Done

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import pyiqa
+import torch
+import pandas as pd
+from PIL import Image
+
+# Initialize device
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+# Load Models (Pre-selected for variety: Statistical, Perception, Deep Learning)
+# Note: You can add more like 'niqe', 'clipiqa', etc.
+metrics = {
+    "BRISQUE (Lower=Better)": pyiqa.create_metric('brisque', device=device),
+    "MUSIQ (Higher=Better)": pyiqa.create_metric('musiq', device=device),
+    "NIMA (Higher=Better)": pyiqa.create_metric('nima', device=device)
+}
+
+def analyze_image(input_img):
+    if input_img is None:
+        return None
+        
+    # Standardize input for pyiqa
+    img_tensor = pyiqa.utils.img2tensor(input_img).unsqueeze(0).to(device)
+    
+    results = []
+    with torch.no_grad():
+        for name, metric in metrics.items():
+            score = metric(img_tensor).item()
+            results.append({"Model": name, "Score": round(score, 4)})
+    
+    return pd.DataFrame(results)
+
+# Create Gradio Interface
+with gr.Blocks(title="Multi-Model IQA Comparison") as demo:
+    gr.Markdown("# 📸 Image Quality Checker")
+    gr.Markdown("Upload an image to evaluate its quality score across various SOTA models.")
+    
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Input Image")
+        result_table = gr.Dataframe(label="Model Scores")
+        
+    btn = gr.Button("Get Scores")
+    btn.click(fn=analyze_image, inputs=image_input, outputs=result_table)
+
+    gr.Markdown("### Model Descriptions:")
+    gr.Markdown("- **BRISQUE:** Statistical model focusing on naturalness and noise.")
+    gr.Markdown("- **MUSIQ:** Transformer-based model optimized for varied image sizes.")
+    gr.Markdown("- **NIMA:** Neural Aesthetic model that mimics human perception.")
+
+demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+pyiqa
+torch
+torchvision
+pandas
+gradio
+Pillow