Update README.md

README.md

@@ -43,11 +43,34 @@ The model can be directly used for analyzing dermatology images, providing insig
 
 ```python
 
+import torch
 from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+from PIL import Image
 
+# Load the model and processor
 model_id = "brucewayne0459/paligemma_derm"
 processor = AutoProcessor.from_pretrained(model_id)
-model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, device_map={"": 0})
+model.eval()
+
+# Load a sample image and text input
+input_text = "Identify the skin condition?"
+input_image_path = " Replace with your actual image path"  
+input_image = Image.open(input_image_path).convert("RGB")
+
+# Process the input
+inputs = processor(text=input_text, images=input_image, return_tensors="pt", padding="longest").to("cuda" if torch.cuda.is_available() else "cpu")
+
+# Set the maximum length for generation
+max_new_tokens = 50
+
+# Run inference
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)
+
+# Decode the output
+decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
+print("Model Output:", decoded_output)
 ```
 ## Training Details