Update app.py

app.py

@@ -0,0 +1,51 @@
+from unsloth import FastVisionModel
+from PIL import Image
+import numpy as np
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model and tokenizer
+model, tokenizer = FastVisionModel.from_pretrained(
+    "0llheaven/Llama-3.2-11B-Vision-Radiology-mini",
+    load_in_4bit=True,
+    use_gradient_checkpointing="unsloth",
+)
+FastVisionModel.for_inference(model)
+
+model.to(device)
+
+def predict_radiology_description(image, instruction):
+    try:
+        messages = [{"role": "user", "content": [
+            {"type": "image"},
+            {"type": "text", "text": instruction}
+        ]}]
+        input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+
+        inputs = tokenizer(
+            image,
+            input_text,
+            add_special_tokens=False,
+            return_tensors="pt",
+        ).to(device)
+
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            temperature=1.5,
+            min_p=0.1
+        )
+
+        generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        return generated_text.replace("assistant", "\n\nassistant").strip()
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Example of usage!
+image_path = 'example_image.jpeg'
+instruction = 'You are an expert radiographer. Describe accurately what you see in this image.'
+
+image = Image.open(image_path).convert("RGB")
+output = predict_radiology_description(image, instruction)
+print(output)