supply the images as individual tensors

src/model.py

@@ -55,9 +55,9 @@ def auto_complete_config(auto_complete_model_config):
         inputs = [
             {"name": "text_input", "data_type": "TYPE_STRING", "dims": [1]},
             {
-                "name": "multi_modal_data",
+                "name": "image",
                 "data_type": "TYPE_STRING",
-                "dims": [1],
+                "dims": [-1],  # can be multiple images as separate elements
                 "optional": True,
             },
             {
@@ -394,20 +394,16 @@ async def generate(self, request):
             if isinstance(prompt, bytes):
                 prompt = prompt.decode("utf-8")
 
-            multi_modal_data_input_tensor = pb_utils.get_input_tensor_by_name(
-                request, "multi_modal_data"
+            image_input_tensor = pb_utils.get_input_tensor_by_name(
+                request, "image"
             )
-            if multi_modal_data_input_tensor:
-                multi_modal_data = multi_modal_data_input_tensor.as_numpy()[0].decode("utf-8")
-                multi_modal_data = json.loads(multi_modal_data)
-                if "image" in multi_modal_data:
-                    image_list = []
-                    for image_base64_string in multi_modal_data["image"]:
-                        if "base64," in image_base64_string:
-                            image_base64_string = image_base64_string.split("base64,")[-1]
-                        image_data = base64.b64decode(image_base64_string)
-                        image = Image.open(BytesIO(image_data)).convert("RGB")
-                        image_list.append(image)
+            if image_input_tensor:
+                image_list = []
+                for image_raw in image_input_tensor.as_numpy():
+                    image_data = base64.b64decode(image_raw.decode("utf-8"))
+                    image = Image.open(BytesIO(image_data)).convert("RGB")
+                    image_list.append(image)
+                if len(image_list) > 0:
                     prompt = {
                         "prompt": prompt,
                         "multi_modal_data": {