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import gradio as gr
from langchain.agents import initialize_agent
# from langchain.llms import OpenAI
# from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from PIL import Image
from demotool import *
from loader import *
# from llmLoader import *
import re
from gradio_tools.tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,
TextToVideoTool)
from langchain.memory import ConversationBufferMemory
# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain import PromptTemplate, HuggingFaceHub, LLMChain
def init_model_config():
llm = ChatLLM()
llm.model_type = 'chatglm'
llm.model_name_or_path = llm_model_dict['chatglm'][
'ChatGLM-6B-int4']
llm.load_llm()
return llm
# initialize HF LLM
# flan_t5 = HuggingFaceHub(
# repo_id="google/flan-t5-xl",
# model_kwargs={"temperature":1e-10},
# huggingfacehub_api_token="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"
# )
# llm = ChatOpenAI(openai_api_key="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",temperature=0,streaming=True,callbacks=[StreamingStdOutCallbackHandler()])
# llm = ModelLoader()
# llm.loader()
# chatLLM = ModelLoader()
# chatLLM.loader()
memory = ConversationBufferMemory(memory_key="chat_history")
# tools = [ Text2Image()]
# tools = [ Tool.from_function(
# func=search,
# name = "Search",
# description="useful for when you need to answer questions about current events"
# )]
# tools = [ Tool.from_function(
# func=optimizationProblem,
# name = "optimizationProblem",
# description=" you must use this tool when you need to Add more information"
# )]
# tools = [ StableDiffusionPromptGeneratorTool().langchain]
tools = []
agent = initialize_agent(tools, init_model_config(), memory=memory, agent="conversational-react-description", verbose=True)
def run_text(text, state):
# print("stat:"+text)
# res = llm_chain.run(text)
# print("res:"+res)
res = agent.run(input=(text))
response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res)
state = state + [(text, response)]
return state,state
with gr.Blocks(css="#chatbot {overflow:auto; height:500px;}") as demo:
chatbot = gr.Chatbot(elem_id="chatbot",show_label=False)
state = gr.State([])
with gr.Row() as input_raws:
with gr.Column(scale=0.6):
txt = gr.Textbox(show_label=False).style(container=False)
with gr.Column(scale=0.20, min_width=0):
run = gr.Button("🏃♂️Run")
with gr.Column(scale=0.20, min_width=0):
clear = gr.Button("🔄Clear️")
txt.submit(run_text, [txt, state], [chatbot,state])
txt.submit(lambda: "", None, txt)
run.click(run_text, [txt, state], [chatbot,state])
demo.queue(concurrency_count=10).launch(server_name="0.0.0.0", server_port=7865) |