diff --git a/riffusion/datatypes.py b/riffusion/datatypes.py
index b24a903..6a8cdb8 100644
--- a/riffusion/datatypes.py
+++ b/riffusion/datatypes.py
@@ -6,7 +6,7 @@ from dataclasses import dataclass
 import typing as T
 
 
-@dataclass
+@dataclass(frozen=True)
 class PromptInput:
     """
     Parameters for one end of interpolation.
@@ -25,7 +25,7 @@ class PromptInput:
     guidance: float = 7.0
 
 
-@dataclass
+@dataclass(frozen=True)
 class InferenceInput:
     """
     Parameters for a single run of the riffusion model, interpolating between
@@ -53,7 +53,7 @@ class InferenceInput:
     mask_image_id: T.Optional[str] = None
 
 
-@dataclass
+@dataclass(frozen=True)
 class InferenceOutput:
     """
     Response from the model inference server.
diff --git a/riffusion/server.py b/riffusion/server.py
index 4abcfad..ff7d026 100644
--- a/riffusion/server.py
+++ b/riffusion/server.py
@@ -4,6 +4,7 @@ Inference server for the riffusion project.
 
 import base64
 import dataclasses
+import functools
 import logging
 import io
 import json
@@ -13,6 +14,7 @@ import typing as T
 
 import dacite
 import flask
+
 from flask_cors import CORS
 import PIL
 import torch
@@ -116,16 +118,29 @@ def run_inference():
         logging.info(json_data)
         return str(exception), 400
 
+    response = compute(inputs)
+
+    # Log the total time
+    logging.info(f"Request took {time.time() - start_time:.2f} s")
+
+    return response
+
+# TODO(hayk): Enable cache here.
+# @functools.lru_cache()
+def compute(inputs: InferenceInput) -> str:
+    """
+    Does all the heavy lifting of the request.
+    """
     # Load the seed image by ID
     init_image_path = Path(SEED_IMAGES_DIR, f"{inputs.seed_image_id}.png")
-    if not init_image_path.is_file:
+    if not init_image_path.is_file():
         return f"Invalid seed image: {inputs.seed_image_id}", 400
     init_image = PIL.Image.open(str(init_image_path)).convert("RGB")
 
     # Load the mask image by ID
     if inputs.mask_image_id:
         mask_image_path = Path(SEED_IMAGES_DIR, f"{inputs.mask_image_id}.png")
-        if not mask_image_path.is_file:
+        if not mask_image_path.is_file():
             return f"Invalid mask image: {inputs.mask_image_id}", 400
         mask_image = PIL.Image.open(str(mask_image_path)).convert("RGB")
     else:
@@ -148,12 +163,10 @@ def run_inference():
         duration_s=duration_s,
     )
 
-    # Log the total time
-    logging.info(f"Request took {time.time() - start_time:.2f} s")
-
     return flask.jsonify(dataclasses.asdict(output))
 
 
+
 def image_bytes_from_image(image: PIL.Image, mode: str = "PNG") -> io.BytesIO:
     """
     Convert a PIL image into bytes of the given image format.