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#
# This file is part of TEN Framework, an open source project.
# Licensed under the Apache License, Version 2.0.
# See the LICENSE file for more information.
#
import json
from pydantic import BaseModel
from base64 import b64encode
from io import BytesIO
from typing import Any, Dict
from ten_ai_base.const import CONTENT_DATA_OUT_NAME, DATA_OUT_PROPERTY_END_OF_SEGMENT, DATA_OUT_PROPERTY_TEXT
from ten_ai_base.llm_tool import AsyncLLMToolBaseExtension
from ten_ai_base.types import LLMToolMetadata, LLMToolMetadataParameter, LLMToolResult, LLMToolResultLLMResult
from .openai import OpenAIChatGPT, OpenAIChatGPTConfig
from PIL import Image
from ten import (
AsyncTenEnv,
AudioFrame,
VideoFrame,
Data
)
OPEN_WEBSITE_TOOL_NAME = "open_website"
OPEN_WEBSITE_TOOL_DESCRIPTION = "Open a website with given site name"
class WebsiteEvent(BaseModel):
website_name: str
website_url: str
def rgb2base64jpeg(rgb_data, width, height):
# Convert the RGB image to a PIL Image
pil_image = Image.frombytes("RGBA", (width, height), bytes(rgb_data))
pil_image = pil_image.convert("RGB")
# Resize the image while maintaining its aspect ratio
pil_image = resize_image_keep_aspect(pil_image, 1080)
# Save the image to a BytesIO object in JPEG format
buffered = BytesIO()
pil_image.save(buffered, format="png")
pil_image.save("test.png", format="png")
# Get the byte data of the JPEG image
jpeg_image_data = buffered.getvalue()
# Convert the JPEG byte data to a Base64 encoded string
base64_encoded_image = b64encode(jpeg_image_data).decode("utf-8")
# Create the data URL
mime_type = "image/png"
base64_url = f"data:{mime_type};base64,{base64_encoded_image}"
return base64_url
def resize_image_keep_aspect(image, max_size=512):
"""
Resize an image while maintaining its aspect ratio, ensuring the larger dimension is max_size.
If both dimensions are smaller than max_size, the image is not resized.
:param image: A PIL Image object
:param max_size: The maximum size for the larger dimension (width or height)
:return: A PIL Image object (resized or original)
"""
# Get current width and height
width, height = image.size
# If both dimensions are already smaller than max_size, return the original image
if width <= max_size and height <= max_size:
return image
# Calculate the aspect ratio
aspect_ratio = width / height
# Determine the new dimensions
if width > height:
new_width = max_size
new_height = int(max_size / aspect_ratio)
else:
new_height = max_size
new_width = int(max_size * aspect_ratio)
# Resize the image with the new dimensions
resized_image = image.resize((new_width, new_height))
return resized_image
class ComputerToolExtension(AsyncLLMToolBaseExtension):
def __init__(self, name: str) -> None:
super().__init__(name)
self.openai_chatgpt = None
self.config = None
self.loop = None
self.memory = []
self.max_memory_length = 10
self.image_data = None
self.image_width = 0
self.image_height = 0
async def on_init(self, ten_env: AsyncTenEnv) -> None:
ten_env.log_debug("on_init")
await super().on_init(ten_env)
async def on_start(self, ten_env: AsyncTenEnv) -> None:
ten_env.log_debug("on_start")
await super().on_start(ten_env)
# Prepare configuration
self.config = await OpenAIChatGPTConfig.create_async(ten_env=ten_env)
# Mandatory properties
if not self.config.api_key:
ten_env.log_info("API key is missing, exiting on_start")
return
self.openai_chatgpt = OpenAIChatGPT(ten_env, self.config)
async def on_stop(self, ten_env: AsyncTenEnv) -> None:
ten_env.log_debug("on_stop")
await super().on_stop(ten_env)
async def on_deinit(self, ten_env: AsyncTenEnv) -> None:
ten_env.log_debug("on_deinit")
await super().on_deinit(ten_env)
async def on_audio_frame(self, ten_env: AsyncTenEnv, audio_frame: AudioFrame) -> None:
audio_frame_name = audio_frame.get_name()
ten_env.log_debug("on_audio_frame name {}".format(audio_frame_name))
async def on_video_frame(self, ten_env: AsyncTenEnv, video_frame: VideoFrame) -> None:
video_frame_name = video_frame.get_name()
ten_env.log_debug("on_video_frame name {}".format(video_frame_name))
self.image_data = video_frame.get_buf()
self.image_width = video_frame.get_width()
self.image_height = video_frame.get_height()
def get_tool_metadata(self, _: AsyncTenEnv) -> list[LLMToolMetadata]:
return [
LLMToolMetadata(
name=OPEN_WEBSITE_TOOL_NAME,
description=OPEN_WEBSITE_TOOL_DESCRIPTION,
parameters=[
LLMToolMetadataParameter(
name="name",
type="string",
description="The name of the website to open",
required=True,
),
LLMToolMetadataParameter(
name="url",
type="string",
description="The url of the given website, get based on name",
required=True,
),
]
),
]
async def run_tool(self, ten_env: AsyncTenEnv, name: str, args: dict) -> LLMToolResult:
if name == OPEN_WEBSITE_TOOL_NAME:
site_name = args.get("name")
site_url = args.get("url")
ten_env.log_info(f"open site {site_name} {site_url}")
result = await self._open_website(site_name, site_url, ten_env)
return LLMToolResultLLMResult(
type="llmresult",
content=json.dumps(result),
)
async def _open_website(self, site_name: str, site_url: str, ten_env: AsyncTenEnv) -> Any:
await self._send_data(ten_env, "browse_website", {"name": site_name, "url": site_url})
return {"result": "success"}
async def _send_data(self, ten_env: AsyncTenEnv, action: str, data: Dict[str, Any]):
try:
action_data = json.dumps({
"type": "action",
"data": {
"action": action,
"data": data
}
})
output_data = Data.create(CONTENT_DATA_OUT_NAME)
output_data.set_property_string(
DATA_OUT_PROPERTY_TEXT,
action_data
)
output_data.set_property_bool(
DATA_OUT_PROPERTY_END_OF_SEGMENT, True
)
await ten_env.send_data(output_data)
except Exception as err:
ten_env.log_warn(f"send data error {err}") |