app.py

import gradio as gr
import os
import json
import tempfile
from pathlib import Path
from PIL import Image
from huggingface_hub import hf_hub_download
from model.FGResQ import FGResQ

# Path to model weights
try:
    MODEL_PATH = hf_hub_download(
        repo_id="orpheus0429/FGResQ",
        filename="weights/FGResQ.pth" 
    )
except Exception as e:
    print(f"Failed to download model: {e}")
    # Fallback to local weights if available
    MODEL_PATH = "weights/FGResQ.pth"

print(f"Loading model from {MODEL_PATH}")
model = FGResQ(model_path=MODEL_PATH)

def _save_temp_image(img):
    if img is None:
        return None
    if isinstance(img, str) and Path(img).exists():
        return img
    if isinstance(img, Image.Image):
        tf = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
        tf.close()
        img.save(tf.name)
        return tf.name
    try:
        pil = Image.fromarray(img)
        tf = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
        tf.close()
        pil.save(tf.name)
        return tf.name
    except Exception:
        return None

def predict_single(image, task):
    img_path = _save_temp_image(image)
    if img_path is None:
        return "No image provided"
    
    try:
        score = model.predict_single(img_path)
        if score is None:
            return "Model returned None"
        return f"{score:.4f}"
    except Exception as e:
        return f"Error: {str(e)}"
    finally:
        pass

def predict_pair(image1, image2, task):
    p1 = _save_temp_image(image1)
    p2 = _save_temp_image(image2)
    
    if not p1 or not p2:
        return "Missing images"
    
    try:
        result = model.predict_pair(p1, p2)
        if result is None:
            return "Model returned None"
        
        comparison = result.get("comparison")
        return comparison
    except Exception as e:
        return f"Error: {str(e)}"

def load_examples(json_path, mode="single"):
    if not os.path.exists(json_path):
        print(f"Warning: {json_path} not found")
        return []
        
    with open(json_path, 'r') as f:
        data = json.load(f)
    
    examples = []
    for item in data:
        if mode == "single":
            # Prepend fineData/ to the path
            img_path = os.path.join("fineData", item["image_name"])
            task = item.get("task", "")
            if os.path.exists(img_path):
                examples.append([img_path, task])
        elif mode == "pair":
            img_path_A = os.path.join("fineData", item["image_nameA"])
            img_path_B = os.path.join("fineData", item["image_nameB"])
            task = item.get("task", "")
            if os.path.exists(img_path_A) and os.path.exists(img_path_B):
                examples.append([img_path_A, img_path_B, task])
    return examples

single_examples = load_examples("score_example.json", mode="single")
pair_examples = load_examples("rank_example.json", mode="pair")

with gr.Blocks(title="FGResQ Demo") as demo:
    gr.Markdown("Fine-grained Image Quality Assessment for Perceptual Image Restoration Demo")

    with gr.Tab("Single Image Mode"):
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(type="pil", label="Input Image")
                task_input = gr.Textbox(label="Task")
                submit_btn = gr.Button("Predict")
            with gr.Column():
                score_output = gr.Textbox(label="Quality Score")
        
        if single_examples:
            gr.Examples(
                examples=single_examples,
                inputs=[image_input, task_input],
                label="Examples"
            )
        
        submit_btn.click(
            fn=predict_single, 
            inputs=[image_input, task_input], 
            outputs=score_output
        )

    with gr.Tab("Pairwise Mode"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    image_input_A = gr.Image(type="pil", label="Image A")
                    image_input_B = gr.Image(type="pil", label="Image B")
                task_input_pair = gr.Textbox(label="Task")
                submit_btn_pair = gr.Button("Compare")
            with gr.Column():
                compare_output = gr.Textbox(label="Comparison Result")
        
        if pair_examples:
            gr.Examples(
                examples=pair_examples,
                inputs=[image_input_A, image_input_B, task_input_pair],
                label="Examples"
            )

        submit_btn_pair.click(
            fn=predict_pair, 
            inputs=[image_input_A, image_input_B, task_input_pair], 
            outputs=compare_output
        )


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