diff --git a/test.py b/test.py
deleted file mode 100644
index 47eeaca..0000000
--- a/test.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import base64
-import io
-import os
-from pathlib import Path
-from typing import Optional, Union
-
-import fire
-import instructor
-import torch
-import weave
-from diffusers import StableDiffusion3Pipeline
-from PIL import Image
-from openai import OpenAI
-from pydantic import BaseModel
-
-EXT_TO_MIMETYPE = {
-    ".jpg": "image/jpeg",
-    ".png": "image/png",
-    ".svg": "image/svg+xml",
-}
-
-
-def base64_encode_image(
-    image_path: Union[str, Image.Image], mimetype: Optional[str] = None
-) -> str:
-    image = Image.open(image_path) if isinstance(image_path, str) else image_path
-    mimetype = (
-        EXT_TO_MIMETYPE[Path(image_path).suffix]
-        if isinstance(image_path, str)
-        else "image/png"
-    )
-    byte_arr = io.BytesIO()
-    image.save(byte_arr, format="PNG")
-    encoded_string = base64.b64encode(byte_arr.getvalue()).decode("utf-8")
-    encoded_string = f"data:{mimetype};base64,{encoded_string}"
-    return str(encoded_string)
-
-
-class SD3Model(weave.Model):
-    model_name_or_path: str
-    enable_cpu_offfload: bool
-    _pipeline: StableDiffusion3Pipeline
-
-    def __init__(self, model_name_or_path: str, enable_cpu_offfload: bool):
-        super().__init__(
-            model_name_or_path=model_name_or_path,
-            enable_cpu_offfload=enable_cpu_offfload,
-        )
-        self._pipeline = StableDiffusion3Pipeline.from_pretrained(
-            self.model_name_or_path, torch_dtype=torch.float16
-        )
-        if self.enable_cpu_offfload:
-            self._pipeline.enable_model_cpu_offload()
-        else:
-            self._pipeline = self._pipeline.to("cuda")
-
-    @weave.op()
-    def predict(
-        self,
-        prompt: str,
-        negative_prompt: str,
-        num_inference_steps: int,
-        image_size: int,
-        guidance_scale: float,
-    ) -> str:
-        image = self._pipeline(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            num_inference_steps=num_inference_steps,
-            height=image_size,
-            width=image_size,
-            guidance_scale=guidance_scale,
-        ).images[0]
-        return base64_encode_image(image)
-
-
-class JudgeMent(BaseModel):
-    score: float
-    explanation: str
-
-
-class OpenAIModel(weave.Model):
-    model: str
-    max_retries: int = 5
-    seed: int = 42
-    _openai_client: instructor.Instructor
-
-    def __init__(self, model: str):
-        super().__init__(model=model)
-        self._openai_client = instructor.from_openai(
-            OpenAI(api_key=os.environ["OPENAI_API_KEY"])
-        )
-
-    @weave.op()
-    def predict(self, prompt: str, image: str) -> JudgeMent:
-        return self._openai_client.chat.completions.create(
-            model=self.model,
-            response_model=JudgeMent,
-            max_retries=self.max_retries,
-            seed=self.seed,
-            messages=[
-                {
-                    "role": "system",
-                    "content": """
-You are a helpful assistant meant to describe images is detail. You should pay special attention to the objects
-in the image and their attributes (such as color, shape, texture), spatial layout and action relationships.
-You have to extract the the score and the explanation from the response.
-                    """,
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": f"""
-How well the image align with the prompt '{prompt}'. Assign a score between 0 and 1.
-Also provide a detailed explanation to justify your score.
-                            """,
-                        },
-                        {"type": "image_url", "image_url": {"url": image}},
-                    ],
-                },
-            ],
-        )
-
-
-weave.init(project_name="intro-example")
-
-diffusion_model = SD3Model(
-    model_name_or_path="stabilityai/stable-diffusion-3-medium-diffusers",
-    enable_cpu_offfload=True,
-)
-judge_model = OpenAIModel(model="gpt-4-turbo")
-
-
-@weave.op()
-def evaluate(prompt: str) -> JudgeMent:
-    image = diffusion_model.predict(
-        prompt=prompt,
-        negative_prompt="",
-        num_inference_steps=28,
-        image_size=1024,
-        guidance_scale=7.0,
-    )
-    return judge_model.predict(prompt=prompt, image=image)
-
-
-fire.Fire(evaluate)