Rename app1.py to app.py

app1.py → app.py

@@ -1,64 +1,20 @@
-# import gradio as gr
-# import spaces
-
-# # Initialize the model only once, outside of any function
-# # Ensure that CUDA initialization happens within the worker process
-# model_pipe = None
-
-# @spaces.GPU
-# def generate(model_name, image, text):
-#     global model_pipe
-#     import torch
-#     torch.jit.script = lambda f: f
-    
-#     from t2v_metrics import VQAScore, list_all_vqascore_models
-    
-#     if model_pipe is None:
-#         print("Initializing model...")
-#         model_pipe = VQAScore(model="clip-flant5-xl", device="cuda")  # our recommended scoring model
-#         # model_pipe.to("cuda")
-    
-#     print(list_all_vqascore_models())
-#     print("Image:", image)
-#     print("Text:", text)
-    
-#     print("Generating!")
-#     result = model_pipe(images=[image], texts=[text])
-#     return result
-
 import gradio as gr
 import spaces
-import os
+import torch
+
+torch.jit.script = lambda f: f  # Avoid script error in lambda
+
+from t2v_metrics import VQAScore, list_all_vqascore_models
 
 # Global model variable, but do not initialize or move to CUDA here
-model_pipe = None
+model_pipe = VQAScore(model="clip-flant5-xl", device="cpu")  # our recommended scoring model
 
 @spaces.GPU
 def generate(model_name, image, text):
-    global model_pipe
-    
-    # Debugging lines to trace CUDA initialization
-    import torch
-    
-    print(f"PID: {os.getpid()}")
-    print(f"Before import: CUDA available: {torch.cuda.is_available()}")
-    
-    torch.jit.script = lambda f: f  # Avoid script error in lambda
-    
-    from t2v_metrics import VQAScore, list_all_vqascore_models
-    
-    print(f"After import: CUDA available: {torch.cuda.is_available()}")
-    
-    # Worker Process: Perform all GPU-related initializations here
-    if model_pipe is None:
-        print("Initializing model in PID:", os.getpid())
-        model_pipe = VQAScore(model="clip-flant5-xl", device="cuda")  # our recommended scoring model
-        print(f"Model initialized: CUDA available: {torch.cuda.is_available()}")
-    
+
     print(list_all_vqascore_models())  # Debug: List available models
     print("Image:", image)  # Debug: Print image path
     print("Text:", text)  # Debug: Print text input
-    
     print("Generating!")
     # Wrap the model call in a try-except block to capture and debug CUDA errors
     try: