Update README.md

README.md

@@ -40,20 +40,20 @@ Feel free to explore and integrate this model into your applications for accurat
 ### Approach
 
 ### First Approach
-<code>
+```python
 import numpy as np 
 from PIL import Image
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-#Load the model and image processor
+# Load the model and image processor
 processor = AutoImageProcessor.from_pretrained("beingamit99/car_damage_detection")
 model = AutoModelForImageClassification.from_pretrained("beingamit99/car_damage_detection")
 
-#Load and process the image
+# Load and process the image
 image = Image.open(IMAGE)
 inputs = processor(images=image, return_tensors="pt")
 
-#Make predictions
+# Make predictions
 outputs = model(**inputs)
 logits = outputs.logits.detach().cpu().numpy()
 predicted_class_id = np.argmax(logits)
@@ -61,15 +61,12 @@ predicted_proba = np.max(logits)
 label_map = model.config.id2label
 predicted_class_name = label_map[predicted_class_id]
 
-#Print the results
-print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f})")
-</code>
+# Print the results
+print(f"Predicted class: {predicted_class_name} (probability: {predicted_proba:.4f}")
 
 ### Second Approach
-<code>
+```python
 from transformers import pipeline
-
 #Create a classification pipeline
 pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
 pipe(IMAGE)
-</code>