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import gradio as gr
import os
import time
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import LlamaCpp
from langchain_experimental.chat_models import Llama2Chat
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain.schema import SystemMessage
import urllib
# urllib.request.urlretrieve(
# "https://huggingface.co/hfl/chinese-alpaca-2-7b-rlhf-gguf/resolve/main/ggml-model-q6_k.gguf?download=true",
# "ggml-model-q6_k.gguf"
# )
# template_messages = [
# SystemMessage(content="你是一名软件工程师,你的名字叫做贺英旭。请你以这个身份回答以下问题!"),
# MessagesPlaceholder(variable_name="chat_history"),
# HumanMessagePromptTemplate.from_template("{text}"),
# ]
# prompt_template = ChatPromptTemplate.from_messages(template_messages)
# llm = LlamaCpp(
# model_path="ggml-model-q6_k.gguf",
# temperature=0.75,
# max_tokens=64
# )
# model = Llama2Chat(llm=llm)
# memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
print(history)
response = infer(history[-1][0])
history[-1][1] = ""
for character in response:
history[-1][1] += character
time.sleep(0.05)
print(history)
yield history
def infer(question):
# result = chain.run(text=question).strip()
result = "123123123123"
print(result)
return result
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with Yingxu</h1>
<p style="text-align: center;">Free feel to talk about anything :)</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=300)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send Message")
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()
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