app.py

# import json
# import os

# import gradio as gr
# import requests
# from lagent.schema import AgentStatusCode

# os.system("python -m mindsearch.app --lang cn --model_format internlm_silicon &")

# PLANNER_HISTORY = []
# SEARCHER_HISTORY = []


# def rst_mem(history_planner: list, history_searcher: list):
#     '''
#     Reset the chatbot memory.
#     '''
#     history_planner = []
#     history_searcher = []
#     if PLANNER_HISTORY:
#         PLANNER_HISTORY.clear()
#     return history_planner, history_searcher


# def format_response(gr_history, agent_return):
#     if agent_return['state'] in [
#             AgentStatusCode.STREAM_ING, AgentStatusCode.ANSWER_ING
#     ]:
#         gr_history[-1][1] = agent_return['response']
#     elif agent_return['state'] == AgentStatusCode.PLUGIN_START:
#         thought = gr_history[-1][1].split('```')[0]
#         if agent_return['response'].startswith('```'):
#             gr_history[-1][1] = thought + '\n' + agent_return['response']
#     elif agent_return['state'] == AgentStatusCode.PLUGIN_END:
#         thought = gr_history[-1][1].split('```')[0]
#         if isinstance(agent_return['response'], dict):
#             gr_history[-1][
#                 1] = thought + '\n' + f'```json\n{json.dumps(agent_return["response"], ensure_ascii=False, indent=4)}\n```'  # noqa: E501
#     elif agent_return['state'] == AgentStatusCode.PLUGIN_RETURN:
#         assert agent_return['inner_steps'][-1]['role'] == 'environment'
#         item = agent_return['inner_steps'][-1]
#         gr_history.append([
#             None,
#             f"```json\n{json.dumps(item['content'], ensure_ascii=False, indent=4)}\n```"
#         ])
#         gr_history.append([None, ''])
#     return


# def predict(history_planner, history_searcher):

#     def streaming(raw_response):
#         for chunk in raw_response.iter_lines(chunk_size=8192,
#                                              decode_unicode=False,
#                                              delimiter=b'\n'):
#             if chunk:
#                 decoded = chunk.decode('utf-8')
#                 if decoded == '\r':
#                     continue
#                 if decoded[:6] == 'data: ':
#                     decoded = decoded[6:]
#                 elif decoded.startswith(': ping - '):
#                     continue
#                 response = json.loads(decoded)
#                 yield (response['response'], response['current_node'])

#     global PLANNER_HISTORY
#     PLANNER_HISTORY.append(dict(role='user', content=history_planner[-1][0]))
#     new_search_turn = True

#     url = 'http://localhost:8002/solve'
#     headers = {'Content-Type': 'application/json'}
#     data = {'inputs': PLANNER_HISTORY}
#     raw_response = requests.post(url,
#                                  headers=headers,
#                                  data=json.dumps(data),
#                                  timeout=20,
#                                  stream=True)

#     for resp in streaming(raw_response):
#         agent_return, node_name = resp
#         if node_name:
#             if node_name in ['root', 'response']:
#                 continue
#             agent_return = agent_return['nodes'][node_name]['detail']
#             if new_search_turn:
#                 history_searcher.append([agent_return['content'], ''])
#                 new_search_turn = False
#             format_response(history_searcher, agent_return)
#             if agent_return['state'] == AgentStatusCode.END:
#                 new_search_turn = True
#             yield history_planner, history_searcher
#         else:
#             new_search_turn = True
#             format_response(history_planner, agent_return)
#             if agent_return['state'] == AgentStatusCode.END:
#                 PLANNER_HISTORY = agent_return['inner_steps']
#             yield history_planner, history_searcher
#     return history_planner, history_searcher


# with gr.Blocks() as demo:
#     gr.HTML("""<h1 align="center">MindSearch Gradio Demo</h1>""")
#     gr.HTML("""<p style="text-align: center; font-family: Arial, sans-serif;">MindSearch is an open-source AI Search Engine Framework with Perplexity.ai Pro performance. You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).</p>""")
#     gr.HTML("""
#     <div style="text-align: center; font-size: 16px;">
#         <a href="https://github.com/InternLM/MindSearch" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">🔗 GitHub</a>
#         <a href="https://arxiv.org/abs/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📄 Arxiv</a>
#         <a href="https://huggingface.co/papers/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📚 Hugging Face Papers</a>
#         <a href="https://huggingface.co/spaces/internlm/MindSearch" style="text-decoration: none; color: #4A90E2;">🤗 Hugging Face Demo</a>
#     </div>
#     """)
#     with gr.Row():
#         with gr.Column(scale=10):
#             with gr.Row():
#                 with gr.Column():
#                     planner = gr.Chatbot(label='planner',
#                                          height=700,
#                                          show_label=True,
#                                          show_copy_button=True,
#                                          bubble_full_width=False,
#                                          render_markdown=True)
#                 with gr.Column():
#                     searcher = gr.Chatbot(label='searcher',
#                                           height=700,
#                                           show_label=True,
#                                           show_copy_button=True,
#                                           bubble_full_width=False,
#                                           render_markdown=True)
#             with gr.Row():
#                 user_input = gr.Textbox(show_label=False,
#                                         placeholder='帮我搜索一下 InternLM 开源体系',
#                                         lines=5,
#                                         container=False)
#             with gr.Row():
#                 with gr.Column(scale=2):
#                     submitBtn = gr.Button('Submit')
#                 with gr.Column(scale=1, min_width=20):
#                     emptyBtn = gr.Button('Clear History')

#     def user(query, history):
#         return '', history + [[query, '']]

#     submitBtn.click(user, [user_input, planner], [user_input, planner],
#                     queue=False).then(predict, [planner, searcher],
#                                       [planner, searcher])
#     emptyBtn.click(rst_mem, [planner, searcher], [planner, searcher],
#                    queue=False)

# demo.queue()
# demo.launch(server_name='0.0.0.0',
#             server_port=7860,
#             inbrowser=True,
#             share=True)


import json
import os

import gradio as gr
import requests
from lagent.schema import AgentStatusCode

os.system("python -m mindsearch.app --lang cn --model_format internlm_silicon &")

PLANNER_HISTORY = []
SEARCHER_HISTORY = []


def rst_mem(history_planner: list, history_searcher: list):
    '''
    Reset the chatbot memory.
    '''
    history_planner = []
    history_searcher = []
    if PLANNER_HISTORY:
        PLANNER_HISTORY.clear()
    return history_planner, history_searcher


def format_response(gr_history, agent_return):
    if agent_return['state'] in [
            AgentStatusCode.STREAM_ING, AgentStatusCode.ANSWER_ING
    ]:
        gr_history[-1][1] = agent_return['response']
    elif agent_return['state'] == AgentStatusCode.PLUGIN_START:
        thought = gr_history[-1][1].split('```')[0]
        if agent_return['response'].startswith('```'):
            gr_history[-1][1] = thought + '\n' + agent_return['response']
    elif agent_return['state'] == AgentStatusCode.PLUGIN_END:
        thought = gr_history[-1][1].split('```')[0]
        if isinstance(agent_return['response'], dict):
            gr_history[-1][
                1] = thought + '\n' + f'```json\n{json.dumps(agent_return["response"], ensure_ascii=False, indent=4)}\n```'  # noqa: E501
    elif agent_return['state'] == AgentStatusCode.PLUGIN_RETURN:
        assert agent_return['inner_steps'][-1]['role'] == 'environment'
        item = agent_return['inner_steps'][-1]
        gr_history.append([
            None,
            f"```json\n{json.dumps(item['content'], ensure_ascii=False, indent=4)}\n```"
        ])
        gr_history.append([None, ''])
    return


def predict(history_planner, history_searcher):

    def streaming(raw_response):
        for chunk in raw_response.iter_lines(chunk_size=8192,
                                             decode_unicode=False,
                                             delimiter=b'\n'):
            if chunk:
                decoded = chunk.decode('utf-8')
                if decoded == '\r':
                    continue
                if decoded[:6] == 'data: ':
                    decoded = decoded[6:]
                elif decoded.startswith(': ping - '):
                    continue
                response = json.loads(decoded)
                yield (response['response'], response['current_node'])

    global PLANNER_HISTORY
    PLANNER_HISTORY.append(dict(role='user', content=history_planner[-1][0]))
    new_search_turn = True

    url = 'http://localhost:8002/solve'
    headers = {'Content-Type': 'application/json'}
    data = {'inputs': PLANNER_HISTORY}
    raw_response = requests.post(url,
                                 headers=headers,
                                 data=json.dumps(data),
                                 timeout=20,
                                 stream=True)

    for resp in streaming(raw_response):
        agent_return, node_name = resp
        if node_name:
            if node_name in ['root', 'response']:
                continue
            agent_return = agent_return['nodes'][node_name]['detail']
            if new_search_turn:
                history_searcher.append([agent_return['content'], ''])
                new_search_turn = False
            format_response(history_searcher, agent_return)
            if agent_return['state'] == AgentStatusCode.END:
                new_search_turn = True
            yield history_planner, history_searcher
        else:
            new_search_turn = True
            format_response(history_planner, agent_return)
            if agent_return['state'] == AgentStatusCode.END:
                PLANNER_HISTORY = agent_return['inner_steps']
            yield history_planner, history_searcher
    return history_planner, history_searcher


with gr.Blocks(css="""
    .gr-button {
        background-color: #4A90E2; 
        color: white; 
        border-radius: 10px;
        padding: 10px 20px;
        font-size: 16px;
        border: none;
    }
    .gr-button:hover {
        background-color: #357ABD;
    }
    .gr-textbox {
        border: 2px solid #4A90E2; 
        border-radius: 5px;
        padding: 10px;
        font-size: 14px;
    }
    .gr-row {
        margin-bottom: 10px;
    }
    .gr-chatbot {
        border: 2px solid #4A90E2; 
        border-radius: 10px;
        padding: 10px;
    }
""") as demo:
    gr.HTML("""<h1 align="center" style="color:#4A90E2;">MindSearch Gradio Demo</h1>""")
    gr.HTML("""<p style="text-align: center; font-family: Arial, sans-serif; color: #333;">MindSearch is an open-source AI Search Engine Framework with Perplexity.ai Pro performance. You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).</p>""")
    gr.HTML("""
    <div style="text-align: center; font-size: 16px; margin-bottom: 20px;">
        <a href="https://github.com/InternLM/MindSearch" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">🔗 GitHub</a>
        <a href="https://arxiv.org/abs/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📄 Arxiv</a>
        <a href="https://huggingface.co/papers/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2;">📚 Hugging Face Papers</a>
        <a href="https://huggingface.co/spaces/internlm/MindSearch" style="text-decoration: none; color: #4A90E2;">🤗 Hugging Face Demo</a>
    </div>
    """)
    with gr.Row():
        with gr.Column(scale=10):
            with gr.Row():
                with gr.Column():
                    planner = gr.Chatbot(label='Planner',
                                         height=700,
                                         show_label=True,
                                         show_copy_button=True,
                                         bubble_full_width=True,
                                         render_markdown=True,
                                         elem_id="planner")
                with gr.Column():
                    searcher = gr.Chatbot(label='Searcher',
                                          height=700,
                                          show_label=True,
                                          show_copy_button=True,
                                          bubble_full_width=True,
                                          render_markdown=True,
                                          elem_id="searcher")
            with gr.Row():
                user_input = gr.Textbox(show_label=False,
                                        placeholder='帮我搜索一下 InternLM 开源体系',
                                        lines=5,
                                        container=False,
                                        elem_id="user_input")
            with gr.Row():
                with gr.Column(scale=2):
                    submitBtn = gr.Button('Submit', elem_id="submitBtn")
                with gr.Column(scale=1, min_width=20):
                    emptyBtn = gr.Button('Clear History', elem_id="emptyBtn")

    def user(query, history):
        return '', history + [[query, '']]

    submitBtn.click(user, [user_input, planner], [user_input, planner],
                    queue=False).then(predict, [planner, searcher],
                                      [planner, searcher])
    emptyBtn.click(rst_mem, [planner, searcher], [planner, searcher],
                   queue=False)

demo.queue()
demo.launch(server_name='0.0.0.0',
            server_port=7860,
            inbrowser=True,
            share=True)