feat: use media url directly in both LMM and code sandbox

vision_agent/agent/vision_agent_coder.py

@@ -718,7 +718,12 @@ def chat_with_workflow(
  for chat_i in chat:
  if "media" in chat_i:
  for media in chat_i["media"]:
- media = code_interpreter.upload_file(media)
+ media = (
+ media
+ if type(media) is str
+ and media.startswith(("http", "https"))
+ else code_interpreter.upload_file(media)
+ )
  chat_i["content"] += f" Media name {media}" # type: ignore
  media_list.append(media)
 

vision_agent/lmm/lmm.py

@@ -30,6 +30,12 @@ def encode_image_bytes(image: bytes) -> str:
 
 
 def encode_media(media: Union[str, Path]) -> str:
+ if type(media) is str and media.startswith(("http", "https")):
+ # for mp4 video url, we assume there is a same url but ends with png
+ # vision-agent-ui will upload this png when uploading the video
+ if media.endswith((".mp4", "mov")) and media.find("vision-agent-dev.s3") != -1:
+ return media[:-4] + ".png"
+ return media
  extension = "png"
  extension = Path(media).suffix
  if extension.lower() not in {
@@ -138,7 +144,11 @@ def chat(
  {
  "type": "image_url",
  "image_url": {
- "url": f"data:image/png;base64,{encoded_media}",
+ "url": (
+ encoded_media
+ if encoded_media.startswith(("http", "https"))
+ else f"data:image/png;base64,{encoded_media}"
+ ),
  "detail": "low",
  },
  },
@@ -390,7 +400,6 @@ def chat(
  tmp_kwargs = self.kwargs | kwargs
  data.update(tmp_kwargs)
  if "stream" in tmp_kwargs and tmp_kwargs["stream"]:
-
  json_data = json.dumps(data)
 
  def f() -> Iterator[Optional[str]]:
@@ -424,7 +433,6 @@ def generate(
  media: Optional[List[Union[str, Path]]] = None,
  **kwargs: Any,
  ) -> Union[str, Iterator[Optional[str]]]:
-
  url = f"{self.url}/generate"
  data: Dict[str, Any] = {
  "model": self.model_name,
@@ -439,7 +447,6 @@ def generate(
  tmp_kwargs = self.kwargs | kwargs
  data.update(tmp_kwargs)
  if "stream" in tmp_kwargs and tmp_kwargs["stream"]:
-
  json_data = json.dumps(data)
 
  def f() -> Iterator[Optional[str]]:

vision_agent/tools/tools.py

@@ -1,3 +1,4 @@
+import os
 import io
 import json
 import logging
@@ -14,6 +15,7 @@
 from PIL import Image, ImageDraw, ImageFont
 from pillow_heif import register_heif_opener # type: ignore
 from pytube import YouTube # type: ignore
+import urllib.request
 
 from vision_agent.clients.landing_public_api import LandingPublicAPI
 from vision_agent.tools.tool_utils import (
@@ -1220,6 +1222,13 @@ def extract_frames(
  video_file_path = video.download(output_path=temp_dir)
 
  return extract_frames_from_video(video_file_path, fps)
+ elif str(video_uri).startswith(("http", "https")):
+ _, image_suffix = os.path.splitext(video_uri)
+ with tempfile.NamedTemporaryFile(delete=False, suffix=image_suffix) as tmp_file:
+ # Download the video and save it to the temporary file
+ with urllib.request.urlopen(str(video_uri)) as response:
+ tmp_file.write(response.read())
+ return extract_frames_from_video(tmp_file.name, fps)
 
  return extract_frames_from_video(str(video_uri), fps)
 
@@ -1250,10 +1259,10 @@ def default(self, obj: Any): # type: ignore
 
 
 def load_image(image_path: str) -> np.ndarray:
- """'load_image' is a utility function that loads an image from the given file path string.
+ """'load_image' is a utility function that loads an image from the given file path string or an URL.
 
  Parameters:
- image_path (str): The path to the image.
+ image_path (str): The path or URL to the image.
 
  Returns:
  np.ndarray: The image as a NumPy array.
@@ -1265,6 +1274,13 @@ def load_image(image_path: str) -> np.ndarray:
  # NOTE: sometimes the generated code pass in a NumPy array
  if isinstance(image_path, np.ndarray):
  return image_path
+ if image_path.startswith(("http", "https")):
+ _, image_suffix = os.path.splitext(image_path)
+ with tempfile.NamedTemporaryFile(delete=False, suffix=image_suffix) as tmp_file:
+ # Download the image and save it to the temporary file
+ with urllib.request.urlopen(image_path) as response:
+ tmp_file.write(response.read())
+ image_path = tmp_file.name
  image = Image.open(image_path).convert("RGB")
  return np.array(image)