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internlm2-math-7b
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
import streamlit as st
from openxlab.model import download
from modelscope import snapshot_download
import os

# level = os.getenv('level')
level = '7'

with st.sidebar:
    st.markdown('[InternLM Math GitHub Page](https://github.com/InternLM/InternLM-Math)')
    max_length = st.slider("max_length", 0, 1024, 512, step=1)
    # system_prompt = st.text_input("System_Prompt", "")

st.title("InternLM2-math-7B")
st.caption("🚀 Powered By Shanghai Ai Lab")

# 定义模型路径
## ModelScope
# model_id = 'Shanghai_AI_Laboratory/internlm2-chat-'+ str(level) +'b'
# mode_name_or_path = snapshot_download(model_id, revision='master')
mode_name_or_path = "internlm/internlm2-math-7b"
# OpenXLab
# model_repo = "OpenLMLab/internlm2-chat-7b"
# mode_name_or_path = download(model_repo=model_repo)


@st.cache_resource
def get_model():
    tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(mode_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
    model.eval()  
    return tokenizer, model

tokenizer, model = get_model()
if "messages" not in st.session_state:
    st.session_state["messages"] = []
for msg in st.session_state.messages:
    st.chat_message("user").write(msg[0])
    st.chat_message("assistant").write(msg[1])
if prompt := st.chat_input():
    st.chat_message("user").write(prompt)
    response, history = model.chat(tokenizer, prompt, meta_instruction='', history=st.session_state.messages)
    st.session_state.messages.append((prompt, response))
    st.chat_message("assistant").write(response)

# import os
# os.system("pip uninstall -y gradio")
# os.system("pip install gradio==3.43.0")
# from lmdeploy.serve.gradio.turbomind_coupled import *
# from lmdeploy.messages import TurbomindEngineConfig
# from lmdeploy import ChatTemplateConfig

# chat_template = ChatTemplateConfig(model_name='internlm2-chat-7b', system='', eosys='', meta_instruction='')
# backend_config = TurbomindEngineConfig(model_name='internlm2-chat-7b', max_batch_size=1, cache_max_entry_count=0.05)#, model_format='awq')
# model_path = 'internlm/internlm2-math-7b'

# InterFace.async_engine = AsyncEngine(
#     model_path=model_path,
#     backend='turbomind',
#     backend_config=backend_config,
#     chat_template_config=chat_template,
#     tp=1)

# async def reset_local_func(instruction_txtbox: gr.Textbox,
#                            state_chatbot: Sequence, session_id: int):
#     """reset the session.

#     Args:
#         instruction_txtbox (str): user's prompt
#         state_chatbot (Sequence): the chatting history
#         session_id (int): the session id
#     """
#     state_chatbot = []
#     # end the session
#     with InterFace.lock:
#         InterFace.global_session_id += 1
#         session_id = InterFace.global_session_id
#     return (state_chatbot, state_chatbot, gr.Textbox.update(value=''), session_id)

# async def cancel_local_func(state_chatbot: Sequence, cancel_btn: gr.Button,
#                             reset_btn: gr.Button, session_id: int):
#     """stop the session.

#     Args:
#         instruction_txtbox (str): user's prompt
#         state_chatbot (Sequence): the chatting history
#         cancel_btn (gr.Button): the cancel button
#         reset_btn (gr.Button): the reset button
#         session_id (int): the session id
#     """
#     yield (state_chatbot, disable_btn, disable_btn, session_id)
#     InterFace.async_engine.stop_session(session_id)
#     # pytorch backend does not support resume chat history now
#     if InterFace.async_engine.backend == 'pytorch':
#         yield (state_chatbot, disable_btn, enable_btn, session_id)
#     else:
#         with InterFace.lock:
#             InterFace.global_session_id += 1
#             session_id = InterFace.global_session_id
#         messages = []
#         for qa in state_chatbot:
#             messages.append(dict(role='user', content=qa[0]))
#             if qa[1] is not None:
#                 messages.append(dict(role='assistant', content=qa[1]))
#         gen_config = GenerationConfig(max_new_tokens=0)
#         async for out in InterFace.async_engine.generate(messages,
#                                                          session_id,
#                                                          gen_config=gen_config,
#                                                          stream_response=True,
#                                                          sequence_start=True,
#                                                          sequence_end=False):
#             pass
#         yield (state_chatbot, disable_btn, enable_btn, session_id)

# with gr.Blocks(css=CSS, theme=THEME) as demo:
#     state_chatbot = gr.State([])
#     state_session_id = gr.State(0)

#     with gr.Column(elem_id='container'):
#         gr.Markdown('## LMDeploy Playground')
#         gr.Markdown('[InternLM Math GitHub Page](https://github.com/InternLM/InternLM-Math)')

#         chatbot = gr.Chatbot(
#             elem_id='chatbot',
#             label=InterFace.async_engine.engine.model_name)
#         instruction_txtbox = gr.Textbox(
#             placeholder='Please input the instruction',
#             label='Instruction')
#         with gr.Row():
#             cancel_btn = gr.Button(value='Cancel', interactive=False)
#             reset_btn = gr.Button(value='Reset')
#         with gr.Row():
#             request_output_len = gr.Slider(1,
#                                             1024,
#                                             value=512,
#                                             step=1,
#                                             label='Maximum new tokens')
#             top_p = gr.Slider(0.01, 1, value=1.0, step=0.01, label='Top_p')
#             temperature = gr.Slider(0.01,
#                                     1.5,
#                                     value=0.01,
#                                     step=0.01,
#                                     label='Temperature')

#     send_event = instruction_txtbox.submit(chat_stream_local, [
#         instruction_txtbox, state_chatbot, cancel_btn, reset_btn,
#         state_session_id, top_p, temperature, request_output_len
#     ], [state_chatbot, chatbot, cancel_btn, reset_btn])
#     instruction_txtbox.submit(
#         lambda: gr.Textbox.update(value=''),
#         [],
#         [instruction_txtbox],
#     )
#     cancel_btn.click(
#         cancel_local_func,
#         [state_chatbot, cancel_btn, reset_btn, state_session_id],
#         [state_chatbot, cancel_btn, reset_btn, state_session_id],
#         cancels=[send_event])

#     reset_btn.click(reset_local_func,
#                     [instruction_txtbox, state_chatbot, state_session_id],
#                     [state_chatbot, chatbot, instruction_txtbox, state_session_id],
#                     cancels=[send_event])

#     def init():
#         with InterFace.lock:
#             InterFace.global_session_id += 1
#         new_session_id = InterFace.global_session_id
#         return new_session_id

#     demo.load(init, inputs=None, outputs=[state_session_id])

# # demo.queue(concurrency_count=InterFace.async_engine.instance_num,
#             # max_size=100).launch()
# demo.queue(max_size=1000).launch(max_threads=InterFace.async_engine.instance_num)