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c4555db a4b071c 0a24f02 a4b071c c4555db 0bc7b50 c4555db 0a24f02 c4555db 0a24f02 c4555db 0a24f02 c4555db 7beeafa c4555db 0a24f02 c4555db a4b071c c4555db a4b071c c4555db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import time
import gradio as gr
from gpt_index import RefinePrompt
from gpt_index import (
SimpleWebPageReader,
WikipediaReader,
GPTListIndex,
GPTSimpleVectorIndex,
LLMPredictor,
QuestionAnswerPrompt,
RefinePrompt,
PromptHelper
)
system_message = {"role": "system", "content": "You are an AI specialized in Atlanta."}
with gr.Blocks() as demo:
gr.Markdown(
'''
# Customized Atlanta Chatbot Demo
This chatbot uses the Atlantaga.gov and ATL311.com websites as its custom knowledge base.
Before starting a new conversation, please refresh the chatbot for the best results.
If the chatbot is giving incorrect answers, please refresh.
'''
)
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
state = gr.State([])
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history, messages_history):
user_message = history[-1][0]
bot_message, messages_history = ask_gpt(user_message, messages_history)
messages_history += [{"role": "assistant", "content": bot_message}]
history[-1][1] = bot_message
time.sleep(1)
return history, messages_history
def ask_gpt(message, messages_history):
messages_history += [{"role": "user", "content": message}]
query_str = ''
QA_PROMPT_TMPL = (
"You are an conversational AI specialized in Atlanta.\n"
"If a query does not relate to Atlanta, say you can't answer the query.\n"# and make the answer related to Atlanta.\n"
"We have provided context information below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Given this information, please give a detailed and conversational answer to the query: {query_str} and cite the url source associated with this answer.\n"
"Use information from previous queries in your response when appropriate.\n"
"Format the answer to the query like this: Answer: .\n"
"\nSource: followed by the source in bold.\n"
"Put the Answer and Source on different lines of the response and the Source is the url source associated with the answer.\n"
)
QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)
# Takes in the input from the user to deliver responses
index = GPTSimpleVectorIndex.load_from_disk('index_demo.json')
message = ' '.join([message['content'] for message in messages_history])
response = index.query(message, text_qa_template = QA_PROMPT)
return response.response, messages_history
#return response['choices'][0]['message']['content'], messages_history
def init_history(messages_history):
messages_history = []
messages_history += [system_message]
return messages_history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot, state], [chatbot, state]
)
clear.click(lambda: None, None, chatbot, queue=False).success(init_history, [state], [state])
demo.launch() |