Spaces:
Sleeping
Sleeping
File size: 7,309 Bytes
843a514 ae7be4f 843a514 91d8471 843a514 91d8471 843a514 91d8471 843a514 ae7be4f 843a514 91d8471 843a514 91d8471 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f 843a514 ae7be4f |
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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
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```'
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=".button-primary { background-color: #4CAF50; color: white; border-radius: 8px; border: none; padding: 10px 20px; font-size: 16px; cursor: pointer; transition: background-color 0.3s ease; } .button-primary:hover { background-color: #45a049; } .button-secondary { background-color: #f44336; color: white; border-radius: 8px; border: none; padding: 10px 20px; font-size: 16px; cursor: pointer; transition: background-color 0.3s ease; } .button-secondary:hover { background-color: #e53935; }") as demo:
gr.HTML("""<h1 align="center" style="font-family: 'Arial', sans-serif; color: #4A90E2;">MindSearch Gradio Demo</h1>""")
gr.HTML("""<p style="text-align: center; font-family: Arial, sans-serif; color: #333; max-width: 800px; margin: 0 auto;">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; font-weight: bold;">🔗 GitHub</a>
<a href="https://arxiv.org/abs/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2; font-weight: bold;">📄 Arxiv</a>
<a href="https://huggingface.co/papers/2407.20183" style="margin-right: 15px; text-decoration: none; color: #4A90E2; font-weight: bold;">📚 Hugging Face Papers</a>
<a href="https://huggingface.co/spaces/internlm/MindSearch" style="text-decoration: none; color: #4A90E2; font-weight: bold;">🤗 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,
css="border-radius: 8px; border: 1px solid #ddd; padding: 10px;")
with gr.Row():
with gr.Column(scale=2):
submitBtn = gr.Button('Submit', css="button-primary")
with gr.Column(scale=1, min_width=20):
emptyBtn = gr.Button('Clear History', css="button-secondary")
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)
|