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from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
from .chat_history import *
from .agent import *
try:
from ..screen.shot import *
from ..utils.db import load_model_settings, agents
from ..llm import get_model
from ..llm_settings import each_message_extension, llm_settings
except ImportError:
from screen.shot import *
from utils.db import load_model_settings, agents
from llm import get_model
from llm_settings import each_message_extension, llm_settings
config = {"configurable": {"thread_id": "abc123"}}
def agentic(
llm_input, llm_history, client, screenshot_path=None, dont_save_image=False
):
global agents
from crewai import Task, Crew
from crewai import Agent as crewai_Agent
the_agents = []
for each in agents:
the_agents.append(
crewai_Agent(
role=each["role"],
goal=each["goal"],
backstory=each["backstory"],
llm=get_model(high_context=True),
)
)
agents = the_agents
print("LLM INPUT", llm_input)
def image_explaination():
the_message = [
{"type": "text", "text": "Explain the image"},
]
if screenshot_path:
base64_image = encode_image(screenshot_path)
the_message.append(
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
},
)
print("LEN OF İMAGE", len(base64_image))
the_message = HumanMessage(content=the_message)
get_chat_message_history().add_message(the_message)
the_model = load_model_settings()
if llm_settings[the_model]["provider"] == "openai":
msg = get_agent_executor().invoke(
{"messages": llm_history + [the_message]}, config=config
)
if llm_settings[the_model]["provider"] == "google":
msg = get_agent_executor().invoke(
{"messages": llm_history + [the_message]}, config=config
)
if llm_settings[the_model]["provider"] == "ollama":
msg = get_agent_executor().invoke(
{
"input": the_message,
"chat_history": llm_history,
}
)
the_last_messages = msg["messages"]
return the_last_messages[-1].content
if screenshot_path:
image_explain = image_explaination()
llm_input += "User Sent Image and image content is: " + image_explain
llm_input = llm_input + each_message_extension
task = Task(
description=llm_input, expected_output="Answer", agent=agents[0], tools=get_tools()
)
the_crew = Crew(
agents=agents,
tasks=[task],
full_output=True,
verbose=True,
)
result = the_crew.kickoff()["final_output"]
get_chat_message_history().add_message(HumanMessage(content=[llm_input.replace(each_message_extension, "")]))
get_chat_message_history().add_message(AIMessage(content=[result]))
return result
def assistant(
llm_input, llm_history, client, screenshot_path=None, dont_save_image=False
):
if len(agents) != 0:
print("Moving to Agentic")
return agentic(llm_input, llm_history, client, screenshot_path, dont_save_image)
print("LLM INPUT", llm_input)
llm_input = llm_input + each_message_extension
the_message = [
{"type": "text", "text": f"{llm_input}"},
]
if screenshot_path:
base64_image = encode_image(screenshot_path)
the_message.append(
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
},
)
print("LEN OF IMAGE", len(base64_image))
the_message = HumanMessage(content=the_message)
get_chat_message_history().add_message(the_message)
the_model = load_model_settings()
if llm_settings[the_model]["provider"] == "openai":
msg = get_agent_executor().invoke(
{"messages": llm_history + [the_message]}, config=config
)
if llm_settings[the_model]["provider"] == "google":
the_history = []
for message in llm_history:
try:
if isinstance(message, SystemMessage):
the_mes = HumanMessage(content=message.content[0]["text"])
the_history.append(the_mes)
elif isinstance(message, HumanMessage):
the_mes = HumanMessage(content=message.content[0]["text"])
the_history.append(the_mes)
else:
the_mes = AIMessage(content=message.content[0]["text"])
the_history.append(the_mes)
except:
the_mes = AIMessage(content=message.content)
the_history.append(the_mes)
llm_input += each_message_extension
the_last_message = HumanMessage(content=llm_input)
msg = get_agent_executor().invoke(
{"messages": the_history + [the_last_message]}, config=config
)
elif llm_settings[the_model]["provider"] == "groq":
the_history = []
for message in llm_history:
try:
if isinstance(message, SystemMessage):
the_mes = SystemMessage(content=message.content[0]["text"])
the_history.append(the_mes)
elif isinstance(message, HumanMessage):
the_mes = HumanMessage(content=message.content[0]["text"])
the_history.append(the_mes)
else:
the_mes = AIMessage(content=message.content[0]["text"])
the_history.append(the_mes)
except:
the_mes = AIMessage(content=message.content)
the_history.append(the_mes)
llm_input += each_message_extension
the_last_message = HumanMessage(content=llm_input)
msg = get_agent_executor().invoke(
{"messages": the_history + [the_last_message]}, config=config
)
elif llm_settings[the_model]["provider"] == "ollama":
msg = get_agent_executor().invoke(
{
"input": the_message,
"chat_history": llm_history,
}
)
the_last_messages = msg["messages"]
if dont_save_image and screenshot_path is not None:
currently_messages = get_chat_message_history().messages
last_message = currently_messages[-1].content[0]
currently_messages.remove(currently_messages[-1])
get_chat_message_history().clear()
for message in currently_messages:
get_chat_message_history().add_message(message)
get_chat_message_history().add_message(HumanMessage(content=[last_message]))
get_chat_message_history().add_message(the_last_messages[-1])
# Replace each_message_extension with empty string
list_of_messages = get_chat_message_history().messages
get_chat_message_history().clear()
for message in list_of_messages:
try:
message.content[0]["text"] = message.content[0]["text"].replace(each_message_extension, "")
get_chat_message_history().add_message(message)
except:
get_chat_message_history().add_message(message)
return the_last_messages[-1].content
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