app.py

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
except ImportError:
    GPU = lambda duration: (lambda f: f)  # Dummy decorator if spaces.GPU is not available

@GPU(duration=20)
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

@GPU(duration=20)
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

# Create the first demo
demo_vqascore = 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."
)

# 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
    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'  
)

# Combine the demos into a tabbed interface
tabbed_interface = gr.TabbedInterface([demo_vqascore, demo_vqascore_ranking], ["VQAScore", "VQAScore Ranking"])

# Launch the tabbed interface
tabbed_interface.queue()
tabbed_interface.launch(share=False)