update app

app.py

@@ -1,13 +1,16 @@
-import gradio as gr
-from videosys import CogVideoConfig, VideoSysEngine
-from videosys.models.cogvideo.pipeline import CogVideoPABConfig
 import os
+
+os.environ["GRADIO_TEMP_DIR"] = os.path.join(os.getcwd(), ".tmp_outputs")
+
 import torch
 from openai import OpenAI
 from time import time
 import tempfile
 import uuid
 import logging
+import gradio as gr
+from videosys import CogVideoConfig, VideoSysEngine
+from videosys.models.cogvideo.pipeline import CogVideoPABConfig
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -88,7 +91,7 @@ def generate(engine, prompt, num_inference_steps=50, guidance_scale=6.0):
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
             temp_file.name
             unique_filename = f"{uuid.uuid4().hex}.mp4"
-            output_path = os.path.join(tempfile.gettempdir(), unique_filename)
+            output_path = os.path.join("./temp_outputs", unique_filename)
 
             engine.save_video(video, output_path)
         return output_path
@@ -122,19 +125,6 @@ body {
     min-width: 0;
 }
 
-
-button {
-    background-color: #4CAF50;
-    color: white;
-    border: none;
-    cursor: pointer;
-    transition: background-color 0.3s;
-}
-
-button:hover {
-    background-color: #45a049;
-}
-
 .video-output {
     width: 100%;
     max-width: 720px;
@@ -176,7 +166,7 @@ with gr.Blocks(css=css) as demo:
 
     with gr.Row():
         with gr.Column():
-            prompt = gr.Textbox(label="Prompt (Less than 200 Words)", value="a bear hunting for prey", lines=5)
+            prompt = gr.Textbox(label="Prompt (Less than 200 Words)", value="Sunset over the sea.", lines=5)
             with gr.Row():
                 gr.Markdown(
                     "✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one."
@@ -250,4 +240,5 @@ with gr.Blocks(css=css) as demo:
     enhance_button.click(enhance_prompt_func, inputs=[prompt], outputs=[prompt])
 
 if __name__ == "__main__":
+    demo.queue(max_size=10, default_concurrency_limit=1)
     demo.launch()