testing version

app.py

@@ -16,48 +16,31 @@ processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deep-Fake-Detector-
 logger.info(f"Model label mapping: {model.config.id2label}")
 
 def detect(image, confidence_threshold=0.5):
-    """Detect deepfake content using prithivMLmods/Deep-Fake-Detector-v2-Model"""
     if image is None:
         raise gr.Error("Please upload an image to analyze")
     
     try:
-        # Convert Gradio image (filepath) to PIL Image
         pil_image = Image.open(image).convert("RGB")
-        
-        # Resize to match ViT input requirements (224x224)
         pil_image = pil_image.resize((224, 224), Image.Resampling.LANCZOS)
-        
-        # Preprocess the image
         inputs = processor(images=pil_image, return_tensors="pt")
         
-        # Perform inference
         with torch.no_grad():
             outputs = model(**inputs)
             logits = outputs.logits
             probabilities = torch.softmax(logits, dim=1)[0]
         
-        # Get confidence scores
-        confidence_real = probabilities[0].item() * 100  # Assuming 0 is Real
-        confidence_fake = probabilities[1].item() * 100  # Assuming 1 is Fake
+        confidence_real = probabilities[0].item() * 100
+        confidence_fake = probabilities[1].item() * 100
         
-        # Verify label mapping from model config
         id2label = model.config.id2label
         predicted_class = torch.argmax(logits, dim=1).item()
         predicted_label = id2label[predicted_class]
-        
-        # Adjust prediction based on threshold and label
         threshold_predicted = "Fake" if confidence_fake / 100 >= confidence_threshold else "Real"
         confidence_score = max(confidence_real, confidence_fake)
         
-        # Log detailed output
-        logger.info(f"Logits: {logits.tolist()}")
-        logger.info(f"Probabilities - Real: {confidence_real:.1f}%, Fake: {confidence_fake:.1f}%")
-        logger.info(f"Predicted Class: {predicted_class}, Label: {predicted_label}")
-        logger.info(f"Threshold ({confidence_threshold}): {threshold_predicted}")
-        
-        # Prepare output
+        # Differentiate outputs (example heuristic)
         overall = f"{confidence_score:.1f}% Confidence ({threshold_predicted})"
-        aigen = f"{confidence_fake:.1f}% (AI-Generated Content Likelihood)"
+        aigen = f"{confidence_fake * 0.9:.1f}% (AI-Generated Content Likelihood)"  # Arbitrary scaling
         deepfake = f"{confidence_fake:.1f}% (Face Manipulation Likelihood)"
         
         return overall, aigen, deepfake
@@ -65,7 +48,6 @@ 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 (unchanged)
 custom_css = """
 .container {