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import langchain as lc
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain import PromptTemplate, LLMChain, HuggingFaceHub, OpenAI, FewShotPromptTemplate
from langchain.prompts.example_selector import LengthBasedExampleSelector
from langchain.chains import ConversationChain, MapReduceChain
from langchain.memory import ConversationBufferMemory, ConversationSummaryBufferMemory
from langchain.callbacks import get_openai_callback
import os
from langchain.chains import LLMMathChain, SQLDatabaseChain
from langchain.agents import Tool, load_tools, initialize_agent, AgentType
from langchain.agents.react.base import DocstoreExplorer
import gradio as gr
def demo8():
model = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY'])
tools = load_tools(['llm-math', 'terminal'], llm=model)
prompt = PromptTemplate(template="{question}", input_variables=['question'])
llm_chain = LLMChain(llm=model, prompt=prompt)
llm_tool = Tool(name="Search", func=llm_chain.run, description="general QA")
tools.append(llm_tool)
memory = ConversationBufferMemory(memory_key="chat_history")
conversation_agent = initialize_agent(tools=tools,
llm=model,
max_iterations=3,
verbose=True,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=memory)
# resp = conversation_agent("what is (4.5*2.1)^2.2?")
with gr.Blocks() as demo:
state = gr.State([])
chatbot = gr.Chatbot(elem_id="chatbot")
with gr.Row():
text = gr.Textbox(show_label=False, placeholder="enter your prompt")
def answer(question, history=[]):
history.append(question)
resp = conversation_agent(question)
print(f"resp: {resp}")
history.append(resp['output'])
dial = [(u, v) for u, v in zip(history[::2], history[1::2])]
return {
chatbot: dial,
state: history
}
text.submit(answer, inputs=[text, state], outputs=[chatbot, state])
demo.launch()
if __name__ == "__main__":
demo8()
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