Create handler.py

handler.py

@@ -0,0 +1,56 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import torch
+from io import BytesIO
+import base64
+
+# Initialize the model and tokenizer
+model_id = "HuggingFaceM4/idefics2-8b"
+model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Check if CUDA (GPU support) is available and then set the device to GPU or CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def preprocess_image(encoded_image):
+    """Decode and preprocess the input image."""
+    decoded_image = base64.b64decode(encoded_image)
+    img = Image.open(BytesIO(decoded_image)).convert("RGB")
+    return img
+
+def handler(event, context):
+    """Handle the incoming request."""
+    try:
+        # Extract the base64-encoded image and question from the event
+        input_image = event['body']['image']
+        question = event['body'].get('question', "What is this image about?")
+
+        # Preprocess the image
+        img = preprocess_image(input_image)
+
+        # Perform inference
+        enc_image = model.encode_image(img).to(device)
+        answer = model.answer_question(enc_image, question, tokenizer)
+
+        # If the output is a tensor, move it back to CPU and convert to list
+        if isinstance(answer, torch.Tensor):
+            answer = answer.cpu().numpy().tolist()
+
+        # Create the response
+        response = {
+            "statusCode": 200,
+            "body": {
+                "answer": answer
+            }
+        }
+        return response
+    except Exception as e:
+        # Handle any errors
+        response = {
+            "statusCode": 500,
+            "body": {
+                "error": str(e)
+            }
+        }
+        return response