Update app.py

app.py

@@ -17,10 +17,8 @@ from PIL import Image, ImageDraw, ImageFont
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from functools import lru_cache
 
-# 初始化环境
 load_dotenv()
 
-# 安全检测配置
 MODEL_URL = "TostAI/nsfw-text-detection-large"
 CLASS_NAMES = {
     0: "✅ SAFE",
@@ -28,11 +26,9 @@ CLASS_NAMES = {
     2: "🚫 UNSAFE"
 }
 
-# 加载模型
 tokenizer = AutoTokenizer.from_pretrained(MODEL_URL)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_URL)
 
-# 会话管理
 class SessionManager:
     _instances = {}
     _lock = threading.Lock()
@@ -56,7 +52,6 @@ class SessionManager:
             for k in expired:
                 del cls._instances[k]
 
-# 频率限制
 class RateLimiter:
     def __init__(self):
         self.clients = {}
@@ -79,11 +74,9 @@ class RateLimiter:
             self.clients[client_id]['count'] += 1
             return True
 
-# 初始化模块
 session_manager = SessionManager()
 rate_limiter = RateLimiter()
 
-# 图像处理函数
 def image_to_base64(file_path):
     try:
         with open(file_path, "rb") as f:
@@ -112,7 +105,7 @@ def create_error_image(message):
     img.save("error.jpg")
     return "error.jpg"
 
-# 核心生成逻辑
+
 @lru_cache(maxsize=100)
 def classify_prompt(prompt):
     inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
@@ -133,27 +126,23 @@ def generate_video(
     size,
     session_id
 ):
-    # 安全检查
+
     safety_level = classify_prompt(prompt)
     if safety_level != 0:
         error_img = create_error_image(CLASS_NAMES[safety_level])
         yield f"❌ Blocked: {CLASS_NAMES[safety_level]}", error_img
         return
 
-    # 频率检查
     if not rate_limiter.check(session_id):
         error_img = create_error_image("Hourly limit exceeded (20 requests)")
         yield "❌ 请求过于频繁，请稍后再试", error_img
         return
 
-    # 会话更新
     session = session_manager.get_session(session_id)
     session['last_active'] = time.time()
     session['count'] += 1
 
-    # API调用
     try:
-        # 准备请求
         api_key = os.getenv("WAVESPEED_API_KEY")
         if not api_key:
             raise ValueError("API key missing")
@@ -192,7 +181,6 @@ def generate_video(
         yield f"❌ 提交失败: {str(e)}", error_img
         return
 
-    # 轮询结果
     result_url = f"https://api.wavespeed.ai/api/v2/predictions/{request_id}/result"
     start_time = time.time()
     
@@ -224,13 +212,11 @@ def generate_video(
             yield f"❌ 生成失败: {str(e)}", error_img
             return
 
-# 后台清理线程
 def cleanup_task():
     while True:
         session_manager.cleanup_sessions()
         time.sleep(3600)
 
-# Gradio界面
 with gr.Blocks(
     theme=gr.themes.Soft(),
     css="""
@@ -267,7 +253,7 @@ with gr.Blocks(
                 guidance = gr.Slider(1, 20, value=7, label="Guidance Scale")
             with gr.Row():
                 seed = gr.Number(-1, label="Seed")
-                random_seed_btn = gr.Button("Random Seed", variant="secondary")
+                random_seed_btn = gr.Button("Random🎲Seed", variant="secondary")
             with gr.Row():
                 enable_safety = gr.Checkbox(label="🔒 Enable Safety Checker",value=True,interactive=True)
                 flow_shift = gr.Number(3, label="Flow Shift",interactive=False)
@@ -277,8 +263,8 @@ with gr.Blocks(
             status_output = gr.Textbox(label="System Status", interactive=False, lines=4)
             generate_btn = gr.Button("Generated", variant="primary")
             
-            with gr.Accordion("Generation History", open=False):
-                history_gallery = gr.Gallery(label="History", columns=3)
+ #           with gr.Accordion("Generation History", open=False):
+ #               history_gallery = gr.Gallery(label="History", columns=3)
                 
             with gr.Accordion("Safety Status", open=True):
                 gr.Markdown("""