chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Author: Saaket Agashe
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# Date: 2021-09-15
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# License: MIT
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import base64
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import re
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from gui_agents.s1.mllm.MultimodalEngine import (
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LMMEngineAnthropic,
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LMMEngineAzureOpenAI,
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LMMEngineOpenAI,
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LMMEnginevLLM,
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)
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data_type_map = {
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"openai": {"image_url": "image_url"},
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"anthropic": {"image_url": "image"},
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}
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class LMMAgent:
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def __init__(self, engine_params=None, system_prompt=None, engine=None):
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if engine is None:
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if engine_params is not None:
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engine_type = engine_params.get("engine_type")
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if engine_type == "openai":
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self.engine = LMMEngineOpenAI(**engine_params)
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elif engine_type == "anthropic":
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self.engine = LMMEngineAnthropic(**engine_params)
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elif engine_type == "azure":
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self.engine = LMMEngineAzureOpenAI(**engine_params)
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elif engine_type == "vllm":
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self.engine = LMMEnginevLLM(**engine_params)
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else:
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raise ValueError("engine_type must be either 'openai' or 'azure'")
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else:
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raise ValueError("engine_params must be provided")
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else:
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self.engine = engine
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self.messages = [] # Empty messages
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if system_prompt:
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self.add_system_prompt(system_prompt)
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else:
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self.add_system_prompt("You are a helpful assistant.")
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def encode_image(self, image_content):
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# if image_content is a path to an image file, check type of the image_content to verify
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if isinstance(image_content, str):
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with open(image_content, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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else:
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return base64.b64encode(image_content).decode("utf-8")
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def reset(
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self,
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):
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self.messages = [
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{
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"role": "system",
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"content": [{"type": "text", "text": self.system_prompt}],
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}
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]
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def add_system_prompt(self, system_prompt):
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self.system_prompt = system_prompt
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if len(self.messages) > 0:
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self.messages[0] = {
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"role": "system",
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"content": [{"type": "text", "text": self.system_prompt}],
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}
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else:
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self.messages.append(
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{
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"role": "system",
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"content": [{"type": "text", "text": self.system_prompt}],
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}
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)
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def remove_message_at(self, index):
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"""Remove a message at a given index"""
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if index < len(self.messages):
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self.messages.pop(index)
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def replace_message_at(
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self, index, text_content, image_content=None, image_detail="high"
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):
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"""Replace a message at a given index"""
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if index < len(self.messages):
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self.messages[index] = {
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"role": self.messages[index]["role"],
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"content": [{"type": "text", "text": text_content}],
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}
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if image_content:
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base64_image = self.encode_image(image_content)
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self.messages[index]["content"].append(
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{base64_image}",
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"detail": image_detail,
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},
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}
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)
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def add_message(
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self, text_content, image_content=None, role=None, image_detail="high"
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):
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"""Add a new message to the list of messages"""
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# API-style inference from OpenAI and AzureOpenAI
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if isinstance(self.engine, (LMMEngineOpenAI, LMMEngineAzureOpenAI)):
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# infer role from previous message
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if role != "user":
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if self.messages[-1]["role"] == "system":
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role = "user"
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elif self.messages[-1]["role"] == "user":
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role = "assistant"
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elif self.messages[-1]["role"] == "assistant":
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role = "user"
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message = {
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"role": role,
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"content": [{"type": "text", "text": text_content}],
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}
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if image_content:
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# Check if image_content is a list or a single image
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if isinstance(image_content, list):
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# If image_content is a list of images, loop through each image
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for image in image_content:
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base64_image = self.encode_image(image)
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message["content"].append(
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{base64_image}",
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"detail": image_detail,
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},
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}
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)
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else:
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# If image_content is a single image, handle it directly
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base64_image = self.encode_image(image_content)
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message["content"].append(
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{base64_image}",
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"detail": image_detail,
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},
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}
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)
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self.messages.append(message)
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# For API-style inference from Anthropic
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elif isinstance(self.engine, LMMEngineAnthropic):
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# infer role from previous message
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if role != "user":
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if self.messages[-1]["role"] == "system":
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role = "user"
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elif self.messages[-1]["role"] == "user":
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role = "assistant"
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elif self.messages[-1]["role"] == "assistant":
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role = "user"
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message = {
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"role": role,
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"content": [{"type": "text", "text": text_content}],
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}
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if image_content:
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# Check if image_content is a list or a single image
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if isinstance(image_content, list):
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# If image_content is a list of images, loop through each image
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for image in image_content:
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base64_image = self.encode_image(image)
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message["content"].append(
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/png",
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"data": base64_image,
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},
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}
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)
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else:
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# If image_content is a single image, handle it directly
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base64_image = self.encode_image(image_content)
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message["content"].append(
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/png",
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"data": base64_image,
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},
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}
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)
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self.messages.append(message)
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# Locally hosted vLLM model inference
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elif isinstance(self.engine, LMMEnginevLLM):
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# infer role from previous message
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if role != "user":
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if self.messages[-1]["role"] == "system":
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role = "user"
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elif self.messages[-1]["role"] == "user":
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role = "assistant"
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elif self.messages[-1]["role"] == "assistant":
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role = "user"
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message = {
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"role": role,
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"content": [{"type": "text", "text": text_content}],
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}
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if image_content:
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# Check if image_content is a list or a single image
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if isinstance(image_content, list):
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# If image_content is a list of images, loop through each image
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for image in image_content:
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base64_image = self.encode_image(image)
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message["content"].append(
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{
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"type": "image",
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"image": f"data:image;base64,{base64_image}",
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}
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)
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else:
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# If image_content is a single image, handle it directly
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base64_image = self.encode_image(image_content)
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message["content"].append(
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{"type": "image", "image": f"data:image;base64,{base64_image}"}
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)
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self.messages.append(message)
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def get_response(
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self,
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user_message=None,
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image=None,
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messages=None,
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temperature=0.0,
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max_new_tokens=None,
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**kwargs,
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):
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"""Generate the next response based on previous messages"""
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if messages is None:
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messages = self.messages
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if user_message:
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messages.append(
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{"role": "user", "content": [{"type": "text", "text": user_message}]}
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)
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return self.engine.generate(
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messages,
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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**kwargs,
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)
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