Update app.py

app.py

@@ -8,6 +8,8 @@ import pandas as pd
 import warnings
 import math
 import numpy as np
+from utils import call_inference
+
 
 # Suppress warnings
 warnings.filterwarnings("ignore", category=UserWarning, message="Using a slow image processor as `use_fast` is unset")
@@ -47,6 +49,10 @@ def softmax(vector):
     
 @spaces.GPU(duration=10)
 def predict_image(img, confidence_threshold):
+
+    response5_raw = call_inference(img)
+    response5 = response5_raw.json()
+    
     # Ensure the image is a PIL Image
     if not isinstance(img, Image.Image):
         raise ValueError(f"Expected a PIL Image, but got {type(img)}")
@@ -72,9 +78,9 @@ def predict_image(img, confidence_threshold):
         
         # Check if either class meets the confidence threshold
         if result_1['artificial'] >= confidence_threshold:
-            label_1 = f"Label: artificial, Confidence: {result_1['artificial']:.4f}"
+            label_1 = f"AI, Confidence: {result_1['artificial']:.4f}"
         elif result_1['real'] >= confidence_threshold:
-            label_1 = f"Label: real, Confidence: {result_1['real']:.4f}"
+            label_1 = f"Real, Confidence: {result_1['real']:.4f}"
         else:
             label_1 = "Uncertain Classification"
     except Exception as e:
@@ -92,9 +98,9 @@ def predict_image(img, confidence_threshold):
         
         # Check if either class meets the confidence threshold
         if result_2['AI Image'] >= confidence_threshold:
-            label_2 = f"Label: AI Image, Confidence: {result_2['AI Image']:.4f}"
+            label_2 = f"AI, Confidence: {result_2['AI Image']:.4f}"
         elif result_2['Real Image'] >= confidence_threshold:
-            label_2 = f"Label: Real Image, Confidence: {result_2['Real Image']:.4f}"
+            label_2 = f"Real, Confidence: {result_2['Real Image']:.4f}"
         else:
             label_2 = "Uncertain Classification"
     except Exception as e:
@@ -120,9 +126,9 @@ def predict_image(img, confidence_threshold):
         
         # Check if either class meets the confidence threshold
         if result_3['AI'] >= confidence_threshold:
-            label_3 = f"Label: AI, Confidence: {result_3['AI']:.4f}"
+            label_3 = f"AI, Confidence: {result_3['AI']:.4f}"
         elif result_3['Real'] >= confidence_threshold:
-            label_3 = f"Label: Real, Confidence: {result_3['Real']:.4f}"
+            label_3 = f"Real, Confidence: {result_3['Real']:.4f}"
         else:
             label_3 = "Uncertain Classification"
     except Exception as e:
@@ -148,9 +154,9 @@ def predict_image(img, confidence_threshold):
         
         # Check if either class meets the confidence threshold
         if result_4['AI'] >= confidence_threshold:
-            label_4 = f"Label: AI, Confidence: {result_4['AI']:.4f}"
+            label_4 = f"AI, Confidence: {result_4['AI']:.4f}"
         elif result_4['Real'] >= confidence_threshold:
-            label_4 = f"Label: Real, Confidence: {result_4['Real']:.4f}"
+            label_4 = f"Real, Confidence: {result_4['Real']:.4f}"
         else:
             label_4 = "Uncertain Classification"
     except Exception as e:
@@ -168,10 +174,10 @@ def predict_image(img, confidence_threshold):
     
     # Combine results
     combined_results = {
-        "SwinV2": label_1,
-        "AI-vs-Real-Image-Detection": label_2,
-        "Organika/sdxl-detector": label_3,
-        "cmckinle/sdxl-flux-detector": label_4,
+        "SwinV2/detect": label_1,
+        "ViT/AI-vs-Real": label_2,
+        "Swin/SDXL": label_3,
+        "Swin/SDXL-FLUX": label_4,
         # "ALSv": label_5
     }