Update app.py

app.py

@@ -38,11 +38,13 @@ def detect(image, confidence_threshold=0.5):
         threshold_predicted = "Fake" if confidence_fake / 100 >= confidence_threshold else "Real"
         confidence_score = max(confidence_real, confidence_fake)
         
-        # Use raw probabilities for clarity, with optional fine-tuning for specific categories
-        aigen_likelihood = confidence_fake  # Assuming AI-Generated is synonymous with Fake for simplicity
-        face_manipulation_likelihood = confidence_fake  # You might want to refine this with additional analysis
+        # Use raw probabilities for clarity
+        aigen_likelihood = confidence_fake  # Assuming AI-Generated is synonymous with Fake
+        face_manipulation_likelihood = confidence_fake  # Refine if possible
+        
+        # Add diagnostic output for debugging
+        logger.info(f"Probabilities - Real: {confidence_real:.1f}%, Fake: {confidence_fake:.1f}%")
         
-        # Format outputs
         overall = f"{confidence_score:.1f}% Confidence ({threshold_predicted})"
         aigen = f"{aigen_likelihood:.1f}% (AI-Generated Content Likelihood)"
         deepfake = f"{face_manipulation_likelihood:.1f}% (Face Manipulation Likelihood)"
@@ -52,7 +54,7 @@ def detect(image, confidence_threshold=0.5):
     except Exception as e:
         logger.error(f"Error during analysis: {str(e)}")
         raise gr.Error(f"Analysis error: {str(e)}")
-
+        
 custom_css = """
 .container {
     max-width: 1200px;