patti-j commited on
Commit
4b55d71
1 Parent(s): 233b22e

Update app.py

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Files changed (1) hide show
  1. app.py +39 -18
app.py CHANGED
@@ -3,29 +3,50 @@ import gradio as gr
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  # from llama_index import GPTVectorStoreIndex
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  # from query_data import get_chain
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  from langchain.chat_models import ChatOpenAI
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- from langchain import LLMChain
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- from langchain.chat_models import ChatOpenAI
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- from langchain.prompts.chat import (
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- ChatPromptTemplate,
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- SystemMessagePromptTemplate,
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- HumanMessagePromptTemplate,
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- )
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- from langchain.agents import initialize_agent, AgentType
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- # initialize OpenAI chatbot agent
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- llm = ChatOpenAI(temperature=0)
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- agent = initialize_agent(llm, AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION)
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- # define function to get chatbot response
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- def get_response(text):
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- response = agent.run(text)
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  return response
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- # create Gradio interface
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- iface = gr.Interface(fn=get_response, inputs="text", outputs="text")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # launch interface
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- iface.launch()
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  """chat = ChatOpenAI(temperature=0)
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  # from llama_index import GPTVectorStoreIndex
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  # from query_data import get_chain
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  from langchain.chat_models import ChatOpenAI
 
 
 
 
 
 
 
 
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+ # create the OpenAI chatbot
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+ chatbot = ChatOpenAI()
 
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+ # define the function to generate the chatbot response
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+ def generate_response(text):
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+ response = chatbot.generate_response(text)
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  return response
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+ # create the Gradio interface
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+ interface = gr.Interface(
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+ fn=generate_response,
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+ inputs=gr.inputs.Textbox(label="Input Text"),
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+ outputs=gr.outputs.Textbox(label="Output Text")
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+ )
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+
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+ # launch the interface
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+ interface.launch()
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+
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+ #from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
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+ #from langchain.memory import ConversationBufferWindowMemory
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+
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+ #template =
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+ """You are a brilliant and empathic counselor. You encourage human to share feelings.
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+ You provide resources when appropriate or if asked.
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+ {history}
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+ Human: {human_input}
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+ Assistant:"""
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+
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+ """prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
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+
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+ chatgpt_chain = LLMChain(
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+ llm=OpenAI(temperature=0.8),
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+ prompt=prompt,
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+ verbose=False,
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+ memory=ConversationBufferWindowMemory(k=2),
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+ )
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+
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+ output = chatgpt_chain.predict(
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+ human_input=
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+
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+ iface = gr.Interface(fn=get_response, inputs="text", outputs="text")"""
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+
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  """chat = ChatOpenAI(temperature=0)
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