Update app.py

app.py

@@ -7,6 +7,10 @@ from PIL import Image
 import numpy as np
 from utils.goat import call_inference
 import io
+import warnings
+
+# Suppress warnings
+warnings.filterwarnings("ignore", category=UserWarning, message="Using a slow image processor as `use_fast` is unset")
 
 # Ensure using GPU if available
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -168,6 +172,43 @@ def predict_image(img, confidence_threshold):
     }
     return img_pil, combined_results
 
+# Define a function to generate the HTML content
+def generate_results_html(results):
+    html_content = f"""
+    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet">
+    <div class="container">
+        <div class="row mt-4">
+            <div class="col">
+                <h5>SwinV2/detect</h5>
+                <p>{results.get("SwinV2/detect", "N/A")}</p>
+            </div>
+            <div class="col">
+                <h5>ViT/AI-vs-Real</h5>
+                <p>{results.get("ViT/AI-vs-Real", "N/A")}</p>
+            </div>
+            <div class="col">
+                <h5>Swin/SDXL</h5>
+                <p>{results.get("Swin/SDXL", "N/A")}</p>
+            </div>
+            <div class="col">
+                <h5>Swin/SDXL-FLUX</h5>
+                <p>{results.get("Swin/SDXL-FLUX", "N/A")}</p>
+            </div>
+            <div class="col">
+                <h5>GOAT</h5>
+                <p>{results.get("GOAT", "N/A")}</p>
+            </div>
+        </div>
+    </div>
+    """
+    return html_content
+
+# Modify the predict_image function to return the HTML content
+def predict_image_with_html(img, confidence_threshold):
+    img_pil, results = predict_image(img, confidence_threshold)
+    html_content = generate_results_html(results)
+    return img_pil, html_content
+
 # Define the Gradio interface
 with gr.Blocks() as iface:
     gr.Markdown("# AI Generated Image Classification")
@@ -185,42 +226,5 @@ with gr.Blocks() as iface:
     
     gr.Button("Predict").click(fn=predict_image_with_html, inputs=inputs, outputs=outputs)
 
-    # Define a function to generate the HTML content
-    def generate_results_html(results):
-        html_content = f"""
-        <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet">
-        <div class="container">
-            <div class="row mt-4">
-                <div class="col">
-                    <h5>SwinV2/detect</h5>
-                    <p>{results.get("SwinV2/detect", "N/A")}</p>
-                </div>
-                <div class="col">
-                    <h5>ViT/AI-vs-Real</h5>
-                    <p>{results.get("ViT/AI-vs-Real", "N/A")}</p>
-                </div>
-                <div class="col">
-                    <h5>Swin/SDXL</h5>
-                    <p>{results.get("Swin/SDXL", "N/A")}</p>
-                </div>
-                <div class="col">
-                    <h5>Swin/SDXL-FLUX</h5>
-                    <p>{results.get("Swin/SDXL-FLUX", "N/A")}</p>
-                </div>
-                <div class="col">
-                    <h5>GOAT</h5>
-                    <p>{results.get("GOAT", "N/A")}</p>
-                </div>
-            </div>
-        </div>
-        """
-        return html_content
-
-    # Modify the predict_image function to return the HTML content
-    def predict_image_with_html(img, confidence_threshold):
-        img_pil, results = predict_image(img, confidence_threshold)
-        html_content = generate_results_html(results)
-        return img_pil, html_content
-
 # Launch the interface
 iface.launch()