Update README.md

README.md

@@ -73,25 +73,23 @@ model_path = "Ateeqq/nsfw-image-detection"
 processor = AutoImageProcessor.from_pretrained(model_path)
 model = SiglipForImageClassification.from_pretrained(model_path)
 
-image = Image.open("your_image_path.jpg")
+image_path = r"your_image_path.jpg"
+image = Image.open(image_path).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")
 
 with torch.no_grad():
     logits = model(**inputs).logits
-
 probabilities = F.softmax(logits, dim=1)
 
 predicted_class_id = logits.argmax().item()
 predicted_class_label = model.config.id2label[predicted_class_id]
-
 confidence_scores = probabilities[0].tolist()
 
 print(f"Predicted class ID: {predicted_class_id}")
 print(f"Predicted class label: {predicted_class_label}\n")
-
 for i, score in enumerate(confidence_scores):
     label = model.config.id2label[i]
-    print(f"Confidence for '{label}': {score:.4f}")
+    print(f"Confidence for '{label}': {score:.6f}")
 ```
 
 ### Output
@@ -100,9 +98,9 @@ for i, score in enumerate(confidence_scores):
 Predicted class ID: 0
 Predicted class label: graphically_violent
 
-Confidence for 'graphically_violent': 0.9941
-Confidence for 'nudity_pornography': 0.0040
-Confidence for 'safe_normal': 0.0019
+Confidence for 'graphically_violent': 0.999988
+Confidence for 'nudity_pornography': 0.000004
+Confidence for 'safe_normal': 0.000008
 ```
 
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