1])
+            }
+            task_results[task_id]["completed_at"] = datetime.now().isoformat()
+            
+    except Exception as e:
+        with task_lock:
+            task_results[task_id]["status"] = "error"
+            task_results[task_id]["error"] = str(e)
+            task_results[task_id]["completed_at"] = datetime.now().isoformat()
+
+
+def submit_task(text, model_type):
+    """提交异步任务"""
+    task_id = str(uuid.uuid4())
+    
+    # 启动后台线程处理任务
+    thread = threading.Thread(
+        target=process_task_async,
+        args=(task_id, text, model_type)
+    )
+    thread.daemon = True
+    thread.start()
+    
+    return {
+        "task_id": task_id,
+        "status": "submitted",
+        "message": "Task submitted successfully"
+    }
+
+
+def query_task_result(task_id):
+    """查询任务结果"""
+    with task_lock:
+        if task_id not in task_results:
+            return {
+                "error": "Task not found",
+                "task_id": task_id
+            }
+        
+        task_info = task_results[task_id].copy()
+        task_info["task_id"] = task_id
+        return task_info
 
-print(predict_en('Peking University is one of the best universities in the world.'))
 
+print(predict_en('Peking University is one of the best universities in the world.'))
 print(predict_zh('很高兴认识你！'))
 
 
@@ -93,13 +155,48 @@ with gr.Blocks() as demo:
                 [Paper Link 论文链接](https://arxiv.org/abs/2305.18149)
 
                 The loadable versions are as follows 可加载的检测器版本如下：
-                English: [En-v3](https://huggingface.co/yuchuantian/AIGC_detector_env3) / [En-v3-short](https://huggingface.co/yuchuantian/AIGC_detector_env3short) / [En_v2](https://huggingface.co/yuchuantian/AIGC_detector_env2)
-                Chinese:  [Zh-v3](https://huggingface.co/yuchuantian/AIGC_detector_zhv3) /  [Zh-v3-short](https://huggingface.co/yuchuantian/AIGC_detector_zhv3short) / [Zh_v2](https://huggingface.co/yuchuantian/AIGC_detector_zhv2)
+                English: [En-v3](https://huggingface.co/yuchuantian/AIGC_detector_env3) / [En-v3-short](https://huggingface.co/yuchuantian/AIGC_detector_env3short)
+                Chinese:  [Zh-v3](https://huggingface.co/yuchuantian/AIGC_detector_zhv3) /  [Zh-v3-short](https://huggingface.co/yuchuantian/AIGC_detector_zhv3short)
 
                 Acknowledgement 致谢
                 We sincerely thank [Hello-SimpleAI](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-single) for their code.
                 """)
 
+    with gr.Tab("异步API接口"):
+        gr.Markdown("""
+                    ## 异步API接口使用说明
+                    
+                    ### 1. 提交任务接口
+                    - 函数: `submit_task(text, model_type)`
+                    - 参数: 
+                      - text: 要检测的文本
+                      - model_type: 模型类型 (en_v3, en_v3_short, zh_v3, zh_v3_short)
+                    - 返回: task_id 和状态信息
+                    
+                    ### 2. 查询结果接口  
+                    - 函数: `query_task_result(task_id)`
+                    - 参数: task_id (任务ID)
+                    - 返回: 任务状态和结果
+                    """)
+        
+        with gr.Row():
+            with gr.Column():
+                api_text = gr.Textbox(lines=5, label='文本内容', value="北京大学建立于1898年7月3日")
+                api_model = gr.Dropdown(
+                    choices=["en_v3", "en_v3_short", "zh_v3", "zh_v3_short"],
+                    value="zh_v3",
+                    label="模型类型"
+                )
+                submit_btn = gr.Button("📤 提交任务")
+                
+            with gr.Column():
+                task_id_input = gr.Textbox(label="任务ID", placeholder="输入任务ID查询结果")
+                query_btn = gr.Button("🔍 查询结果")
+        
+        with gr.Row():
+            submit_output = gr.JSON(label="提交结果")
+            query_output = gr.JSON(label="查询结果")
+
     with gr.Tab("中文-V3"):
         gr.Markdown("""
                     注意: 本检测器提供的结果仅供参考，应谨慎作为事实依据。
@@ -109,7 +206,6 @@ with gr.Blocks() as demo:
         label2 = gr.Textbox(lines=1, label='预测结果')
         score2 = gr.Textbox(lines=1, label='模型概率')
 
-    
     with gr.Tab("中文-V3-短文本"):
         gr.Markdown("""
                     注意: 本检测器提供的结果仅供参考，应谨慎作为事实依据。
@@ -119,16 +215,6 @@ with gr.Blocks() as demo:
         label4 = gr.Textbox(lines=1, label='预测结果')
         score4 = gr.Textbox(lines=1, label='模型概率')
 
-    with gr.Tab("中文-V2"):
-        gr.Markdown("""
-                    注意: 本检测器提供的结果仅供参考，应谨慎作为事实依据。
-                    """)
-        t6 = gr.Textbox(lines=5, label='文本',value="北京大学建立于1898年7月3日，初名京师大学堂，辛亥革命后于1912年改为北京大学。1938年更名为国立西南联合大学。1946年10月在北平复员。1952年成为以文理学科为主的综合性大学。")
-        button6 = gr.Button("🚀 检测!")
-        label6 = gr.Textbox(lines=1, label='预测结果')
-        score6 = gr.Textbox(lines=1, label='模型概率')
-
-
     with gr.Tab("English-V3"):
         gr.Markdown("""
                     Note: The results are for reference only; they could not be used as factual evidence.
@@ -147,25 +233,20 @@ with gr.Blocks() as demo:
         label3 = gr.Textbox(lines=1, label='Predicted Label')
         score3 = gr.Textbox(lines=1, label='Probability')
 
-    with gr.Tab("English-V2"):
-        gr.Markdown("""
-                    Note: The results are for reference only; they could not be used as factual evidence.
-                    """)
-        t5 = gr.Textbox(lines=5, label='Text',value="Originated as the Imperial University of Peking in 1898, Peking University was China's first national comprehensive university and the supreme education authority at the time. Since the founding of the People's Republic of China in 1949, it has developed into a comprehensive university with fundamental education and research in both humanities and science. The reform and opening-up of China in 1978 has ushered in a new era for the University unseen in history.")
-        button5 = gr.Button("🚀 Predict!")
-        label5 = gr.Textbox(lines=1, label='Predicted Label')
-        score5 = gr.Textbox(lines=1, label='Probability')
-
+    # 绑定事件
+    submit_btn.click(submit_task, inputs=[api_text, api_model], outputs=[submit_output])
+    query_btn.click(query_task_result, inputs=[task_id_input], outputs=[query_output])
+    
     button1.click(predict_en, inputs=[t1], outputs=[label1,score1])
     button2.click(predict_zh, inputs=[t2], outputs=[label2,score2])
     button3.click(predict_en3, inputs=[t3], outputs=[label3,score3])
     button4.click(predict_zh4, inputs=[t4], outputs=[label4,score4])
-    button5.click(predict_en5, inputs=[t5], outputs=[label5,score5])
-    button6.click(predict_zh6, inputs=[t6], outputs=[label6,score6])
 
     # Page Count
     gr.Markdown("""
                 <center><a href='https://clustrmaps.com/site/1bsdc'  title='Visit tracker'><img src='//clustrmaps.com/map_v2.png?cl=080808&w=a&t=tt&d=NXQdnwxvIm27veMbB5F7oHNID09nhSvkBRZ_Aji9eIA&co=ffffff&ct=808080'/></a></center>
                 """)
 
+# 启用队列模式以支持真正的异步处理
+demo.queue()
 demo.launch()