update

app.py

@@ -1,149 +1,10 @@
-import gradio as gr
-import os
-import yaml
-import base64
-import logging
-import os
-from io import BytesIO
-from typing import Optional
-
-import yaml
-from openai import AzureOpenAI, OpenAI  # pip install openai
-from PIL import Image
-from tenacity import (
-    retry,
-    stop_after_attempt,
-    stop_after_delay,
-    wait_random_exponential,
-)
+import gradio as gr 
+import logging  
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
-class GPTclient:
-    """A client to interact with the GPT model via OpenAI or Azure API."""
-
-    def __init__(
-        self,
-        endpoint: str,
-        api_key: str,
-        model_name: str = "yfb-gpt-4o",
-        api_version: str = None,
-        verbose: bool = False,
-    ):
-        if api_version is not None:
-            self.client = AzureOpenAI(
-                azure_endpoint=endpoint,
-                api_key=api_key,
-                api_version=api_version,
-            )
-        else:
-            self.client = OpenAI(
-                base_url=endpoint,
-                api_key=api_key,
-            )
-
-        self.endpoint = endpoint
-        self.model_name = model_name
-        self.image_formats = {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif"}
-        self.verbose = verbose
-        logger.info(f"Using GPT model: {self.model_name}.")
-
-    @retry(
-        wait=wait_random_exponential(min=1, max=20),
-        stop=(stop_after_attempt(10) | stop_after_delay(30)),
-    )
-    def completion_with_backoff(self, **kwargs):
-        return self.client.chat.completions.create(**kwargs)
-
-    def query(
-        self,
-        text_prompt: str,
-        image_base64: Optional[list[str | Image.Image]] = None,
-        system_role: Optional[str] = None,
-    ) -> Optional[str]:
-        """Queries the GPT model with a text and optional image prompts.
-
-        Args:
-            text_prompt (str): The main text input that the model responds to.
-            image_base64 (Optional[List[str]]): A list of image base64 strings
-                or local image paths or PIL.Image to accompany the text prompt.
-            system_role (Optional[str]): Optional system-level instructions
-                that specify the behavior of the assistant.
-
-        Returns:
-            Optional[str]: The response content generated by the model based on
-                the prompt. Returns `None` if an error occurs.
-        """
-        if system_role is None:
-            system_role = "You are a highly knowledgeable assistant specializing in physics, engineering, and object properties."  # noqa
-
-        content_user = [
-            {
-                "type": "text",
-                "text": text_prompt,
-            },
-        ]
-
-        # Process images if provided
-        if image_base64 is not None:
-            image_base64 = (
-                image_base64
-                if isinstance(image_base64, list)
-                else [image_base64]
-            )
-            for img in image_base64:
-                if isinstance(img, Image.Image):
-                    buffer = BytesIO()
-                    img.save(buffer, format=img.format or "PNG")
-                    buffer.seek(0)
-                    image_binary = buffer.read()
-                    img = base64.b64encode(image_binary).decode("utf-8")
-                elif (
-                    len(os.path.splitext(img)) > 1
-                    and os.path.splitext(img)[-1].lower() in self.image_formats
-                ):
-                    if not os.path.exists(img):
-                        raise FileNotFoundError(f"Image file not found: {img}")
-                    with open(img, "rb") as f:
-                        img = base64.b64encode(f.read()).decode("utf-8")
-
-                content_user.append(
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": f"data:image/png;base64,{img}"},
-                    }
-                )
-
-        payload = {
-            "messages": [
-                {"role": "system", "content": system_role},
-                {"role": "user", "content": content_user},
-            ],
-            "temperature": 0.1,
-            "max_tokens": 500,
-            "top_p": 0.1,
-            "frequency_penalty": 0,
-            "presence_penalty": 0,
-            "stop": None,
-        }
-        payload.update({"model": self.model_name})
-
-        response = None
-        try:
-            response = self.completion_with_backoff(**payload)
-            response = response.choices[0].message.content
-        except Exception as e:
-            logger.error(f"Error GPTclint {self.endpoint} API call: {e}")
-            response = None
-
-        if self.verbose:
-            logger.info(f"Prompt: {text_prompt}")
-            logger.info(f"Response: {response}")
-
-        return response
-
 from embodied_gen.utils.gpt_clients import GPT_CLIENT
 
 print(GPT_CLIENT.api_version, GPT_CLIENT.model_name, GPT_CLIENT.endpoint)

embodied_gen/validators/urdf_convertor.py

@@ -366,6 +366,9 @@ class URDFGenerator(object):
             image_path = combine_images_to_base64(image_path)
 
         response = self.gpt_client.query(text_prompt, image_path)
+        print("text_prompt: ", text_prompt)
+        print("image_path: ", image_path)
+        print("response: ", response)
         if response is None:
             asset_attrs = {
                 "category": category.lower(),