Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import spaces
 import torch
-
 torch.jit.script = lambda f: f  # Avoid script error in lambda
 
 from t2v_metrics import VQAScore, list_all_vqascore_models
@@ -9,9 +8,8 @@ from t2v_metrics import VQAScore, list_all_vqascore_models
 # Global model variable, but do not initialize or move to CUDA here
 model_pipe = VQAScore(model="clip-flant5-xl", device="cuda")  # our recommended scoring model
 
-@spaces.GPU
+@spaces.GPU(duration = 300)
 def generate(model_name, image, text):
-
     print(list_all_vqascore_models())  # Debug: List available models
     print("Image:", image)  # Debug: Print image path
     print("Text:", text)  # Debug: Print text input
@@ -25,10 +23,12 @@ def generate(model_name, image, text):
     
     return result  # Return the result
 
-iface = gr.Interface(
+demo = gr.Interface(
     fn=generate,  # function to call
     inputs=[gr.Dropdown(["clip-flant5-xl", "clip-flant5-xxl"], label="Model Name"), gr.Image(type="filepath"), gr.Textbox(label="Prompt")],  # define the types of inputs
     outputs="number",  # define the type of output
     title="VQAScore",  # title of the app
     description="This model evaluates the similarity between an image and a text prompt."
-).launch()
+)
+
+demo.launch()