Update app.py

app.py

@@ -1,14 +1,49 @@
 import gradio as gr
-from transformers import AutoProcessor, AutoModelForCausalLM
+from transformers import AutoProcessor, AutoModelForImageTextToText
 from PIL import Image
+import requests
 import torch
 
 model_id = "google/medgemma-4b-it"
 
-processor = AutoProcessor.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to(
-    "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
 )
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
+image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
+image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
+
+messages = [
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are an expert radiologist."}]
+    },
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this X-ray"},
+            {"type": "image", "image": image}
+        ]
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=True,
+    return_dict=True, return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+input_len = inputs["input_ids"].shape[-1]
+
+with torch.inference_mode():
+    generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+    generation = generation[0][input_len:]
+
+decoded = processor.decode(generation, skip_special_tokens=True)
+print(decoded)
 
 def generate_report(image):
     if image is None: