Update app.py

app.py

@@ -60,79 +60,6 @@ def rank_images(model_name, images, text):
     
     return ranked_images
     
-# import spaces
-# import gradio as gr
-# import torch
-# torch.jit.script = lambda f: f  # Avoid script error in lambda
-# from t2v_metrics import VQAScore, list_all_vqascore_models
-
-
-# def update_model(model_name):
-#     return VQAScore(model=model_name, device="cuda")
-
-# # Use global variables for model pipe and current model name
-# global model_pipe, cur_model_name
-# cur_model_name = "clip-flant5-xl"
-# model_pipe = update_model(cur_model_name)
-
-
-# # Ensure GPU context manager is imported correctly (assuming spaces is a module you have)
-# #try:
-#     #from spaces import GPU # i believe this is wrong, spaces package does not have "GPU"
-# #except ImportError:
-#     # GPU = lambda duration: (lambda f: f)  # Dummy decorator if spaces.GPU is not available
-
-# if torch.cuda.is_available():
-#     model_pipe.device = "cuda"
-# else:
-#     print("CUDA is not available")
-    
-# @spaces.GPU # a duration lower than 60 does not work, leave as is.
-# def generate(model_name, image, text):
-#     global model_pipe, cur_model_name
-    
-#     if model_name != cur_model_name:
-#         cur_model_name = model_name  # Update the current model name
-#         model_pipe = update_model(model_name)
-    
-#     print("Image:", image)  # Debug: Print image path
-#     print("Text:", text)  # Debug: Print text input
-#     print("Using model:", model_name)
-    
-#     try:
-#         result = model_pipe(images=[image], texts=[text]).cpu()[0][0].item()  # Perform the model inference
-#         print("Result:", result)
-#     except RuntimeError as e:
-#         print(f"RuntimeError during model inference: {e}")
-#         raise e
-    
-#     return result
-
-
-# def rank_images(model_name, images, text):
-#     global model_pipe, cur_model_name
-    
-#     if model_name != cur_model_name:
-#         cur_model_name = model_name  # Update the current model name
-#         model_pipe = update_model(model_name)
-
-#     images = [image_tuple[0] for image_tuple in images]
-#     print("Images:", images)  # Debug: Print image paths
-#     print("Text:", text)  # Debug: Print text input
-#     print("Using model:", model_name)
-    
-#     try:
-#         results = model_pipe(images=images, texts=[text]).cpu()[:, 0].tolist()  # Perform the model inference on all images
-#         print("Initial results: should be imgs x texts", results)
-#         ranked_results = sorted(zip(images, results), key=lambda x: x[1], reverse=True)  # Rank results
-#         ranked_images = [(img, f"Rank: {rank + 1} - Score: {score:.2f}") for rank, (img, score) in enumerate(ranked_results)]  # Pair images with their scores and rank
-#         print("Ranked Results:", ranked_results)
-#     except RuntimeError as e:
-#         print(f"RuntimeError during model inference: {e}")
-#         raise e
-    
-#     return ranked_images
-
 
 ### EXAMPLES ###
 example_imgs = ["0_imgs/DALLE3.png", 
@@ -141,65 +68,12 @@ example_imgs = ["0_imgs/DALLE3.png",
                 "0_imgs/SDXL.jpg"]
 example_prompt0 = "Two dogs of different breeds playfully chasing around a tree"
 example_prompt1 = "Two dogs of the same breed playing on the grass"
-###
-
-# # Create the first demo
-# demo_vqascore = gr.Interface(
-#     fn=generate,  # function to call
-#     inputs=[
-#         gr.Dropdown(["clip-flant5-xxl", "clip-flant5-xl", ], label="Model Name"), 
-#         gr.Image(type="filepath"), 
-#         gr.Textbox(label="Prompt")
-#     ],  # define the types of inputs
-#     examples=[
-#         ["clip-flant5-xl", example_imgs[0], example_prompt0],
-#         ["clip-flant5-xl", example_imgs[0], example_prompt1],
-#         ],
-#     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."
-# )
 
-# # Create the second demo
-# demo_vqascore_ranking = gr.Interface(
-#     fn=rank_images,  # function to call
-#     inputs=[
-#         gr.Dropdown(["clip-flant5-xl", "clip-flant5-xxl"], label="Model Name"), 
-#         gr.Gallery(label="Generated Images"), 
-#         gr.Textbox(label="Prompt")
-#     ],  # define the types of inputs
-#     outputs=gr.Gallery(label="Ranked Images"),  # define the type of output
-#     examples=[
-#         ["clip-flant5-xl", [[img, ""] for img in example_imgs], example_prompt0],
-#         ["clip-flant5-xl", [[img, ""] for img in example_imgs], example_prompt1]
-        
-#     ],
-#     title="VQAScore Ranking",  # title of the app
-#     description="This model ranks a gallery of images based on their similarity to a text prompt.",
-#     allow_flagging='never'  
-# )
 
 # Custom component for loading examples
 def load_example(model_name, images, prompt):
     return model_name, images, prompt
 
-
-# demo_vqascore = gr.Interface(
-#     fn=generate,  # function to call
-#     inputs=[
-#         gr.Dropdown(["clip-flant5-xxl", "clip-flant5-xl", ], label="Model Name"), 
-#         gr.Image(type="filepath"), 
-#         gr.Textbox(label="Prompt")
-#     ],  # define the types of inputs
-#     examples=[
-#         ["clip-flant5-xl", example_imgs[0], example_prompt0],
-#         ["clip-flant5-xl", example_imgs[0], example_prompt1],
-#         ],
-#     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."
-# )
-
 # Create the second demo: VQAScore Ranking
 with gr.Blocks() as demo_vqascore_ranking:
     # gr.Markdown("# VQAScore Ranking\nThis model ranks a gallery of images based on their similarity to a text prompt.")
@@ -227,29 +101,6 @@ with gr.Blocks() as demo_vqascore_ranking:
             example1_button.click(fn=lambda: load_example("clip-flant5-xxl", example_imgs, example_prompt0), inputs=[], outputs=[model_dropdown, gallery, prompt])
             example2_button.click(fn=lambda: load_example("clip-flant5-xxl", example_imgs, example_prompt1), inputs=[], outputs=[model_dropdown, gallery, prompt])
     
-# # Create the second demo
-# with gr.Blocks() as demo_vqascore_ranking:
-#     gr.Markdown("# VQAScore Ranking\nThis model ranks a gallery of images based on their similarity to a text prompt.")
-#     model_dropdown = gr.Dropdown(["clip-flant5-xxl", "clip-flant5-xl"], value="clip-flant5-xxl",  label="Model Name")
-#     gallery = gr.Gallery(label="Generated Images", elem_id="input-gallery", columns=4, allow_preview=True)
-#     prompt = gr.Textbox(label="Prompt")
-#     rank_button = gr.Button("Rank Images")
-#     ranked_gallery = gr.Gallery(label="Ranked Images with Scores", elem_id="ranked-gallery", columns=4, allow_preview=True)
-    
-#     rank_button.click(fn=rank_images, inputs=[model_dropdown, gallery, prompt], outputs=ranked_gallery)
-    
-#     # Custom example buttons
-#     example1_button = gr.Button("Load Example 1")
-#     example2_button = gr.Button("Load Example 2")
-    
-#     example1_button.click(fn=lambda: load_example("clip-flant5-xxl", example_imgs, example_prompt0), inputs=[], outputs=[model_dropdown, gallery, prompt])
-#     example2_button.click(fn=lambda: load_example("clip-flant5-xxl", example_imgs, example_prompt1), inputs=[], outputs=[model_dropdown, gallery, prompt])
-
-#     # Layout to allow user to input their own data
-#     with gr.Row():
-#         gr.Column([model_dropdown, gallery, prompt, rank_button])
-#         gr.Column([example1_button, example2_button])
-
 # Launch the interface
 demo_vqascore_ranking.queue()
 demo_vqascore_ranking.launch(share=True)