app.py

import gradio as gr
import torch
from torchvision.datasets import CIFAR100
from PIL import Image
import random
import numpy as np
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution

Image.warnings.simplefilter('ignore', Image.DecompressionBombWarning)

try:
    sr_processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x4-64")
    sr_model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x4-64")
    sr_model.eval()
except Exception as e:
    sr_model = None

try:
    classifier_model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar100_resnet56", pretrained=True)
    classifier_model.eval()
except Exception as e:
    classifier_model = None

cifar100_dataset = CIFAR100(root="./cifar100_data", train=False, download=True)
cifar100_labels = cifar100_dataset.classes

def upscale_image(low_res_pil_image):
    if sr_model is None or low_res_pil_image is None:
        return low_res_pil_image.resize((400, 400), Image.Resampling.NEAREST)

    with torch.no_grad():
        inputs = sr_processor(low_res_pil_image, return_tensors="pt")
        outputs = sr_model(**inputs)
        
    output_tensor = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1)
    output_numpy = np.moveaxis(output_tensor.numpy(), 0, -1)
    output_image = (output_numpy * 255.0).round().astype(np.uint8)
    
    return Image.fromarray(output_image)

def predict_ai(low_res_pil_image):
    try:
        from torchvision import transforms
        preprocess_for_classifier = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.5071, 0.4867, 0.4408], 
                std=[0.2675, 0.2565, 0.2761]
            ),
        ])
        img_t = preprocess_for_classifier(low_res_pil_image.convert("RGB"))
        batch_t = torch.unsqueeze(img_t, 0)
        
        with torch.no_grad():
            out = classifier_model(batch_t)
        
        _, index = torch.max(out, 1)
        return cifar100_labels[index[0]]
    except Exception as e:
        return "Error"
        
def generate_category_markdown():
    md = "|||||\n|:---|:---|:---|:---|\n"
    for i in range(0, 100, 4):
        row = cifar100_labels[i:i+4]
        md += "| " + " | ".join(row) + " |\n"
    return md

def battle(user_guess, state):
    user_score = state["user_score"]
    ai_score = state["ai_score"]
    current_image_idx = state["current_image_idx"]
    played_indices = state["played_indices"]

    low_res_image, label_idx = cifar100_dataset[current_image_idx]
    current_label = cifar100_labels[label_idx]

    ai_guess = predict_ai(low_res_image)

    if user_guess.lower().strip() == current_label.lower():
        user_score += 1
    if ai_guess.lower() == current_label.lower():
        ai_score += 1
    
    if len(played_indices) >= len(cifar100_dataset):
        message = f"AI's Guess: '{ai_guess}'\nCorrect Answer: '{current_label}'\n\nAll images have been played! Game Over."
        next_high_res_image = None
    else:
        while True:
            next_image_idx = random.randint(0, len(cifar100_dataset) - 1)
            if next_image_idx not in played_indices:
                break
        
        next_low_res_image, _ = cifar100_dataset[next_image_idx]
        next_high_res_image = upscale_image(next_low_res_image)
        message = f"AI's Guess: '{ai_guess}'\nCorrect Answer: '{current_label}'"
        state["current_image_idx"] = next_image_idx
        played_indices.add(next_image_idx)

    new_state = {
        "user_score": user_score,
        "ai_score": ai_score,
        "current_image_idx": state["current_image_idx"],
        "played_indices": played_indices
    }
    
    return user_score, ai_score, message, "", next_high_res_image, new_state

def start_game():
    if not classifier_model or not sr_model:
        return 0, 0, "A required AI model failed to load. Please restart.", "", None, {}

    first_idx = random.randint(0, len(cifar100_dataset) - 1)
    first_low_res_image, _ = cifar100_dataset[first_idx]
    first_high_res_image = upscale_image(first_low_res_image)
    
    initial_state = {
        "user_score": 0,
        "ai_score": 0,
        "current_image_idx": first_idx,
        "played_indices": {first_idx}
    }
    return 0, 0, "Game Start! What is this high-resolution image?", "", first_high_res_image, initial_state

with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky")) as demo:
    state = gr.State()

    gr.Markdown("<h1>Human vs. AI: Super-Resolution Battle</h1>")
    gr.Markdown("A Swin2SR AI has upscaled a 32x32 image for you. Can you guess it before the other AI, which only sees the original low-res image?")

    with gr.Row():
        user_score_display = gr.Number(label="Your Score", value=0, interactive=False)
        ai_score_display = gr.Number(label="AI Score", value=0, interactive=False)
    
    with gr.Row(equal_height=False):
        with gr.Column(scale=2):
            image_display = gr.Image(label="Guess this upscaled image!", type="pil", height=400, width=400, interactive=False)
            result_display = gr.Textbox(label="Round Result", interactive=False, lines=3)
        with gr.Column(scale=1):
            user_input = gr.Textbox(label="What is this image?", placeholder="e.g., apple, bicycle, cloud...")
            submit_button = gr.Button("Submit Guess", variant="primary")
            with gr.Accordion("View All 100 Categories", open=False):
                gr.Markdown(generate_category_markdown())

    submit_button.click(fn=battle, inputs=[user_input, state], outputs=[user_score_display, ai_score_display, result_display, user_input, image_display, state])
    user_input.submit(fn=battle, inputs=[user_input, state], outputs=[user_score_display, ai_score_display, result_display, user_input, image_display, state])
    demo.load(fn=start_game, inputs=None, outputs=[user_score_display, ai_score_display, result_display, user_input, image_display, state])

if __name__ == "__main__":
    demo.launch()