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from llama_index.readers import TrafilaturaWebReader
from llama_index import VectorStoreIndex
from llama_index import ServiceContext
from langchain.llms import HuggingFaceHub
from llama_index.llms import LangChainLLM
import gradio as gr
repo_id = 'HuggingFaceH4/zephyr-7b-beta'
def loading_website(): return "Loading..."
def load_url(url):
documents = TrafilaturaWebReader().load_data([url])
llm = LangChainLLM(llm=HuggingFaceHub(repo_id=repo_id, model_kwargs={'temperature': 0.2, 'max_tokens': 4096, 'top_p': 0.95}))
service_context = ServiceContext.from_defaults(llm=llm, embed_model="local:BAAI/bge-small-en-v1.5")
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
global query_engine
query_engine = index.as_query_engine()
return 'Ready'
# def chat(query):
# response = query_engine.query(query)
# return str(response)
def add_text(history, text):
history = history + [(text, None)]
return history, ''
def bot(history):
response = infer(history[-1][0])
history[-1][1] = response
return history
def infer(question):
response = query_engine.query(question)
return str(response)
with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
with gr.Column():
chatbot = gr.Chatbot([], elem_id='chatbot')
with gr.Row():
web_address = gr.Textbox(label='Web Address', placeholder='http://karpathy.github.io/2019/04/25/recipe/')
website_status = gr.Textbox(label='Status', placeholder='', interactive=False)
load_website = gr.Button('Load Website')
with gr.Row():
question = gr.Textbox(label='Question', placeholder='Type your query...')
submit_btn = gr.Button('Submit')
load_website.click(load_website, inputs=[web_address], outputs=[website_status], queue=False)
question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
demo.launch(share=True) |